A Markovian event-based framework for stochastic spiking neural networks.

PubMed

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

The application of neural networks to the SSME startup transient

NASA Technical Reports Server (NTRS)

Meyer, Claudia M.; Maul, William A.

1991-01-01

Feedforward neural networks were used to model three parameters during the Space Shuttle Main Engine startup transient. The three parameters were the main combustion chamber pressure, a controlled parameter, the high pressure oxidizer turbine discharge temperature, a redlined parameter, and the high pressure fuel pump discharge pressure, a failure-indicating performance parameter. Network inputs consisted of time windows of data from engine measurements that correlated highly to the modeled parameter. A standard backpropagation algorithm was used to train the feedforward networks on two nominal firings. Each trained network was validated with four additional nominal firings. For all three parameters, the neural networks were able to accurately predict the data in the validation sets as well as the training set.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

PubMed

Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

2015-11-01

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hui; Song, Yongduan; Xue, Fangzheng

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than themore » SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.« less

The effects of dynamical synapses on firing rate activity: a spiking neural network model.

PubMed

Khalil, Radwa; Moftah, Marie Z; Moustafa, Ahmed A

2017-11-01

Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABA A signaling and a lateral spread length between neighboring neurons (i.e., local connectivity). Furthermore, a number of studies consider short-term synaptic plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson input frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF) and these key parameters in vitro remains elusive. Therefore, we designed a spiking neural network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) short-term synaptic depression (STD) and (2) short-term synaptic facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses (STP) have a critical differential role on evaluating and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) and inhibition (I) on stabilizing this firing activity. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Propagating waves can explain irregular neural dynamics.

PubMed

Keane, Adam; Gong, Pulin

2015-01-28

Cortical neurons in vivo fire quite irregularly. Previous studies about the origin of such irregular neural dynamics have given rise to two major models: a balanced excitation and inhibition model, and a model of highly synchronized synaptic inputs. To elucidate the network mechanisms underlying synchronized synaptic inputs and account for irregular neural dynamics, we investigate a spatially extended, conductance-based spiking neural network model. We show that propagating wave patterns with complex dynamics emerge from the network model. These waves sweep past neurons, to which they provide highly synchronized synaptic inputs. On the other hand, these patterns only emerge from the network with balanced excitation and inhibition; our model therefore reconciles the two major models of irregular neural dynamics. We further demonstrate that the collective dynamics of propagating wave patterns provides a mechanistic explanation for a range of irregular neural dynamics, including the variability of spike timing, slow firing rate fluctuations, and correlated membrane potential fluctuations. In addition, in our model, the distributions of synaptic conductance and membrane potential are non-Gaussian, consistent with recent experimental data obtained using whole-cell recordings. Our work therefore relates the propagating waves that have been widely observed in the brain to irregular neural dynamics. These results demonstrate that neural firing activity, although appearing highly disordered at the single-neuron level, can form dynamical coherent structures, such as propagating waves at the population level. Copyright © 2015 the authors 0270-6474/15/351591-15$15.00/0.

Detection of Coal Fires: A Case Study Conducted on Indian Coal Seams Using Neural Network and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Singh, B. B.

2016-12-01

India produces majority of its electricity from coal but a huge quantity of coal burns every day due to coal fires and also poses a threat to the environment as severe pollutants. In the present study we had demonstrated the usage of Neural Network based approach with an integrated Particle Swarm Optimization (PSO) inversion technique. The Self Potential (SP) data set is used for the early detection of coal fires. The study was conducted over the East Basuria colliery, Jharia Coal Field, Jharkhand, India. The causative source was modelled as an inclined sheet like anomaly and the synthetic data was generated. Neural Network scheme consists of an input layer, hidden layers and an output layer. The input layer corresponds to the SP data and the output layer is the estimated depth of the coal fire. A synthetic dataset was modelled with some of the known parameters such as depth, conductivity, inclination angle, half width etc. associated with causative body and gives a very low misfit error of 0.0032%. Therefore, the method was found accurate in predicting the depth of the source body. The technique was applied to the real data set and the model was trained until a very good correlation of determination `R2' value of 0.98 is obtained. The depth of the source body was found to be 12.34m with a misfit error percentage of 0.242%. The inversion results were compared with the lithologs obtained from a nearby well which corresponds to the L3 coal seam. The depth of the coal fire had exactly matched with the half width of the anomaly which suggests that the fire is widely spread. The inclination angle of the anomaly was 135.510 which resembles the development of the geometrically complex fracture planes. These fractures may be developed due to anisotropic weakness of the ground which acts as passage for the air. As a result coal fires spreads along these fracture planes. The results obtained from the Neural Network was compared with PSO inversion results and were found in complete agreement. PSO technique had already been found a well-established technique to model SP anomalies. Therefore for successful control and mitigation, SP surveys coupled with Neural Network and PSO technique proves to be novel and economical approach along with other existing geophysical techniques. Keywords: PSO, Coal fire, Self-Potential, Inversion, Neural Network

Pattern Storage, Bifurcations, and Groupwise Correlation Structure of an Exactly Solvable Asymmetric Neural Network Model.

PubMed

Fasoli, Diego; Cattani, Anna; Panzeri, Stefano

2018-05-01

Despite their biological plausibility, neural network models with asymmetric weights are rarely solved analytically, and closed-form solutions are available only in some limiting cases or in some mean-field approximations. We found exact analytical solutions of an asymmetric spin model of neural networks with arbitrary size without resorting to any approximation, and we comprehensively studied its dynamical and statistical properties. The network had discrete time evolution equations and binary firing rates, and it could be driven by noise with any distribution. We found analytical expressions of the conditional and stationary joint probability distributions of the membrane potentials and the firing rates. By manipulating the conditional probability distribution of the firing rates, we extend to stochastic networks the associating learning rule previously introduced by Personnaz and coworkers. The new learning rule allowed the safe storage, under the presence of noise, of point and cyclic attractors, with useful implications for content-addressable memories. Furthermore, we studied the bifurcation structure of the network dynamics in the zero-noise limit. We analytically derived examples of the codimension 1 and codimension 2 bifurcation diagrams of the network, which describe how the neuronal dynamics changes with the external stimuli. This showed that the network may undergo transitions among multistable regimes, oscillatory behavior elicited by asymmetric synaptic connections, and various forms of spontaneous symmetry breaking. We also calculated analytically groupwise correlations of neural activity in the network in the stationary regime. This revealed neuronal regimes where, statistically, the membrane potentials and the firing rates are either synchronous or asynchronous. Our results are valid for networks with any number of neurons, although our equations can be realistically solved only for small networks. For completeness, we also derived the network equations in the thermodynamic limit of infinite network size and we analytically studied their local bifurcations. All the analytical results were extensively validated by numerical simulations.

Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.

PubMed

Xu, Kesheng; Maidana, Jean Paul; Castro, Samy; Orio, Patricio

2018-05-30

Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that - when isolated - can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.

Death and rebirth of neural activity in sparse inhibitory networks

NASA Astrophysics Data System (ADS)

Angulo-Garcia, David; Luccioli, Stefano; Olmi, Simona; Torcini, Alessandro

2017-05-01

Inhibition is a key aspect of neural dynamics playing a fundamental role for the emergence of neural rhythms and the implementation of various information coding strategies. Inhibitory populations are present in several brain structures, and the comprehension of their dynamics is strategical for the understanding of neural processing. In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of neural activity, as expected, but can also promote neural re-activation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neuronal death). However, the random pruning of connections is able to reverse the action of inhibition, i.e. in a random sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of neurons (neuronal rebirth). Thus, the number of firing neurons reaches a minimum value at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by neurons with a higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving a mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, and the system passes from a perfectly regular evolution to irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum.

Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology.

PubMed

Karbasi, Amin; Salavati, Amir Hesam; Vetterli, Martin

2018-04-01

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network's topology has received a lot of attentions in neuroscience and has been the center of many research initiatives such as Human Connectome Project. Nevertheless, direct and invasive approaches that slice and observe the neural tissue have proven to be time consuming, complex and costly. As a result, the inverse methods that utilize firing activity of neurons in order to identify the (functional) connections have gained momentum recently, especially in light of rapid advances in recording technologies; It will soon be possible to simultaneously monitor the activities of tens of thousands of neurons in real time. While there are a number of excellent approaches that aim to identify the functional connections from firing activities, the scalability of the proposed techniques plays a major challenge in applying them on large-scale datasets of recorded firing activities. In exceptional cases where scalability has not been an issue, the theoretical performance guarantees are usually limited to a specific family of neurons or the type of firing activities. In this paper, we formulate the neural network reconstruction as an instance of a graph learning problem, where we observe the behavior of nodes/neurons (i.e., firing activities) and aim to find the links/connections. We develop a scalable learning mechanism and derive the conditions under which the estimated graph for a network of Leaky Integrate and Fire (LIf) neurons matches the true underlying synaptic connections. We then validate the performance of the algorithm using artificially generated data (for benchmarking) and real data recorded from multiple hippocampal areas in rats.

Localized states in an unbounded neural field equation with smooth firing rate function: a multi-parameter analysis.

PubMed

Faye, Grégory; Rankin, James; Chossat, Pascal

2013-05-01

The existence of spatially localized solutions in neural networks is an important topic in neuroscience as these solutions are considered to characterize working (short-term) memory. We work with an unbounded neural network represented by the neural field equation with smooth firing rate function and a wizard hat spatial connectivity. Noting that stationary solutions of our neural field equation are equivalent to homoclinic orbits in a related fourth order ordinary differential equation, we apply normal form theory for a reversible Hopf bifurcation to prove the existence of localized solutions; further, we present results concerning their stability. Numerical continuation is used to compute branches of localized solution that exhibit snaking-type behaviour. We describe in terms of three parameters the exact regions for which localized solutions persist.

Self-organization of synchronous activity propagation in neuronal networks driven by local excitation

PubMed Central

Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen

2015-01-01

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence. PMID:26089794

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks

PubMed Central

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model. PMID:24634645

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

PubMed

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

Network evolution induced by asynchronous stimuli through spike-timing-dependent plasticity.

PubMed

Yuan, Wu-Jie; Zhou, Jian-Fang; Zhou, Changsong

2013-01-01

In sensory neural system, external asynchronous stimuli play an important role in perceptual learning, associative memory and map development. However, the organization of structure and dynamics of neural networks induced by external asynchronous stimuli are not well understood. Spike-timing-dependent plasticity (STDP) is a typical synaptic plasticity that has been extensively found in the sensory systems and that has received much theoretical attention. This synaptic plasticity is highly sensitive to correlations between pre- and postsynaptic firings. Thus, STDP is expected to play an important role in response to external asynchronous stimuli, which can induce segregative pre- and postsynaptic firings. In this paper, we study the impact of external asynchronous stimuli on the organization of structure and dynamics of neural networks through STDP. We construct a two-dimensional spatial neural network model with local connectivity and sparseness, and use external currents to stimulate alternately on different spatial layers. The adopted external currents imposed alternately on spatial layers can be here regarded as external asynchronous stimuli. Through extensive numerical simulations, we focus on the effects of stimulus number and inter-stimulus timing on synaptic connecting weights and the property of propagation dynamics in the resulting network structure. Interestingly, the resulting feedforward structure induced by stimulus-dependent asynchronous firings and its propagation dynamics reflect both the underlying property of STDP. The results imply a possible important role of STDP in generating feedforward structure and collective propagation activity required for experience-dependent map plasticity in developing in vivo sensory pathways and cortices. The relevance of the results to cue-triggered recall of learned temporal sequences, an important cognitive function, is briefly discussed as well. Furthermore, this finding suggests a potential application for examining STDP by measuring neural population activity in a cultured neural network.

Gamma oscillations in a nonlinear regime: a minimal model approach using heterogeneous integrate-and-fire networks.

PubMed

Bathellier, Brice; Carleton, Alan; Gerstner, Wulfram

2008-12-01

Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency.

Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

Effect of inhibitory feedback on correlated firing of spiking neural network.

PubMed

Xie, Jinli; Wang, Zhijie

2013-08-01

Understanding the properties and mechanisms that generate different forms of correlation is critical for determining their role in cortical processing. Researches on retina, visual cortex, sensory cortex, and computational model have suggested that fast correlation with high temporal precision appears consistent with common input, and correlation on a slow time scale likely involves feedback. Based on feedback spiking neural network model, we investigate the role of inhibitory feedback in shaping correlations on a time scale of 100 ms. Notably, the relationship between the correlation coefficient and inhibitory feedback strength is non-monotonic. Further, computational simulations show how firing rate and oscillatory activity form the basis of the mechanisms underlying this relationship. When the mean firing rate holds unvaried, the correlation coefficient increases monotonically with inhibitory feedback, but the correlation coefficient keeps decreasing when the network has no oscillatory activity. Our findings reveal that two opposing effects of the inhibitory feedback on the firing activity of the network contribute to the non-monotonic relationship between the correlation coefficient and the strength of the inhibitory feedback. The inhibitory feedback affects the correlated firing activity by modulating the intensity and regularity of the spike trains. Finally, the non-monotonic relationship is replicated with varying transmission delay and different spatial network structure, demonstrating the universality of the results.

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks

PubMed Central

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections. PMID:28197088

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

PubMed

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

Leader neurons in leaky integrate and fire neural network simulations.

PubMed

Zbinden, Cyrille

2011-10-01

In this paper, we highlight the topological properties of leader neurons whose existence is an experimental fact. Several experimental studies show the existence of leader neurons in population bursts of activity in 2D living neural networks (Eytan and Marom, J Neurosci 26(33):8465-8476, 2006; Eckmann et al., New J Phys 10(015011), 2008). A leader neuron is defined as a neuron which fires at the beginning of a burst (respectively network spike) more often than we expect by chance considering its mean firing rate. This means that leader neurons have some burst triggering power beyond a chance-level statistical effect. In this study, we characterize these leader neuron properties. This naturally leads us to simulate neural 2D networks. To build our simulations, we choose the leaky integrate and fire (lIF) neuron model (Gerstner and Kistler 2002; Cessac, J Math Biol 56(3):311-345, 2008), which allows fast simulations (Izhikevich, IEEE Trans Neural Netw 15(5):1063-1070, 2004; Gerstner and Naud, Science 326:379-380, 2009). The dynamics of our lIF model has got stable leader neurons in the burst population that we simulate. These leader neurons are excitatory neurons and have a low membrane potential firing threshold. Except for these two first properties, the conditions required for a neuron to be a leader neuron are difficult to identify and seem to depend on several parameters involved in the simulations themselves. However, a detailed linear analysis shows a trend of the properties required for a neuron to be a leader neuron. Our main finding is: A leader neuron sends signals to many excitatory neurons as well as to few inhibitory neurons and a leader neuron receives only signals from few other excitatory neurons. Our linear analysis exhibits five essential properties of leader neurons each with different relative importance. This means that considering a given neural network with a fixed mean number of connections per neuron, our analysis gives us a way of predicting which neuron is a good leader neuron and which is not. Our prediction formula correctly assesses leadership for at least ninety percent of neurons.

A Recurrent Network Model of Somatosensory Parametric Working Memory in the Prefrontal Cortex

PubMed Central

Miller, Paul; Brody, Carlos D; Romo, Ranulfo; Wang, Xiao-Jing

2015-01-01

A parametric working memory network stores the information of an analog stimulus in the form of persistent neural activity that is monotonically tuned to the stimulus. The family of persistent firing patterns with a continuous range of firing rates must all be realizable under exactly the same external conditions (during the delay when the transient stimulus is withdrawn). How this can be accomplished by neural mechanisms remains an unresolved question. Here we present a recurrent cortical network model of irregularly spiking neurons that was designed to simulate a somatosensory working memory experiment with behaving monkeys. Our model reproduces the observed positively and negatively monotonic persistent activity, and heterogeneous tuning curves of memory activity. We show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network. Moreover, we show that the fine-tuned network can integrate stimulus inputs over several seconds. Assuming that such time integration occurs in neural populations downstream from a tonically persistent neural population, our model is able to account for the slow ramping-up and ramping-down behaviors of neurons observed in prefrontal cortex. PMID:14576212

A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons.

PubMed

Beim Graben, Peter; Rodrigues, Serafim

2012-01-01

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

PubMed Central

beim Graben, Peter; Rodrigues, Serafim

2013-01-01

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the “open-field” configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement. PMID:23316157

Predicting neural network firing pattern from phase resetting curve

NASA Astrophysics Data System (ADS)

Oprisan, Sorinel; Oprisan, Ana

2007-04-01

Autonomous neural networks called central pattern generators (CPG) are composed of endogenously bursting neurons and produce rhythmic activities, such as flying, swimming, walking, chewing, etc. Simplified CPGs for quadrupedal locomotion and swimming are modeled by a ring of neural oscillators such that the output of one oscillator constitutes the input for the subsequent neural oscillator. The phase response curve (PRC) theory discards the detailed conductance-based description of the component neurons of a network and reduces them to ``black boxes'' characterized by a transfer function, which tabulates the transient change in the intrinsic period of a neural oscillator subject to external stimuli. Based on open-loop PRC, we were able to successfully predict the phase-locked period and relative phase between neurons in a half-center network. We derived existence and stability criteria for heterogeneous ring neural networks that are in good agreement with experimental data.

Optimal Space Station solar array gimbal angle determination via radial basis function neural networks

NASA Technical Reports Server (NTRS)

Clancy, Daniel J.; Oezguener, Uemit; Graham, Ronald E.

1994-01-01

The potential for excessive plume impingement loads on Space Station Freedom solar arrays, caused by jet firings from an approaching Space Shuttle, is addressed. An artificial neural network is designed to determine commanded solar array beta gimbal angle for minimum plume loads. The commanded angle would be determined dynamically. The network design proposed involves radial basis functions as activation functions. Design, development, and simulation of this network design are discussed.

Predicting Slag Generation in Sub-Scale Test Motors Using a Neural Network

NASA Technical Reports Server (NTRS)

Wiesenberg, Brent

1999-01-01

Generation of slag (aluminum oxide) is an important issue for the Reusable Solid Rocket Motor (RSRM). Thiokol performed testing to quantify the relationship between raw material variations and slag generation in solid propellants by testing sub-scale motors cast with propellant containing various combinations of aluminum fuel and ammonium perchlorate (AP) oxidizer particle sizes. The test data were analyzed using statistical methods and an artificial neural network. This paper primarily addresses the neural network results with some comparisons to the statistical results. The neural network showed that the particle sizes of both the aluminum and unground AP have a measurable effect on slag generation. The neural network analysis showed that aluminum particle size is the dominant driver in slag generation, about 40% more influential than AP. The network predictions of the amount of slag produced during firing of sub-scale motors were 16% better than the predictions of a statistically derived empirical equation. Another neural network successfully characterized the slag generated during full-scale motor tests. The success is attributable to the ability of neural networks to characterize multiple complex factors including interactions that affect slag generation.

Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex.

PubMed

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo; Vanni, Simo

2015-01-01

In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate (which has direct association to neural energy consumption), entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response.

Exact event-driven implementation for recurrent networks of stochastic perfect integrate-and-fire neurons.

PubMed

Taillefumier, Thibaud; Touboul, Jonathan; Magnasco, Marcelo

2012-12-01

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks' dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.

Event-driven simulations of nonlinear integrate-and-fire neurons.

PubMed

Tonnelier, Arnaud; Belmabrouk, Hana; Martinez, Dominique

2007-12-01

Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.

Modeling fluctuations in default-mode brain network using a spiking neural network.

PubMed

Yamanishi, Teruya; Liu, Jian-Qin; Nishimura, Haruhiko

2012-08-01

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.

Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

PubMed Central

Weick, Jason P.; Liu, Yan; Zhang, Su-Chun

2011-01-01

Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo. PMID:22106298

A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks

PubMed Central

Schaffer, Evan S.; Ostojic, Srdjan; Abbott, L. F.

2013-01-01

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons. PMID:24204236

Hybrid discrete-time neural networks.

PubMed

Cao, Hongjun; Ibarz, Borja

2010-11-13

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

Metastability and Inter-Band Frequency Modulation in Networks of Oscillating Spiking Neuron Populations

PubMed Central

Bhowmik, David; Shanahan, Murray

2013-01-01

Groups of neurons firing synchronously are hypothesized to underlie many cognitive functions such as attention, associative learning, memory, and sensory selection. Recent theories suggest that transient periods of synchronization and desynchronization provide a mechanism for dynamically integrating and forming coalitions of functionally related neural areas, and that at these times conditions are optimal for information transfer. Oscillating neural populations display a great amount of spectral complexity, with several rhythms temporally coexisting in different structures and interacting with each other. This paper explores inter-band frequency modulation between neural oscillators using models of quadratic integrate-and-fire neurons and Hodgkin-Huxley neurons. We vary the structural connectivity in a network of neural oscillators, assess the spectral complexity, and correlate the inter-band frequency modulation. We contrast this correlation against measures of metastable coalition entropy and synchrony. Our results show that oscillations in different neural populations modulate each other so as to change frequency, and that the interaction of these fluctuating frequencies in the network as a whole is able to drive different neural populations towards episodes of synchrony. Further to this, we locate an area in the connectivity space in which the system directs itself in this way so as to explore a large repertoire of synchronous coalitions. We suggest that such dynamics facilitate versatile exploration, integration, and communication between functionally related neural areas, and thereby supports sophisticated cognitive processing in the brain. PMID:23614040

A solution to neural field equations by a recurrent neural network method

NASA Astrophysics Data System (ADS)

Alharbi, Abir

2012-09-01

Neural field equations (NFE) are used to model the activity of neurons in the brain, it is introduced from a single neuron 'integrate-and-fire model' starting point. The neural continuum is spatially discretized for numerical studies, and the governing equations are modeled as a system of ordinary differential equations. In this article the recurrent neural network approach is used to solve this system of ODEs. This consists of a technique developed by combining the standard numerical method of finite-differences with the Hopfield neural network. The architecture of the net, energy function, updating equations, and algorithms are developed for the NFE model. A Hopfield Neural Network is then designed to minimize the energy function modeling the NFE. Results obtained from the Hopfield-finite-differences net show excellent performance in terms of accuracy and speed. The parallelism nature of the Hopfield approaches may make them easier to implement on fast parallel computers and give them the speed advantage over the traditional methods.

On a phase diagram for random neural networks with embedded spike timing dependent plasticity.

PubMed

Turova, Tatyana S; Villa, Alessandro E P

2007-01-01

This paper presents an original mathematical framework based on graph theory which is a first attempt to investigate the dynamics of a model of neural networks with embedded spike timing dependent plasticity. The neurons correspond to integrate-and-fire units located at the vertices of a finite subset of 2D lattice. There are two types of vertices, corresponding to the inhibitory and the excitatory neurons. The edges are directed and labelled by the discrete values of the synaptic strength. We assume that there is an initial firing pattern corresponding to a subset of units that generate a spike. The number of activated externally vertices is a small fraction of the entire network. The model presented here describes how such pattern propagates throughout the network as a random walk on graph. Several results are compared with computational simulations and new data are presented for identifying critical parameters of the model.

Predictive Coding of Dynamical Variables in Balanced Spiking Networks

PubMed Central

Boerlin, Martin; Machens, Christian K.; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated. PMID:24244113

Equalization of Synaptic Efficacy by Synchronous Neural Activity

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Choi, M. Y.

2007-11-01

It is commonly believed that spike timings of a postsynaptic neuron tend to follow those of the presynaptic neuron. Such orthodromic firing may, however, cause a conflict with the functional integrity of complex neuronal networks due to asymmetric temporal Hebbian plasticity. We argue that reversed spike timing in a synapse is a typical phenomenon in the cortex, which has a stabilizing effect on the neuronal network structure. We further demonstrate how the firing causality in a synapse is perturbed by synchronous neural activity and how the equilibrium property of spike-timing dependent plasticity is determined principally by the degree of synchronization. Remarkably, even noise-induced activity and synchrony of neurons can result in equalization of synaptic efficacy.

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

PubMed Central

Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

2018-01-01

Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural encoding. We believe that our study shed lights on the mechanism underlying the efficient neural information processing via adaptation. PMID:29636675

A generalized linear integrate-and-fire neural model produces diverse spiking behaviors.

PubMed

Mihalaş, Stefan; Niebur, Ernst

2009-03-01

For simulations of neural networks, there is a trade-off between the size of the network that can be simulated and the complexity of the model used for individual neurons. In this study, we describe a generalization of the leaky integrate-and-fire model that produces a wide variety of spiking behaviors while still being analytically solvable between firings. For different parameter values, the model produces spiking or bursting, tonic, phasic or adapting responses, depolarizing or hyperpolarizing after potentials and so forth. The model consists of a diagonalizable set of linear differential equations describing the time evolution of membrane potential, a variable threshold, and an arbitrary number of firing-induced currents. Each of these variables is modified by an update rule when the potential reaches threshold. The variables used are intuitive and have biological significance. The model's rich behavior does not come from the differential equations, which are linear, but rather from complex update rules. This single-neuron model can be implemented using algorithms similar to the standard integrate-and-fire model. It is a natural match with event-driven algorithms for which the firing times are obtained as a solution of a polynomial equation.

A Generalized Linear Integrate-and-Fire Neural Model Produces Diverse Spiking Behaviors

PubMed Central

Mihalaş, Ştefan; Niebur, Ernst

2010-01-01

For simulations of neural networks, there is a trade-off between the size of the network that can be simulated and the complexity of the model used for individual neurons. In this study, we describe a generalization of the leaky integrate-and-fire model that produces a wide variety of spiking behaviors while still being analytically solvable between firings. For different parameter values, the model produces spiking or bursting, tonic, phasic or adapting responses, depolarizing or hyperpolarizing after potentials and so forth. The model consists of a diagonalizable set of linear differential equations describing the time evolution of membrane potential, a variable threshold, and an arbitrary number of firing-induced currents. Each of these variables is modified by an update rule when the potential reaches threshold. The variables used are intuitive and have biological significance. The model’s rich behavior does not come from the differential equations, which are linear, but rather from complex update rules. This single-neuron model can be implemented using algorithms similar to the standard integrate-and-fire model. It is a natural match with event-driven algorithms for which the firing times are obtained as a solution of a polynomial equation. PMID:18928368

Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model

PubMed Central

2017-01-01

Electrical activity is the foundation of the neural system. Coding theories that describe neural electrical activity by the roles of action potential timing or frequency have been thoroughly studied. However, an alternative method to study coding questions is the energy method, which is more global and economical. In this study, we clearly defined and calculated neural energy supply and consumption based on the Hodgkin-Huxley model, during firing action potentials and subthreshold activities using ion-counting and power-integral model. Furthermore, we analyzed energy properties of each ion channel and found that, under the two circumstances, power synchronization of ion channels and energy utilization ratio have significant differences. This is particularly true of the energy utilization ratio, which can rise to above 100% during subthreshold activity, revealing an overdraft property of energy use. These findings demonstrate the distinct status of the energy properties during neuronal firings and subthreshold activities. Meanwhile, after introducing a synapse energy model, this research can be generalized to energy calculation of a neural network. This is potentially important for understanding the relationship between dynamical network activities and cognitive behaviors. PMID:28316842

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

PubMed

Bush, Daniel; Philippides, Andrew; Husbands, Phil; O'Shea, Michael

2010-07-01

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain.

Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study

NASA Astrophysics Data System (ADS)

Pfeil, Thomas; Jordan, Jakob; Tetzlaff, Tom; Grübl, Andreas; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz

2016-04-01

High-level brain function, such as memory, classification, or reasoning, can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy-efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear subthreshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with nonlinear, conductance-based synapses. Emulations of these networks on the analog neuromorphic-hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm that shared-input correlations are actively suppressed by inhibitory feedback also in highly heterogeneous networks exhibiting broad, heavy-tailed firing-rate distributions. In line with former studies, cell heterogeneities reduce shared-input correlations. Overall, however, correlations in the recurrent system can increase with the level of heterogeneity as a consequence of diminished effective negative feedback.

Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

NASA Astrophysics Data System (ADS)

Bertolotti, Elena; Burioni, Raffaella; di Volo, Matteo; Vezzani, Alessandro

2017-01-01

We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons fI and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on fI, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.

Modeling of cortical signals using echo state networks

NASA Astrophysics Data System (ADS)

Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

2009-10-01

Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.

Distributed synaptic weights in a LIF neural network and learning rules

NASA Astrophysics Data System (ADS)

Perthame, Benoît; Salort, Delphine; Wainrib, Gilles

2017-09-01

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connectivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorize a learned signal.

Computational Account of Spontaneous Activity as a Signature of Predictive Coding

PubMed Central

Koren, Veronika

2017-01-01

Spontaneous activity is commonly observed in a variety of cortical states. Experimental evidence suggested that neural assemblies undergo slow oscillations with Up ad Down states even when the network is isolated from the rest of the brain. Here we show that these spontaneous events can be generated by the recurrent connections within the network and understood as signatures of neural circuits that are correcting their internal representation. A noiseless spiking neural network can represent its input signals most accurately when excitatory and inhibitory currents are as strong and as tightly balanced as possible. However, in the presence of realistic neural noise and synaptic delays, this may result in prohibitively large spike counts. An optimal working regime can be found by considering terms that control firing rates in the objective function from which the network is derived and then minimizing simultaneously the coding error and the cost of neural activity. In biological terms, this is equivalent to tuning neural thresholds and after-spike hyperpolarization. In suboptimal working regimes, we observe spontaneous activity even in the absence of feed-forward inputs. In an all-to-all randomly connected network, the entire population is involved in Up states. In spatially organized networks with local connectivity, Up states spread through local connections between neurons of similar selectivity and take the form of a traveling wave. Up states are observed for a wide range of parameters and have similar statistical properties in both active and quiescent state. In the optimal working regime, Up states are vanishing, leaving place to asynchronous activity, suggesting that this working regime is a signature of maximally efficient coding. Although they result in a massive increase in the firing activity, the read-out of spontaneous Up states is in fact orthogonal to the stimulus representation, therefore interfering minimally with the network function. PMID:28114353

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex.

PubMed

Kendrick, Keith M; Zhan, Yang; Fischer, Hanno; Nicol, Alister U; Zhang, Xuejuan; Feng, Jianfeng

2011-06-09

How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex

PubMed Central

2011-01-01

Background How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. Results Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. Conclusions Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs. PMID:21658251

Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment

PubMed Central

Legenstein, Robert; Maass, Wolfgang

2014-01-01

It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through “neural sampling”, i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operations on rapidly varying probability distributions, such as the odds of getting rewarded for a specific behavior. We demonstrate the viability of this new approach towards neural coding and computation, which makes use of the inherent parallelism of generic neural circuits, by showing that this model can explain experimentally observed firing activity of cortical neurons for a variety of tasks that require rapid temporal integration of sensory information. PMID:25340749

Automated Wildfire Detection Through Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

2005-01-01

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

When do correlations increase with firing rates in recurrent networks?

PubMed Central

2017-01-01

A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix. PMID:28448499

Spike timing analysis in neural networks with unsupervised synaptic plasticity

NASA Astrophysics Data System (ADS)

Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

2013-01-01

The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

Integrate and fire neural networks, piecewise contractive maps and limit cycles.

PubMed

Catsigeras, Eleonora; Guiraud, Pierre

2013-09-01

We study the global dynamics of integrate and fire neural networks composed of an arbitrary number of identical neurons interacting by inhibition and excitation. We prove that if the interactions are strong enough, then the support of the stable asymptotic dynamics consists of limit cycles. We also find sufficient conditions for the synchronization of networks containing excitatory neurons. The proofs are based on the analysis of the equivalent dynamics of a piecewise continuous Poincaré map associated to the system. We show that for efficient interactions the Poincaré map is piecewise contractive. Using this contraction property, we prove that there exist a countable number of limit cycles attracting all the orbits dropping into the stable subset of the phase space. This result applies not only to the Poincaré map under study, but also to a wide class of general n-dimensional piecewise contractive maps.

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

PubMed

Li, Guoqiang; Niu, Peifeng; Wang, Huaibao; Liu, Yongchao

2014-03-01

This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

Environmentally induced amplitude death and firing provocation in large-scale networks of neuronal systems

NASA Astrophysics Data System (ADS)

Pankratova, Evgeniya V.; Kalyakulina, Alena I.

2016-12-01

We study the dynamics of multielement neuronal systems taking into account both the direct interaction between the cells via linear coupling and nondiffusive cell-to-cell communication via common environment. For the cells exhibiting individual bursting behavior, we have revealed the dependence of the network activity on its scale. Particularly, we show that small-scale networks demonstrate the inability to maintain complicated oscillations: for a small number of elements in an ensemble, the phenomenon of amplitude death is observed. The existence of threshold network scales and mechanisms causing firing in artificial and real multielement neural networks, as well as their significance for biological applications, are discussed.

Emergent gamma synchrony in all-to-all interneuronal networks.

PubMed

Ratnadurai-Giridharan, Shivakeshavan; Khargonekar, Pramod P; Talathi, Sachin S

2015-01-01

We investigate the emergence of in-phase synchronization in a heterogeneous network of coupled inhibitory interneurons in the presence of spike timing dependent plasticity (STDP). Using a simple network of two mutually coupled interneurons (2-MCI), we first study the effects of STDP on in-phase synchronization. We demonstrate that, with STDP, the 2-MCI network can evolve to either a state of stable 1:1 in-phase synchronization or exhibit multiple regimes of higher order synchronization states. We show that the emergence of synchronization induces a structural asymmetry in the 2-MCI network such that the synapses onto the high frequency firing neurons are potentiated, while those onto the low frequency firing neurons are de-potentiated, resulting in the directed flow of information from low frequency firing neurons to high frequency firing neurons. Finally, we demonstrate that the principal findings from our analysis of the 2-MCI network contribute to the emergence of robust synchronization in the Wang-Buzsaki network (Wang and Buzsáki, 1996) of all-to-all coupled inhibitory interneurons (100-MCI) for a significantly larger range of heterogeneity in the intrinsic firing rate of the neurons in the network. We conclude that STDP of inhibitory synapses provide a viable mechanism for robust neural synchronization.

Emergent gamma synchrony in all-to-all interneuronal networks

PubMed Central

Ratnadurai-Giridharan, Shivakeshavan; Khargonekar, Pramod P.; Talathi, Sachin S.

2015-01-01

We investigate the emergence of in-phase synchronization in a heterogeneous network of coupled inhibitory interneurons in the presence of spike timing dependent plasticity (STDP). Using a simple network of two mutually coupled interneurons (2-MCI), we first study the effects of STDP on in-phase synchronization. We demonstrate that, with STDP, the 2-MCI network can evolve to either a state of stable 1:1 in-phase synchronization or exhibit multiple regimes of higher order synchronization states. We show that the emergence of synchronization induces a structural asymmetry in the 2-MCI network such that the synapses onto the high frequency firing neurons are potentiated, while those onto the low frequency firing neurons are de-potentiated, resulting in the directed flow of information from low frequency firing neurons to high frequency firing neurons. Finally, we demonstrate that the principal findings from our analysis of the 2-MCI network contribute to the emergence of robust synchronization in the Wang-Buzsaki network (Wang and Buzsáki, 1996) of all-to-all coupled inhibitory interneurons (100-MCI) for a significantly larger range of heterogeneity in the intrinsic firing rate of the neurons in the network. We conclude that STDP of inhibitory synapses provide a viable mechanism for robust neural synchronization. PMID:26528174

Effect of gap junctions on the firing patterns and synchrony for different external inputs in the striatal fast-spiking neuron network.

PubMed

Zhang, Mingming; Zhao, Zongya; He, Ping; Wang, Jue

2014-01-01

Gap junctions are the mechanism for striatal fast-spiking interneurons (FSIs) to interconnect with each other and play an important role in determining the physiological functioning of the FSIs. To investigate the effect of gap junctions on the firing activities and synchronization of the network for different external inputs, a simple network with least connections and a Newman-Watts small-world network were constructed. Our research shows that both properties of neural networks are related to the conductance of the gap junctions, as well as the frequency and correlation of the external inputs. The effect of gap junctions on the synchronization of network is different for inputs with different frequencies and correlations. The addition of gap junctions can promote the network synchrony in some conditions but suppress it in others, and they can inhibit the firing activities in most cases. Both the firing rate and synchronization of the network increase along with the increase of the electrical coupling strength for inputs with low frequency and high correlation. Thus, the network of coupled FSIs can act as a detector for synchronous synaptic input from cortex and thalamus.

[Predicting low NOx combustion property of a coal-fired boiler].

PubMed

Zhou, Hao; Mao, Jianbo; Chi, Zuohe; Jiang, Xiao; Wang, Zhenhua; Cen, Kefa

2002-03-01

More attention was paid to the low NOx combustion property of the high capacity tangential firing boiler, but the NOx emission and unburned carbon content in fly ash of coal burned boiler were complicated, they were affected by many factors, such as coal character, boiler's load, air distribution, boiler style, burner style, furnace temperature, excess air ratio, pulverized coal fineness and the uniformity of the air and coal distribution, etc. In this paper, the NOx emission property and unburned carbon content in fly ash of a 600 MW utility tangentially firing coal burned boiler was experimentally investigated, and taking advantage of the nonlinear dynamics characteristics and self-learning characteristics of artificial neural network, an artificial neural network model on low NOx combustion property of the high capacity boiler was developed and verified. The results illustrated that such a model can predicate the NOx emission concentration and unburned carbon content under various operating conditions, if combined with the optimization algorithm, the operator can find the best operation condition of the low NOx combustion.

Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

PubMed

Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

2017-02-01

Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks

PubMed Central

Solanka, Lukas; van Rossum, Mark CW; Nolan, Matthew F

2015-01-01

Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength. DOI: http://dx.doi.org/10.7554/eLife.06444.001 PMID:26146940

A solution for two-dimensional mazes with use of chaotic dynamics in a recurrent neural network model.

PubMed

Suemitsu, Yoshikazu; Nara, Shigetoshi

2004-09-01

Chaotic dynamics introduced into a neural network model is applied to solving two-dimensional mazes, which are ill-posed problems. A moving object moves from the position at t to t + 1 by simply defined motion function calculated from firing patterns of the neural network model at each time step t. We have embedded several prototype attractors that correspond to the simple motion of the object orienting toward several directions in two-dimensional space in our neural network model. Introducing chaotic dynamics into the network gives outputs sampled from intermediate state points between embedded attractors in a state space, and these dynamics enable the object to move in various directions. System parameter switching between a chaotic and an attractor regime in the state space of the neural network enables the object to move to a set target in a two-dimensional maze. Results of computer simulations show that the success rate for this method over 300 trials is higher than that of random walk. To investigate why the proposed method gives better performance, we calculate and discuss statistical data with respect to dynamical structure.

Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

PubMed

Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

2017-06-01

The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Beyond blow-up in excitatory integrate and fire neuronal networks: Refractory period and spontaneous activity.

PubMed

Cáceres, María J; Perthame, Benoît

2014-06-07

The Network Noisy Leaky Integrate and Fire equation is among the simplest model allowing for a self-consistent description of neural networks and gives a rule to determine the probability to find a neuron at the potential v. However, its mathematical structure is still poorly understood and, concerning its solutions, very few results are available. In the midst of them, a recent result shows blow-up in finite time for fully excitatory networks. The intuitive explanation is that each firing neuron induces a discharge of the others; thus increases the activity and consequently the discharge rate of the full network. In order to better understand the details of the phenomena and show that the equation is more complex and fruitful than expected, we analyze further the model. We extend the finite time blow-up result to the case when neurons, after firing, enter a refractory state for a given period of time. We also show that spontaneous activity may occur when, additionally, randomness is included on the firing potential VF in regimes where blow-up occurs for a fixed value of VF. Copyright © 2014 Elsevier Ltd. All rights reserved.

Design of AN Intelligent Individual Evacuation Model for High Rise Building Fires Based on Neural Network Within the Scope of 3d GIS

NASA Astrophysics Data System (ADS)

Atila, U.; Karas, I. R.; Turan, M. K.; Rahman, A. A.

2013-09-01

One of the most dangerous disaster threatening the high rise and complex buildings of today's world including thousands of occupants inside is fire with no doubt. When we consider high population and the complexity of such buildings it is clear to see that performing a rapid and safe evacuation seems hard and human being does not have good memories in case of such disasters like world trade center 9/11. Therefore, it is very important to design knowledge based realtime interactive evacuation methods instead of classical strategies which lack of flexibility. This paper presents a 3D-GIS implementation which simulates the behaviour of an intelligent indoor pedestrian navigation model proposed for a self -evacuation of a person in case of fire. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. A sample fire scenario following through predefined instructions has been performed on 3D model of the Corporation Complex in Putrajaya (Malaysia) and the intelligent evacuation process has been realized within a proposed 3D-GIS based simulation.

A multivariate extension of mutual information for growing neural networks.

PubMed

Ball, Kenneth R; Grant, Christopher; Mundy, William R; Shafer, Timothy J

2017-11-01

Recordings of neural network activity in vitro are increasingly being used to assess the development of neural network activity and the effects of drugs, chemicals and disease states on neural network function. The high-content nature of the data derived from such recordings can be used to infer effects of compounds or disease states on a variety of important neural functions, including network synchrony. Historically, synchrony of networks in vitro has been assessed either by determination of correlation coefficients (e.g. Pearson's correlation), by statistics estimated from cross-correlation histograms between pairs of active electrodes, and/or by pairwise mutual information and related measures. The present study examines the application of Normalized Multiinformation (NMI) as a scalar measure of shared information content in a multivariate network that is robust with respect to changes in network size. Theoretical simulations are designed to investigate NMI as a measure of complexity and synchrony in a developing network relative to several alternative approaches. The NMI approach is applied to these simulations and also to data collected during exposure of in vitro neural networks to neuroactive compounds during the first 12 days in vitro, and compared to other common measures, including correlation coefficients and mean firing rates of neurons. NMI is shown to be more sensitive to developmental effects than first order synchronous and nonsynchronous measures of network complexity. Finally, NMI is a scalar measure of global (rather than pairwise) mutual information in a multivariate network, and hence relies on less assumptions for cross-network comparisons than historical approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Information Transmission and Anderson Localization in two-dimensional networks of firing-rate neurons

NASA Astrophysics Data System (ADS)

Natale, Joseph; Hentschel, George

Firing-rate networks offer a coarse model of signal propagation in the brain. Here we analyze sparse, 2D planar firing-rate networks with no synapses beyond a certain cutoff distance. Additionally, we impose Dale's Principle to ensure that each neuron makes only or inhibitory outgoing connections. Using spectral methods, we find that the number of neurons participating in excitations of the network becomes insignificant whenever the connectivity cutoff is tuned to a value near or below the average interneuron separation. Further, neural activations exceeding a certain threshold stay confined to a small region of space. This behavior is an instance of Anderson localization, a disorder-induced phase transition by which an information channel is rendered unable to transmit signals. We discuss several potential implications of localization for both local and long-range computation in the brain. This work was supported in part by Grants JSMF/ 220020321 and NSF/IOS/1208126.

Spatiotemporal coding in the cortex: information flow-based learning in spiking neural networks.

PubMed

Deco, G; Schürmann, B

1999-05-15

We introduce a learning paradigm for networks of integrate-and-fire spiking neurons that is based on an information-theoretic criterion. This criterion can be viewed as a first principle that demonstrates the experimentally observed fact that cortical neurons display synchronous firing for some stimuli and not for others. The principle can be regarded as the postulation of a nonparametric reconstruction method as optimization criteria for learning the required functional connectivity that justifies and explains synchronous firing for binding of features as a mechanism for spatiotemporal coding. This can be expressed in an information-theoretic way by maximizing the discrimination ability between different sensory inputs in minimal time.

The Complexity of Dynamics in Small Neural Circuits

PubMed Central

Panzeri, Stefano

2016-01-01

Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing. PMID:27494737

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

PubMed

Zenke, Friedemann; Ganguli, Surya

2018-06-01

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Fire flame detection based on GICA and target tracking

NASA Astrophysics Data System (ADS)

Rong, Jianzhong; Zhou, Dechuang; Yao, Wei; Gao, Wei; Chen, Juan; Wang, Jian

2013-04-01

To improve the video fire detection rate, a robust fire detection algorithm based on the color, motion and pattern characteristics of fire targets was proposed, which proved a satisfactory fire detection rate for different fire scenes. In this fire detection algorithm: (a) a rule-based generic color model was developed based on analysis on a large quantity of flame pixels; (b) from the traditional GICA (Geometrical Independent Component Analysis) model, a Cumulative Geometrical Independent Component Analysis (C-GICA) model was developed for motion detection without static background and (c) a BP neural network fire recognition model based on multi-features of the fire pattern was developed. Fire detection tests on benchmark fire video clips of different scenes have shown the robustness, accuracy and fast-response of the algorithm.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Parameter estimation in spiking neural networks: a reverse-engineering approach.

PubMed

Rostro-Gonzalez, H; Cessac, B; Vieville, T

2012-04-01

This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

PubMed Central

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-01-01

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

PubMed

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-08-04

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

Center for Neural Engineering: applications of pulse-coupled neural networks

NASA Astrophysics Data System (ADS)

Malkani, Mohan; Bodruzzaman, Mohammad; Johnson, John L.; Davis, Joel

1999-03-01

Pulsed-Coupled Neural Network (PCNN) is an oscillatory model neural network where grouping of cells and grouping among the groups that form the output time series (number of cells that fires in each input presentation also called `icon'). This is based on the synchronicity of oscillations. Recent work by Johnson and others demonstrated the functional capabilities of networks containing such elements for invariant feature extraction using intensity maps. PCNN thus presents itself as a more biologically plausible model with solid functional potential. This paper will present the summary of several projects and their results where we successfully applied PCNN. In project one, the PCNN was applied for object recognition and classification through a robotic vision system. The features (icons) generated by the PCNN were then fed into a feedforward neural network for classification. In project two, we developed techniques for sensory data fusion. The PCNN algorithm was implemented and tested on a B14 mobile robot. The PCNN-based features were extracted from the images taken from the robot vision system and used in conjunction with the map generated by data fusion of the sonar and wheel encoder data for the navigation of the mobile robot. In our third project, we applied the PCNN for speaker recognition. The spectrogram image of speech signals are fed into the PCNN to produce invariant feature icons which are then fed into a feedforward neural network for speaker identification.

Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.

PubMed

Tuckwell, Henry C

2006-01-01

The circuitry of cortical networks involves interacting populations of excitatory (E) and inhibitory (I) neurons whose relationships are now known to a large extent. Inputs to E- and I-cells may have their origins in remote or local cortical areas. We consider a rudimentary model involving E- and I-cells. One of our goals is to test an analytic approach to finding firing rates in neural networks without using a diffusion approximation and to this end we consider in detail networks of excitatory neurons with leaky integrate-and-fire (LIF) dynamics. A simple measure of synchronization, denoted by S(q), where q is between 0 and 100 is introduced. Fully connected E-networks have a large tendency to become dominated by synchronously firing groups of cells, except when inputs are relatively weak. We observed random or asynchronous firing in such networks with diverse sets of parameter values. When such firing patterns were found, the analytical approach was often able to accurately predict average neuronal firing rates. We also considered several properties of E-E networks, distinguishing several kinds of firing pattern. Included were those with silences before or after periods of intense activity or with periodic synchronization. We investigated the occurrence of synchronized firing with respect to changes in the internal excitatory postsynaptic potential (EPSP) magnitude in a network of 100 neurons with fixed values of the remaining parameters. When the internal EPSP size was less than a certain value, synchronization was absent. The amount of synchronization then increased slowly as the EPSP amplitude increased until at a particular EPSP size the amount of synchronization abruptly increased, with S(5) attaining the maximum value of 100%. We also found network frequency transfer characteristics for various network sizes and found a linear dependence of firing frequency over wide ranges of the external afferent frequency, with non-linear effects at lower input frequencies. The theory may also be applied to sparsely connected networks, whose firing behaviour was found to change abruptly as the probability of a connection passed through a critical value. The analytical method was also found to be useful for a feed-forward excitatory network and a network of excitatory and inhibitory neurons.

Intelligent emissions controller for substance injection in the post-primary combustion zone of fossil-fired boilers

DOEpatents

Reifman, Jaques; Feldman, Earl E.; Wei, Thomas Y. C.; Glickert, Roger W.

2003-01-01

The control of emissions from fossil-fired boilers wherein an injection of substances above the primary combustion zone employs multi-layer feedforward artificial neural networks for modeling static nonlinear relationships between the distribution of injected substances into the upper region of the furnace and the emissions exiting the furnace. Multivariable nonlinear constrained optimization algorithms use the mathematical expressions from the artificial neural networks to provide the optimal substance distribution that minimizes emission levels for a given total substance injection rate. Based upon the optimal operating conditions from the optimization algorithms, the incremental substance cost per unit of emissions reduction, and the open-market price per unit of emissions reduction, the intelligent emissions controller allows for the determination of whether it is more cost-effective to achieve additional increments in emission reduction through the injection of additional substance or through the purchase of emission credits on the open market. This is of particular interest to fossil-fired electrical power plant operators. The intelligent emission controller is particularly adapted for determining the economical control of such pollutants as oxides of nitrogen (NO.sub.x) and carbon monoxide (CO) emitted by fossil-fired boilers by the selective introduction of multiple inputs of substances (such as natural gas, ammonia, oil, water-oil emulsion, coal-water slurry and/or urea, and combinations of these substances) above the primary combustion zone of fossil-fired boilers.

Early distinction system of mine fire in underground by using a neural-network system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ohga, Kotaro; Higuchi, Kiyoshi

1996-12-31

In our laboratory, a new detection system using smell detectors was developed to detect the spontaneous combustion of coal and the combustion of other materials used underground. The results of experiments clearly the combustion of materials can be detected earlier by this detection system than by conventional detectors for gas and smoke, and there were significant differences between output data from each smell detector for coal, rubber, oil and wood. In order to discern the source of combustion gases, we have been developing a distinction system using a neural-network system. It has shown successful results in laboratory tests. This papermore » describes our detection system using smell detectors and our distinction system which uses a neural-network system, and presents results of experiments using both systems.« less

Using artificial neural networks to classify unknown volatile chemicals from the firings of insect olfactory sensory neurons.

PubMed

Bachtiar, Luqman R; Unsworth, Charles P; Newcomb, Richard D; Crampin, Edmund J

2011-01-01

The olfactory system detects volatile chemical compounds, known as odour molecules or odorants. Such odorants have a diverse chemical structure which in turn interact with the receptors of the olfactory system. The insect olfactory system provides a unique opportunity to directly measure the firing rates that are generated by the individual olfactory sensory neurons (OSNs) which have been stimulated by odorants in order to use this data to inform their classification. In this work, we demonstrate that it is possible to use the firing rates from an array of OSNs of the vinegar fly, Drosophila melanogaster, to train an Artificial Neural Network (ANN), as a series of a Multi-Layer Perceptrons (MLPs), to differentiate between eight distinct chemical classes. We demonstrate that the MLPs when trained on 108 odorants, for both clean and 10% noise injected data, can reliably identify 87% of an unseen validation set of chemicals using noise injection. In addition, the noise injected MLPs provide a more accurate level of identification. This demonstrates that a 10% noise injected series of MLPs provides a robust method for classifying chemicals from the firing rates of OSNs and paves the way to a future realisation of an artificial olfactory biosensor.

The effect of an exogenous magnetic field on neural coding in deep spiking neural networks.

PubMed

Guo, Lei; Zhang, Wei; Zhang, Jialei

2018-01-01

A ten-layer feed forward network is constructed in the presence of an exogenous alternating magnetic field. Specifically, our results indicate that for rate coding, the firing rate is significantly increased in the presence of an exogenous alternating magnetic field and particularly with increasing enhancement of the alternating magnetic field amplitude. For temporal coding, the interspike intervals of the spiking sequence are decreased and the distribution of the interspike intervals of the spiking sequence tends to be uniform in the presence of alternating magnetic field.

Irregular Collective Behavior of Heterogeneous Neural Networks

NASA Astrophysics Data System (ADS)

Luccioli, Stefano; Politi, Antonio

2010-10-01

We investigate a network of integrate-and-fire neurons characterized by a distribution of spiking frequencies. Upon increasing the coupling strength, the model exhibits a transition from an asynchronous regime to a nontrivial collective behavior. Numerical simulations of large systems indicate that, at variance with the Kuramoto model, (i) the macroscopic dynamics stays irregular and (ii) the microscopic (single-neuron) evolution is linearly stable.

Phase synchronization motion and neural coding in dynamic transmission of neural information.

PubMed

Wang, Rubin; Zhang, Zhikang; Qu, Jingyi; Cao, Jianting

2011-07-01

In order to explore the dynamic characteristics of neural coding in the transmission of neural information in the brain, a model of neural network consisting of three neuronal populations is proposed in this paper using the theory of stochastic phase dynamics. Based on the model established, the neural phase synchronization motion and neural coding under spontaneous activity and stimulation are examined, for the case of varying network structure. Our analysis shows that, under the condition of spontaneous activity, the characteristics of phase neural coding are unrelated to the number of neurons participated in neural firing within the neuronal populations. The result of numerical simulation supports the existence of sparse coding within the brain, and verifies the crucial importance of the magnitudes of the coupling coefficients in neural information processing as well as the completely different information processing capability of neural information transmission in both serial and parallel couplings. The result also testifies that under external stimulation, the bigger the number of neurons in a neuronal population, the more the stimulation influences the phase synchronization motion and neural coding evolution in other neuronal populations. We verify numerically the experimental result in neurobiology that the reduction of the coupling coefficient between neuronal populations implies the enhancement of lateral inhibition function in neural networks, with the enhancement equivalent to depressing neuronal excitability threshold. Thus, the neuronal populations tend to have a stronger reaction under the same stimulation, and more neurons get excited, leading to more neurons participating in neural coding and phase synchronization motion.

Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.

PubMed

Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime

2016-01-01

It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

Neuronify: An Educational Simulator for Neural Circuits.

PubMed

Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

2017-01-01

Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

PubMed

Cheung, Kit; Schultz, Simon R; Luk, Wayne

2015-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

Neuronify: An Educational Simulator for Neural Circuits

PubMed Central

Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

2017-01-01

Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

PubMed Central

Cheung, Kit; Schultz, Simon R.; Luk, Wayne

2016-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542

Neural coding in graphs of bidirectional associative memories.

PubMed

Bouchain, A David; Palm, Günther

2012-01-24

In the last years we have developed large neural network models for the realization of complex cognitive tasks in a neural network architecture that resembles the network of the cerebral cortex. We have used networks of several cortical modules that contain two populations of neurons (one excitatory, one inhibitory). The excitatory populations in these so-called "cortical networks" are organized as a graph of Bidirectional Associative Memories (BAMs), where edges of the graph correspond to BAMs connecting two neural modules and nodes of the graph correspond to excitatory populations with associative feedback connections (and inhibitory interneurons). The neural code in each of these modules consists essentially of the firing pattern of the excitatory population, where mainly it is the subset of active neurons that codes the contents to be represented. The overall activity can be used to distinguish different properties of the patterns that are represented which we need to distinguish and control when performing complex tasks like language understanding with these cortical networks. The most important pattern properties or situations are: exactly fitting or matching input, incomplete information or partially matching pattern, superposition of several patterns, conflicting information, and new information that is to be learned. We show simple simulations of these situations in one area or module and discuss how to distinguish these situations based on the overall internal activation of the module. This article is part of a Special Issue entitled "Neural Coding". Copyright © 2011 Elsevier B.V. All rights reserved.

Flexible timing by temporal scaling of cortical responses

PubMed Central

Wang, Jing; Narain, Devika; Hosseini, Eghbal A.; Jazayeri, Mehrdad

2017-01-01

Musicians can perform at different tempos, speakers can control the cadence of their speech, and children can flexibly vary their temporal expectations of events. To understand the neural basis of such flexibility, we recorded from the medial frontal cortex of nonhuman primates trained to produce different time intervals with different effectors. Neural responses were heterogeneous, nonlinear and complex, and exhibited a remarkable form of temporal invariance: firing rate profiles were temporally scaled to match the produced intervals. Recording from downstream neurons in the caudate and thalamic neurons projecting to the medial frontal cortex indicated that this phenomenon originates within cortical networks. Recurrent neural network models trained to perform the task revealed that temporal scaling emerges from nonlinearities in the network and degree of scaling is controlled by the strength of external input. These findings demonstrate a simple and general mechanism for conferring temporal flexibility upon sensorimotor and cognitive functions. PMID:29203897

A spiking neural network model of the midbrain superior colliculus that generates saccadic motor commands.

PubMed

Kasap, Bahadir; van Opstal, A John

2017-08-01

Single-unit recordings suggest that the midbrain superior colliculus (SC) acts as an optimal controller for saccadic gaze shifts. The SC is proposed to be the site within the visuomotor system where the nonlinear spatial-to-temporal transformation is carried out: the population encodes the intended saccade vector by its location in the motor map (spatial), and its trajectory and velocity by the distribution of firing rates (temporal). The neurons' burst profiles vary systematically with their anatomical positions and intended saccade vectors, to account for the nonlinear main-sequence kinematics of saccades. Yet, the underlying collicular mechanisms that could result in these firing patterns are inaccessible to current neurobiological techniques. Here, we propose a simple spiking neural network model that reproduces the spike trains of saccade-related cells in the intermediate and deep SC layers during saccades. The model assumes that SC neurons have distinct biophysical properties for spike generation that depend on their anatomical position in combination with a center-surround lateral connectivity. Both factors are needed to account for the observed firing patterns. Our model offers a basis for neuronal algorithms for spatiotemporal transformations and bio-inspired optimal controllers.

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.

A plausible neural circuit for decision making and its formation based on reinforcement learning.

PubMed

Wei, Hui; Dai, Dawei; Bu, Yijie

2017-06-01

A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.

Pulse-coupled neural network sensor fusion

NASA Astrophysics Data System (ADS)

Johnson, John L.; Schamschula, Marius P.; Inguva, Ramarao; Caulfield, H. John

1998-03-01

Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex--the PCNN or Pulse Coupled Neural Network--performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the 3D PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting 2D (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter- term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single 2D pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.

On the of neural modeling of some dynamic parameters of earthquakes and fire safety in high-rise construction

NASA Astrophysics Data System (ADS)

Haritonova, Larisa

2018-03-01

The recent change in the correlation of the number of man-made and natural catastrophes is presented in the paper. Some recommendations are proposed to increase the firefighting efficiency in the high-rise buildings. The article analyzes the methodology of modeling seismic effects. The prospectivity of applying the neural modeling and artificial neural networks to analyze a such dynamic parameters of the earthquake foci as the value of dislocation (or the average rupture slip) is shown. The following two input signals were used: the power class and the number of earthquakes. The regression analysis has been carried out for the predicted results and the target outputs. The equations of the regression for the outputs and target are presented in the work as well as the correlation coefficients in training, validation, testing, and the total (All) for the network structure 2-5-5-1for the average rupture slip. The application of the results obtained in the article for the seismic design for the newly constructed buildings and structures and the given recommendations will provide the additional protection from fire and earthquake risks, reduction of their negative economic and environmental consequences.

Mean-field theory of a plastic network of integrate-and-fire neurons.

PubMed

Chen, Chun-Chung; Jasnow, David

2010-01-01

We consider a noise-driven network of integrate-and-fire neurons. The network evolves as result of the activities of the neurons following spike-timing-dependent plasticity rules. We apply a self-consistent mean-field theory to the system to obtain the mean activity level for the system as a function of the mean synaptic weight, which predicts a first-order transition and hysteresis between a noise-dominated regime and a regime of persistent neural activity. Assuming Poisson firing statistics for the neurons, the plasticity dynamics of a synapse under the influence of the mean-field environment can be mapped to the dynamics of an asymmetric random walk in synaptic-weight space. Using a master equation for small steps, we predict a narrow distribution of synaptic weights that scales with the square root of the plasticity rate for the stationary state of the system given plausible physiological parameter values describing neural transmission and plasticity. The dependence of the distribution on the synaptic weight of the mean-field environment allows us to determine the mean synaptic weight self-consistently. The effect of fluctuations in the total synaptic conductance and plasticity step sizes are also considered. Such fluctuations result in a smoothing of the first-order transition for low number of afferent synapses per neuron and a broadening of the synaptic-weight distribution, respectively.

Identification of the connections in biologically inspired neural networks

NASA Technical Reports Server (NTRS)

Demuth, H.; Leung, K.; Beale, M.; Hicklin, J.

1990-01-01

We developed an identification method to find the strength of the connections between neurons from their behavior in small biologically-inspired artificial neural networks. That is, given the network external inputs and the temporal firing pattern of the neurons, we can calculate a solution for the strengths of the connections between neurons and the initial neuron activations if a solution exists. The method determines directly if there is a solution to a particular neural network problem. No training of the network is required. It should be noted that this is a first pass at the solution of a difficult problem. The neuron and network models chosen are related to biology but do not contain all of its complexities, some of which we hope to add to the model in future work. A variety of new results have been obtained. First, the method has been tailored to produce connection weight matrix solutions for networks with important features of biological neural (bioneural) networks. Second, a computationally efficient method of finding a robust central solution has been developed. This later method also enables us to find the most consistent solution in the presence of noisy data. Prospects of applying our method to identify bioneural network connections are exciting because such connections are almost impossible to measure in the laboratory. Knowledge of such connections would facilitate an understanding of bioneural networks and would allow the construction of the electronic counterparts of bioneural networks on very large scale integrated (VLSI) circuits.

Establishment of a Human Neuronal Network Assessment System by Using a Human Neuron/Astrocyte Co-Culture Derived from Fetal Neural Stem/Progenitor Cells.

PubMed

Fukushima, Kazuyuki; Miura, Yuji; Sawada, Kohei; Yamazaki, Kazuto; Ito, Masashi

2016-01-01

Using human cell models mimicking the central nervous system (CNS) provides a better understanding of the human CNS, and it is a key strategy to improve success rates in CNS drug development. In the CNS, neurons function as networks in which astrocytes play important roles. Thus, an assessment system of neuronal network functions in a co-culture of human neurons and astrocytes has potential to accelerate CNS drug development. We previously demonstrated that human hippocampus-derived neural stem/progenitor cells (HIP-009 cells) were a novel tool to obtain human neurons and astrocytes in the same culture. In this study, we applied HIP-009 cells to a multielectrode array (MEA) system to detect neuronal signals as neuronal network functions. We observed spontaneous firings of HIP-009 neurons, and validated functional formation of neuronal networks pharmacologically. By using this assay system, we investigated effects of several reference compounds, including agonists and antagonists of glutamate and γ-aminobutyric acid receptors, and sodium, potassium, and calcium channels, on neuronal network functions using firing and burst numbers, and synchrony as readouts. These results indicate that the HIP-009/MEA assay system is applicable to the pharmacological assessment of drug candidates affecting synaptic functions for CNS drug development. © 2015 Society for Laboratory Automation and Screening.

Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces.

PubMed

Liu, Jianbo; Khalil, Hassan K; Oweiss, Karim G

2011-10-01

In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.

Computing with Neural Synchrony

PubMed Central

Brette, Romain

2012-01-01

Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties. PMID:22719243

Low-Dimensional Models of "Neuro-Glio-Vascular Unit" for Describing Neural Dynamics under Normal and Energy-Starved Conditions.

PubMed

Chhabria, Karishma; Chakravarthy, V Srinivasa

2016-01-01

The motivation of developing simple minimal models for neuro-glio-vascular (NGV) system arises from a recent modeling study elucidating the bidirectional information flow within the NGV system having 89 dynamic equations (1). While this was one of the first attempts at formulating a comprehensive model for neuro-glio-vascular system, it poses severe restrictions in scaling up to network levels. On the contrary, low--dimensional models are convenient devices in simulating large networks that also provide an intuitive understanding of the complex interactions occurring within the NGV system. The key idea underlying the proposed models is to describe the glio-vascular system as a lumped system, which takes neural firing rate as input and returns an "energy" variable (analogous to ATP) as output. To this end, we present two models: biophysical neuro-energy (Model 1 with five variables), comprising KATP channel activity governed by neuronal ATP dynamics, and the dynamic threshold (Model 2 with three variables), depicting the dependence of neural firing threshold on the ATP dynamics. Both the models show different firing regimes, such as continuous spiking, phasic, and tonic bursting depending on the ATP production coefficient, ɛp, and external current. We then demonstrate that in a network comprising such energy-dependent neuron units, ɛp could modulate the local field potential (LFP) frequency and amplitude. Interestingly, low-frequency LFP dominates under low ɛp conditions, which is thought to be reminiscent of seizure-like activity observed in epilepsy. The proposed "neuron-energy" unit may be implemented in building models of NGV networks to simulate data obtained from multimodal neuroimaging systems, such as functional near infrared spectroscopy coupled to electroencephalogram and functional magnetic resonance imaging coupled to electroencephalogram. Such models could also provide a theoretical basis for devising optimal neurorehabilitation strategies, such as non-invasive brain stimulation for stroke patients.

Efficient video-equipped fire detection approach for automatic fire alarm systems

NASA Astrophysics Data System (ADS)

Kang, Myeongsu; Tung, Truong Xuan; Kim, Jong-Myon

2013-01-01

This paper proposes an efficient four-stage approach that automatically detects fire using video capabilities. In the first stage, an approximate median method is used to detect video frame regions involving motion. In the second stage, a fuzzy c-means-based clustering algorithm is employed to extract candidate regions of fire from all of the movement-containing regions. In the third stage, a gray level co-occurrence matrix is used to extract texture parameters by tracking red-colored objects in the candidate regions. These texture features are, subsequently, used as inputs of a back-propagation neural network to distinguish between fire and nonfire. Experimental results indicate that the proposed four-stage approach outperforms other fire detection algorithms in terms of consistently increasing the accuracy of fire detection in both indoor and outdoor test videos.

Metastable neural dynamics mediates expectation

NASA Astrophysics Data System (ADS)

Mazzucato, Luca; La Camera, Giancarlo; Fontanini, Alfredo

Sensory stimuli are processed faster when their presentation is expected compared to when they come as a surprise. We previously showed that, in multiple single-unit recordings from alert rat gustatory cortex, taste stimuli can be decoded faster from neural activity if preceded by a stimulus-predicting cue. However, the specific computational process mediating this anticipatory neural activity is unknown. Here, we propose a biologically plausible model based on a recurrent network of spiking neurons with clustered architecture. In the absence of stimulation, the model neural activity unfolds through sequences of metastable states, each state being a population vector of firing rates. We modeled taste stimuli and cue (the same for all stimuli) as two inputs targeting subsets of excitatory neurons. As observed in experiment, stimuli evoked specific state sequences, characterized in terms of `coding states', i.e., states occurring significantly more often for a particular stimulus. When stimulus presentation is preceded by a cue, coding states show a faster and more reliable onset, and expected stimuli can be decoded more quickly than unexpected ones. This anticipatory effect is unrelated to changes of firing rates in stimulus-selective neurons and is absent in homogeneous balanced networks, suggesting that a clustered organization is necessary to mediate the expectation of relevant events. Our results demonstrate a novel mechanism for speeding up sensory coding in cortical circuits. NIDCD K25-DC013557 (LM); NIDCD R01-DC010389 (AF); NSF IIS-1161852 (GL).

1/f neural noise and electrophysiological indices of contextual prediction in aging.

PubMed

Dave, S; Brothers, T A; Swaab, T Y

2018-07-15

Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations. Copyright © 2018 Elsevier B.V. All rights reserved.

Robust Working Memory in an Asynchronously Spiking Neural Network Realized with Neuromorphic VLSI

PubMed Central

Giulioni, Massimiliano; Camilleri, Patrick; Mattia, Maurizio; Dante, Vittorio; Braun, Jochen; Del Giudice, Paolo

2011-01-01

We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory) of leaky integrate-and-fire (LIF) neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of “high” and “low”-firing activity. Depending on the overall excitability, transitions to the “high” state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the “high” state retains a “working memory” of a stimulus until well after its release. In the latter case, “high” states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated “corrupted” “high” states comprising neurons of both excitatory populations. Within a “basin of attraction,” the network dynamics “corrects” such states and re-establishes the prototypical “high” state. We conclude that, with effective theoretical guidance, full-fledged attractor dynamics can be realized with comparatively small populations of neuromorphic hardware neurons. PMID:22347151

Robust Working Memory in an Asynchronously Spiking Neural Network Realized with Neuromorphic VLSI.

PubMed

Giulioni, Massimiliano; Camilleri, Patrick; Mattia, Maurizio; Dante, Vittorio; Braun, Jochen; Del Giudice, Paolo

2011-01-01

We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory) of leaky integrate-and-fire (LIF) neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of "high" and "low"-firing activity. Depending on the overall excitability, transitions to the "high" state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the "high" state retains a "working memory" of a stimulus until well after its release. In the latter case, "high" states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated "corrupted" "high" states comprising neurons of both excitatory populations. Within a "basin of attraction," the network dynamics "corrects" such states and re-establishes the prototypical "high" state. We conclude that, with effective theoretical guidance, full-fledged attractor dynamics can be realized with comparatively small populations of neuromorphic hardware neurons.

Optimal sparse approximation with integrate and fire neurons.

PubMed

Shapero, Samuel; Zhu, Mengchen; Hasler, Jennifer; Rozell, Christopher

2014-08-01

Sparse approximation is a hypothesized coding strategy where a population of sensory neurons (e.g. V1) encodes a stimulus using as few active neurons as possible. We present the Spiking LCA (locally competitive algorithm), a rate encoded Spiking Neural Network (SNN) of integrate and fire neurons that calculate sparse approximations. The Spiking LCA is designed to be equivalent to the nonspiking LCA, an analog dynamical system that converges on a ℓ(1)-norm sparse approximations exponentially. We show that the firing rate of the Spiking LCA converges on the same solution as the analog LCA, with an error inversely proportional to the sampling time. We simulate in NEURON a network of 128 neuron pairs that encode 8 × 8 pixel image patches, demonstrating that the network converges to nearly optimal encodings within 20 ms of biological time. We also show that when using more biophysically realistic parameters in the neurons, the gain function encourages additional ℓ(0)-norm sparsity in the encoding, relative both to ideal neurons and digital solvers.

Modeling task-specific neuronal ensembles improves decoding of grasp

NASA Astrophysics Data System (ADS)

Smith, Ryan J.; Soares, Alcimar B.; Rouse, Adam G.; Schieber, Marc H.; Thakor, Nitish V.

2018-06-01

Objective. Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed. Approach. In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed. Main results. Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p  <  0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units. Significance. These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more reliable and accurate neural prosthesis.

Characteristic effects of stochastic oscillatory forcing on neural firing: analytical theory and comparison to paddlefish electroreceptor data.

PubMed

Bauermeister, Christoph; Schwalger, Tilo; Russell, David F; Neiman, Alexander B; Lindner, Benjamin

2013-01-01

Stochastic signals with pronounced oscillatory components are frequently encountered in neural systems. Input currents to a neuron in the form of stochastic oscillations could be of exogenous origin, e.g. sensory input or synaptic input from a network rhythm. They shape spike firing statistics in a characteristic way, which we explore theoretically in this report. We consider a perfect integrate-and-fire neuron that is stimulated by a constant base current (to drive regular spontaneous firing), along with Gaussian narrow-band noise (a simple example of stochastic oscillations), and a broadband noise. We derive expressions for the nth-order interval distribution, its variance, and the serial correlation coefficients of the interspike intervals (ISIs) and confirm these analytical results by computer simulations. The theory is then applied to experimental data from electroreceptors of paddlefish, which have two distinct types of internal noisy oscillators, one forcing the other. The theory provides an analytical description of their afferent spiking statistics during spontaneous firing, and replicates a pronounced dependence of ISI serial correlation coefficients on the relative frequency of the driving oscillations, and furthermore allows extraction of certain parameters of the intrinsic oscillators embedded in these electroreceptors.

Direct adaptive control of wind energy conversion systems using Gaussian networks.

PubMed

Mayosky, M A; Cancelo, I E

1999-01-01

Grid connected wind energy conversion systems (WECS) present interesting control demands, due to the intrinsic nonlinear characteristics of windmills and electric generators. In this paper a direct adaptive control strategy for WECS control is proposed. It is based on the combination of two control actions: a radial basis zfunction network-based adaptive controller, which drives the tracking error to zero with user specified dynamics, and a supervisory controller, based on crude bounds of the system's nonlinearities. The supervisory controller fires when the finite neural-network approximation properties cannot be guaranteed. The form of the supervisor control and the adaptation law for the neural controller are derived from a Lyapunov analysis of stability. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons.

PubMed

Yger, Pierre; El Boustani, Sami; Destexhe, Alain; Frégnac, Yves

2011-10-01

The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparsely-connected networks of conductance-based integrate-and-fire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the "macroscopic" properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussian-distributed local connectivity. We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. In addition, we examined the response of such networks to external input, and found that the correlation landscape can be modulated by the mean level of synchrony imposed by the external drive. This modulation was found again to be independent of the external connectivity profile. We conclude that first and second-order "mean-field" statistics of such networks do not depend on the details of the connectivity at a microscopic scale. This study is an encouraging step toward a mean-field description of topological neuronal networks.

A hybrid neural network model for noisy data regression.

PubMed

Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M

2004-04-01

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Automated Wildfire Detection Through Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

2005-01-01

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

Short-Range Temporal Interactions in Sleep; Hippocampal Spike Avalanches Support a Large Milieu of Sequential Activity Including Replay

PubMed Central

Mahoney, J. Matthew; Titiz, Ali S.; Hernan, Amanda E.; Scott, Rod C.

2016-01-01

Hippocampal neural systems consolidate multiple complex behaviors into memory. However, the temporal structure of neural firing supporting complex memory consolidation is unknown. Replay of hippocampal place cells during sleep supports the view that a simple repetitive behavior modifies sleep firing dynamics, but does not explain how multiple episodes could be integrated into associative networks for recollection during future cognition. Here we decode sequential firing structure within spike avalanches of all pyramidal cells recorded in sleeping rats after running in a circular track. We find that short sequences that combine into multiple long sequences capture the majority of the sequential structure during sleep, including replay of hippocampal place cells. The ensemble, however, is not optimized for maximally producing the behavior-enriched episode. Thus behavioral programming of sequential correlations occurs at the level of short-range interactions, not whole behavioral sequences and these short sequences are assembled into a large and complex milieu that could support complex memory consolidation. PMID:26866597

To Break or to Brake Neuronal Network Accelerated by Ammonium Ions?

PubMed Central

Dynnik, Vladimir V.; Kononov, Alexey V.; Sergeev, Alexander I.; Teplov, Iliya Y.; Tankanag, Arina V.; Zinchenko, Valery P.

2015-01-01

Purpose The aim of present study was to investigate the effects of ammonium ions on in vitro neuronal network activity and to search alternative methods of acute ammonia neurotoxicity prevention. Methods Rat hippocampal neuronal and astrocytes co-cultures in vitro, fluorescent microscopy and perforated patch clamp were used to monitor the changes in intracellular Ca2+- and membrane potential produced by ammonium ions and various modulators in the cells implicated in neural networks. Results Low concentrations of NH4Cl (0.1–4 mM) produce short temporal effects on network activity. Application of 5–8 mM NH4Cl: invariably transforms diverse network firing regimen to identical burst patterns, characterized by substantial neuronal membrane depolarization at plateau phase of potential and high-amplitude Ca2+-oscillations; raises frequency and average for period of oscillations Ca2+-level in all cells implicated in network; results in the appearance of group of «run out» cells with high intracellular Ca2+ and steadily diminished amplitudes of oscillations; increases astrocyte Ca2+-signalling, characterized by the appearance of groups of cells with increased intracellular Ca2+-level and/or chaotic Ca2+-oscillations. Accelerated network activity may be suppressed by the blockade of NMDA or AMPA/kainate-receptors or by overactivation of AMPA/kainite-receptors. Ammonia still activate neuronal firing in the presence of GABA(A) receptors antagonist bicuculline, indicating that «disinhibition phenomenon» is not implicated in the mechanisms of networks acceleration. Network activity may also be slowed down by glycine, agonists of metabotropic inhibitory receptors, betaine, L-carnitine, L-arginine, etc. Conclusions Obtained results demonstrate that ammonium ions accelerate neuronal networks firing, implicating ionotropic glutamate receptors, having preserved the activities of group of inhibitory ionotropic and metabotropic receptors. This may mean, that ammonia neurotoxicity might be prevented by the activation of various inhibitory receptors (i.e. by the reinforcement of negative feedback control), instead of application of various enzyme inhibitors and receptor antagonists (breaking of neural, metabolic and signaling systems). PMID:26217943

Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model

PubMed Central

Gigante, Guido; Deco, Gustavo; Marom, Shimon; Del Giudice, Paolo

2015-01-01

Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed ‘quasi-orbits’, which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network’s firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms. PMID:26558616

Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics

NASA Astrophysics Data System (ADS)

Chao, Zenas C.; Bakkum, Douglas J.; Potter, Steve M.

2007-09-01

Electrically interfaced cortical networks cultured in vitro can be used as a model for studying the network mechanisms of learning and memory. Lasting changes in functional connectivity have been difficult to detect with extracellular multi-electrode arrays using standard firing rate statistics. We used both simulated and living networks to compare the ability of various statistics to quantify functional plasticity at the network level. Using a simulated integrate-and-fire neural network, we compared five established statistical methods to one of our own design, called center of activity trajectory (CAT). CAT, which depicts dynamics of the location-weighted average of spatiotemporal patterns of action potentials across the physical space of the neuronal circuitry, was the most sensitive statistic for detecting tetanus-induced plasticity in both simulated and living networks. By reducing the dimensionality of multi-unit data while still including spatial information, CAT allows efficient real-time computation of spatiotemporal activity patterns. Thus, CAT will be useful for studies in vivo or in vitro in which the locations of recording sites on multi-electrode probes are important.

Information-geometric measures estimate neural interactions during oscillatory brain states

PubMed Central

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain. PMID:24605089

Information-geometric measures estimate neural interactions during oscillatory brain states.

PubMed

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

Implementing Signature Neural Networks with Spiking Neurons

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks. PMID:28066221

Implementing Signature Neural Networks with Spiking Neurons.

PubMed

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

PubMed Central

Rich, Scott; Booth, Victoria; Zochowski, Michal

2016-01-01

The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics. PMID:27812323

Optimization Methods for Spiking Neurons and Networks

PubMed Central

Russell, Alexander; Orchard, Garrick; Dong, Yi; Mihalaş, Ştefan; Niebur, Ernst; Tapson, Jonathan; Etienne-Cummings, Ralph

2011-01-01

Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron’s output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas–Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip. PMID:20959265

A model of metastable dynamics during ongoing and evoked cortical activity

NASA Astrophysics Data System (ADS)

La Camera, Giancarlo

The dynamics of simultaneously recorded spike trains in alert animals often evolve through temporal sequences of metastable states. Little is known about the network mechanisms responsible for the genesis of such sequences, or their potential role in neural coding. In the gustatory cortex of alert rates, state sequences can be observed also in the absence of overt sensory stimulation, and thus form the basis of the so-called `ongoing activity'. This activity is characterized by a partial degree of coordination among neurons, sharp transitions among states, and multi-stability of single neurons' firing rates. A recurrent spiking network model with clustered topology can account for both the spontaneous generation of state sequences and the (network-generated) multi-stability. In the model, each network state results from the activation of specific neural clusters with potentiated intra-cluster connections. A mean field solution of the model shows a large number of stable states, each characterized by a subset of simultaneously active clusters. The firing rate in each cluster during ongoing activity depends on the number of active clusters, so that the same neuron can have different firing rates depending on the state of the network. Because of dense intra-cluster connectivity and recurrent inhibition, in finite networks the stable states lose stability due to finite size effects. Simulations of the dynamics show that the model ensemble activity continuously hops among the different states, reproducing the ongoing dynamics observed in the data. Moreover, when probed with external stimuli, the model correctly predicts the quenching of single neuron multi-stability into bi-stability, the reduction of dimensionality of the population activity, the reduction of trial-to-trial variability, and a potential role for metastable states in the anticipation of expected events. Altogether, these results provide a unified mechanistic model of ongoing and evoked cortical dynamics. NSF IIS-1161852, NIDCD K25-DC013557, NIDCD R01-DC010389.

The mechanics of state dependent neural correlations

PubMed Central

Doiron, Brent; Litwin-Kumar, Ashok; Rosenbaum, Robert; Ocker, Gabriel K.; Josić, Krešimir

2016-01-01

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of the population activity are the trial-to-trial correlated fluctuations of the spike train outputs of recorded neuron pairs. Like the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the network mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate biophysical mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations, and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways, and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high dimensional neural data. PMID:26906505

Improved Autoassociative Neural Networks

NASA Technical Reports Server (NTRS)

Hand, Charles

2003-01-01

Improved autoassociative neural networks, denoted nexi, have been proposed for use in controlling autonomous robots, including mobile exploratory robots of the biomorphic type. In comparison with conventional autoassociative neural networks, nexi would be more complex but more capable in that they could be trained to do more complex tasks. A nexus would use bit weights and simple arithmetic in a manner that would enable training and operation without a central processing unit, programs, weight registers, or large amounts of memory. Only a relatively small amount of memory (to hold the bit weights) and a simple logic application- specific integrated circuit would be needed. A description of autoassociative neural networks is prerequisite to a meaningful description of a nexus. An autoassociative network is a set of neurons that are completely connected in the sense that each neuron receives input from, and sends output to, all the other neurons. (In some instantiations, a neuron could also send output back to its own input terminal.) The state of a neuron is completely determined by the inner product of its inputs with weights associated with its input channel. Setting the weights sets the behavior of the network. The neurons of an autoassociative network are usually regarded as comprising a row or vector. Time is a quantized phenomenon for most autoassociative networks in the sense that time proceeds in discrete steps. At each time step, the row of neurons forms a pattern: some neurons are firing, some are not. Hence, the current state of an autoassociative network can be described with a single binary vector. As time goes by, the network changes the vector. Autoassociative networks move vectors over hyperspace landscapes of possibilities.

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Astrophysics Data System (ADS)

Guo, T. H.; Musgrave, J.

1992-11-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data.

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Musgrave, J.

1992-01-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data.

Oscillations contribute to memory consolidation by changing criticality and stability in the brain

NASA Astrophysics Data System (ADS)

Wu, Jiaxing; Skilling, Quinton; Ognjanovski, Nicolette; Aton, Sara; Zochowski, Michal

Oscillations are a universal feature of every level of brain dynamics and have been shown to contribute to many brain functions. To investigate the fundamental mechanism underpinning oscillatory activity, the properties of heterogeneous networks are compared in situations with and without oscillations. Our results show that both network criticality and stability are changed in the presence of oscillations. Criticality describes the network state of neuronal avalanche, a cascade of bursts of action potential firing in neural network. Stability measures how stable the spike timing relationship between neuron pairs is over time. Using a detailed spiking model, we found that the branching parameter σ changes relative to oscillation and structural network properties, corresponding to transmission among different critical states. Also, analysis of functional network structures shows that the oscillation helps to stabilize neuronal representation of memory. Further, quantitatively similar results are observed in biological data recorded in vivo. In summary, we have observed that, by regulating the neuronal firing pattern, oscillations affect both criticality and stability properties of the network, and thus contribute to memory formation.

On dynamics of integrate-and-fire neural networks with conductance based synapses.

PubMed

Cessac, Bruno; Viéville, Thierry

2008-01-01

We present a mathematical analysis of networks with integrate-and-fire (IF) neurons with conductance based synapses. Taking into account the realistic fact that the spike time is only known within some finite precision, we propose a model where spikes are effective at times multiple of a characteristic time scale delta, where delta can be arbitrary small (in particular, well beyond the numerical precision). We make a complete mathematical characterization of the model-dynamics and obtain the following results. The asymptotic dynamics is composed by finitely many stable periodic orbits, whose number and period can be arbitrary large and can diverge in a region of the synaptic weights space, traditionally called the "edge of chaos", a notion mathematically well defined in the present paper. Furthermore, except at the edge of chaos, there is a one-to-one correspondence between the membrane potential trajectories and the raster plot. This shows that the neural code is entirely "in the spikes" in this case. As a key tool, we introduce an order parameter, easy to compute numerically, and closely related to a natural notion of entropy, providing a relevant characterization of the computational capabilities of the network. This allows us to compare the computational capabilities of leaky and IF models and conductance based models. The present study considers networks with constant input, and without time-dependent plasticity, but the framework has been designed for both extensions.

Anisotropic connectivity implements motion-based prediction in a spiking neural network.

PubMed

Kaplan, Bernhard A; Lansner, Anders; Masson, Guillaume S; Perrinet, Laurent U

2013-01-01

Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may implement predictive coding and what function their connectivity may have. We present a network model of conductance-based integrate-and-fire neurons inspired by the architecture of retinotopic cortical areas that assumes predictive coding is implemented through network connectivity, namely in the connection delays and in selectiveness for the tuning properties of source and target cells. We show that the applied connection pattern leads to motion-based prediction in an experiment tracking a moving dot. In contrast to our proposed model, a network with random or isotropic connectivity fails to predict the path when the moving dot disappears. Furthermore, we show that a simple linear decoding approach is sufficient to transform neuronal spiking activity into a probabilistic estimate for reading out the target trajectory.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

PubMed

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

2018-02-01

Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

On the origin of reproducible sequential activity in neural circuits

NASA Astrophysics Data System (ADS)

Afraimovich, V. S.; Zhigulin, V. P.; Rabinovich, M. I.

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

On the origin of reproducible sequential activity in neural circuits.

PubMed

Afraimovich, V S; Zhigulin, V P; Rabinovich, M I

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

Neural assembly computing.

PubMed

Ranhel, João

2012-06-01

Spiking neurons can realize several computational operations when firing cooperatively. This is a prevalent notion, although the mechanisms are not yet understood. A way by which neural assemblies compute is proposed in this paper. It is shown how neural coalitions represent things (and world states), memorize them, and control their hierarchical relations in order to perform algorithms. It is described how neural groups perform statistic logic functions as they form assemblies. Neural coalitions can reverberate, becoming bistable loops. Such bistable neural assemblies become short- or long-term memories that represent the event that triggers them. In addition, assemblies can branch and dismantle other neural groups generating new events that trigger other coalitions. Hence, such capabilities and the interaction among assemblies allow neural networks to create and control hierarchical cascades of causal activities, giving rise to parallel algorithms. Computing and algorithms are used here as in a nonstandard computation approach. In this sense, neural assembly computing (NAC) can be seen as a new class of spiking neural network machines. NAC can explain the following points: 1) how neuron groups represent things and states; 2) how they retain binary states in memories that do not require any plasticity mechanism; and 3) how branching, disbanding, and interaction among assemblies may result in algorithms and behavioral responses. Simulations were carried out and the results are in agreement with the hypothesis presented. A MATLAB code is available as a supplementary material.

An Investigation on the Role of Spike Latency in an Artificial Olfactory System

PubMed Central

Martinelli, Eugenio; Polese, Davide; Dini, Francesca; Paolesse, Roberto; Filippini, Daniel; Lundström, Ingemar; Di Natale, Corrado

2011-01-01

Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time. PMID:22194721

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

NASA Astrophysics Data System (ADS)

Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

2013-05-01

The collective dynamics of excitatory pulse coupled neurons with spike-timing dependent plasticity is studied. The introduction of spike-timing dependent plasticity induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.

Temporal relation between neural activity and neurite pruning on a numerical model and a microchannel device with micro electrode array.

PubMed

Kondo, Yohei; Yada, Yuichiro; Haga, Tatsuya; Takayama, Yuzo; Isomura, Takuya; Jimbo, Yasuhiko; Fukayama, Osamu; Hoshino, Takayuki; Mabuchi, Kunihiko

2017-04-29

Synapse elimination and neurite pruning are essential processes for the formation of neuronal circuits. These regressive events depend on neural activity and occur in the early postnatal days known as the critical period, but what makes this temporal specificity is not well understood. One possibility is that the neural activities during the developmentally regulated shift of action of GABA inhibitory transmission lead to the critical period. Moreover, it has been reported that the shifting action of the inhibitory transmission on immature neurons overlaps with synapse elimination and neurite pruning and that increased inhibitory transmission by drug treatment could induce temporal shift of the critical period. However, the relationship among these phenomena remains unclear because it is difficult to experimentally show how the developmental shift of inhibitory transmission influences neural activities and whether the activities promote synapse elimination and neurite pruning. In this study, we modeled synapse elimination in neuronal circuits using the modified Izhikevich's model with functional shifting of GABAergic transmission. The simulation results show that synaptic pruning within a specified period like the critical period is spontaneously generated as a function of the developmentally shifting inhibitory transmission and that the specific firing rate and increasing synchronization of neural circuits are seen at the initial stage of the critical period. This temporal relationship was experimentally supported by an in vitro primary culture of rat cortical neurons in a microchannel on a multi-electrode array (MEA). The firing rate decreased remarkably between the 18-25 days in vitro (DIV), and following these changes in the firing rate, the neurite density was slightly reduced. Our simulation and experimental results suggest that decreasing neural activity due to developing inhibitory synaptic transmission could induce synapse elimination and neurite pruning at particular time such as the critical period. Additionally, these findings indicate that we can estimate the maturity level of inhibitory transmission and the critical period by measuring the firing rate and the degree of synchronization in engineered neural networks. Copyright © 2017 Elsevier Inc. All rights reserved.

Sensory Regulation of Network Components Underlying Ciliary Locomotion in Hermissenda

PubMed Central

Crow, Terry; Tian, Lian-Ming

2008-01-01

Ciliary locomotion in the nudibranch mollusk Hermissenda is modulated by the visual and graviceptive systems. Components of the neural network mediating ciliary locomotion have been identified including aggregates of polysensory interneurons that receive monosynaptic input from identified photoreceptors and efferent neurons that activate cilia. Illumination produces an inhibition of type Ii (off-cell) spike activity, excitation of type Ie (on-cell) spike activity, decreased spike activity in type IIIi inhibitory interneurons, and increased spike activity of ciliary efferent neurons. Here we show that pairs of type Ii interneurons and pairs of type Ie interneurons are electrically coupled. Neither electrical coupling or synaptic connections were observed between Ie and Ii interneurons. Coupling is effective in synchronizing dark-adapted spontaneous firing between pairs of Ie and pairs of Ii interneurons. Out-of-phase burst activity, occasionally observed in dark-adapted and light-adapted pairs of Ie and Ii interneurons, suggests that they receive synaptic input from a common presynaptic source or sources. Rhythmic activity is typically not a characteristic of dark-adapted, light-adapted, or light-evoked firing of type I interneurons. However, burst activity in Ie and Ii interneurons may be elicited by electrical stimulation of pedal nerves or generated at the offset of light. Our results indicate that type I interneurons can support the generation of both rhythmic activity and changes in tonic firing depending on sensory input. This suggests that the neural network supporting ciliary locomotion may be multifunctional. However, consistent with the nonmuscular and nonrhythmic characteristics of visually modulated ciliary locomotion, type I interneurons exhibit changes in tonic activity evoked by illumination. PMID:18768639

Neural mechanisms of movement planning: motor cortex and beyond.

PubMed

Svoboda, Karel; Li, Nuo

2018-04-01

Neurons in motor cortex and connected brain regions fire in anticipation of specific movements, long before movement occurs. This neural activity reflects internal processes by which the brain plans and executes volitional movements. The study of motor planning offers an opportunity to understand how the structure and dynamics of neural circuits support persistent internal states and how these states influence behavior. Recent advances in large-scale neural recordings are beginning to decipher the relationship of the dynamics of populations of neurons during motor planning and movements. New behavioral tasks in rodents, together with quantified perturbations, link dynamics in specific nodes of neural circuits to behavior. These studies reveal a neural network distributed across multiple brain regions that collectively supports motor planning. We review recent advances and highlight areas where further work is needed to achieve a deeper understanding of the mechanisms underlying motor planning and related cognitive processes. Copyright © 2017. Published by Elsevier Ltd.

A balanced memory network.

PubMed

Roudi, Yasser; Latham, Peter E

2007-09-01

A fundamental problem in neuroscience is understanding how working memory--the ability to store information at intermediate timescales, like tens of seconds--is implemented in realistic neuronal networks. The most likely candidate mechanism is the attractor network, and a great deal of effort has gone toward investigating it theoretically. Yet, despite almost a quarter century of intense work, attractor networks are not fully understood. In particular, there are still two unanswered questions. First, how is it that attractor networks exhibit irregular firing, as is observed experimentally during working memory tasks? And second, how many memories can be stored under biologically realistic conditions? Here we answer both questions by studying an attractor neural network in which inhibition and excitation balance each other. Using mean-field analysis, we derive a three-variable description of attractor networks. From this description it follows that irregular firing can exist only if the number of neurons involved in a memory is large. The same mean-field analysis also shows that the number of memories that can be stored in a network scales with the number of excitatory connections, a result that has been suggested for simple models but never shown for realistic ones. Both of these predictions are verified using simulations with large networks of spiking neurons.

Neural networks for learning and prediction with applications to remote sensing and speech perception

NASA Astrophysics Data System (ADS)

Gjaja, Marin N.

1997-11-01

Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An unsupervised neural network model is proposed that embodies two principal hypotheses supported by experimental data--that sensory experience guides language-specific development of an auditory neural map and that a population vector can predict psychological phenomena based on map cell activities. Model simulations show how a nonuniform distribution of map cell firing preferences can develop from language-specific input and give rise to the magnet effect.

Stimulus dependence of local field potential spectra: experiment versus theory.

PubMed

Barbieri, Francesca; Mazzoni, Alberto; Logothetis, Nikos K; Panzeri, Stefano; Brunel, Nicolas

2014-10-29

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings. Copyright © 2014 the authors 0270-6474/14/3414589-17$15.00/0.

Physical and mathematical modeling of pollutant emissions when burning peat

NASA Astrophysics Data System (ADS)

Vasilyev, A.; Lozhkin, V.; Tarkhov, D.; Lozhkina, O.; Timofeev, V.

2017-11-01

The article presents an original neural network model of CO dispersion around the experimentally simulated peat fire. It is a self-learning model considering both the measured CO concentrations in the smoke cloud and the refined coefficients of the main equation. The method is recommended for the development of air quality control and forecasting systems.

A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia

PubMed Central

Faghihi, Faramarz; Moustafa, Ahmed A.

2015-01-01

Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed. PMID:25859189

Synchrony-induced modes of oscillation of a neural field model

NASA Astrophysics Data System (ADS)

Esnaola-Acebes, Jose M.; Roxin, Alex; Avitabile, Daniele; Montbrió, Ernest

2017-11-01

We investigate the modes of oscillation of heterogeneous ring networks of quadratic integrate-and-fire (QIF) neurons with nonlocal, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific temporal frequency, analogously to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially homogeneous state and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states and are maintained away from onset.

Synchrony-induced modes of oscillation of a neural field model.

PubMed

Esnaola-Acebes, Jose M; Roxin, Alex; Avitabile, Daniele; Montbrió, Ernest

2017-11-01

We investigate the modes of oscillation of heterogeneous ring networks of quadratic integrate-and-fire (QIF) neurons with nonlocal, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific temporal frequency, analogously to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially homogeneous state and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states and are maintained away from onset.

Automated Propulsion Data Screening demonstration system

NASA Technical Reports Server (NTRS)

Hoyt, W. Andes; Choate, Timothy D.; Whitehead, Bruce A.

1995-01-01

A fully-instrumented firing of a propulsion system typically generates a very large quantity of data. In the case of the Space Shuttle Main Engine (SSME), data analysis from ground tests and flights is currently a labor-intensive process. Human experts spend a great deal of time examining the large volume of sensor data generated by each engine firing. These experts look for any anomalies in the data which might indicate engine conditions warranting further investigation. The contract effort was to develop a 'first-cut' screening system for application to SSME engine firings that would identify the relatively small volume of data which is unusual or anomalous in some way. With such a system, limited and expensive human resources could focus on this small volume of unusual data for thorough analysis. The overall project objective was to develop a fully operational Automated Propulsion Data Screening (APDS) system with the capability of detecting significant trends and anomalies in transient and steady-state data. However, the effort limited screening of transient data to ground test data for throttle-down cases typical of the 3-g acceleration, and for engine throttling required to reach the maximum dynamic pressure limits imposed on the Space Shuttle. This APDS is based on neural networks designed to detect anomalies in propulsion system data that are not part of the data used for neural network training. The delivered system allows engineers to build their own screening sets for application to completed or planned firings of the SSME. ERC developers also built some generic screening sets that NASA engineers could apply immediately to their data analysis efforts.

Sequence memory based on coherent spin-interaction neural networks.

PubMed

Xia, Min; Wong, W K; Wang, Zhijie

2014-12-01

Sequence information processing, for instance, the sequence memory, plays an important role on many functions of brain. In the workings of the human brain, the steady-state period is alterable. However, in the existing sequence memory models using heteroassociations, the steady-state period cannot be changed in the sequence recall. In this work, a novel neural network model for sequence memory with controllable steady-state period based on coherent spininteraction is proposed. In the proposed model, neurons fire collectively in a phase-coherent manner, which lets a neuron group respond differently to different patterns and also lets different neuron groups respond differently to one pattern. The simulation results demonstrating the performance of the sequence memory are presented. By introducing a new coherent spin-interaction sequence memory model, the steady-state period can be controlled by dimension parameters and the overlap between the input pattern and the stored patterns. The sequence storage capacity is enlarged by coherent spin interaction compared with the existing sequence memory models. Furthermore, the sequence storage capacity has an exponential relationship to the dimension of the neural network.

Differential Activation of Fast-Spiking and Regular-Firing Neuron Populations During Movement and Reward in the Dorsal Medial Frontal Cortex

PubMed Central

Insel, Nathan; Barnes, Carol A.

2015-01-01

The medial prefrontal cortex is thought to be important for guiding behavior according to an animal's expectations. Efforts to decode the region have focused not only on the question of what information it computes, but also how distinct circuit components become engaged during behavior. We find that the activity of regular-firing, putative projection neurons contains rich information about behavioral context and firing fields cluster around reward sites, while activity among putative inhibitory and fast-spiking neurons is most associated with movement and accompanying sensory stimulation. These dissociations were observed even between adjacent neurons with apparently reciprocal, inhibitory–excitatory connections. A smaller population of projection neurons with burst-firing patterns did not show clustered firing fields around rewards; these neurons, although heterogeneous, were generally less selective for behavioral context than regular-firing cells. The data suggest a network that tracks an animal's behavioral situation while, at the same time, regulating excitation levels to emphasize high valued positions. In this scenario, the function of fast-spiking inhibitory neurons is to constrain network output relative to incoming sensory flow. This scheme could serve as a bridge between abstract sensorimotor information and single-dimensional codes for value, providing a neural framework to generate expectations from behavioral state. PMID:24700585

Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series.

PubMed

Zeldenrust, Fleur; de Knecht, Sicco; Wadman, Wytse J; Denève, Sophie; Gutkin, Boris

2017-01-01

Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., "the neural code") lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new ( in vitro ) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing "sensory stimulus": the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the "Bayesian neuron" (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant.

Crew exploration vehicle (CEV) attitude control using a neural-immunology/memory network

NASA Astrophysics Data System (ADS)

Weng, Liguo; Xia, Min; Wang, Wei; Liu, Qingshan

2015-01-01

This paper addresses the problem of the crew exploration vehicle (CEV) attitude control. CEVs are NASA's next-generation human spaceflight vehicles, and they use reaction control system (RCS) jet engines for attitude adjustment, which calls for control algorithms for firing the small propulsion engines mounted on vehicles. In this work, the resultant CEV dynamics combines both actuation and attitude dynamics. Therefore, it is highly nonlinear and even coupled with significant uncertainties. To cope with this situation, a neural-immunology/memory network is proposed. It is inspired by the human memory and immune systems. The control network does not rely on precise system dynamics information. Furthermore, the overall control scheme has a simple structure and demands much less computation as compared with most existing methods, making it attractive for real-time implementation. The effectiveness of this approach is also verified via simulation.

Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device.

PubMed

McKinstry, Jeffrey L; Edelman, Gerald M

2013-01-01

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.

PubMed

Higgins, Irina; Stringer, Simon; Schnupp, Jan

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

PubMed Central

Stringer, Simon

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable. PMID:28797034

An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation

PubMed Central

Chen, Chang Hao; McCullagh, Elizabeth A.; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C.

2017-01-01

The ability to record and to control action potential firing in neuronal circuits of the brain is critical to understand how the brain functions on the cellular and network levels. Recent development of optogenetic proteins allows direct stimulation or inhibition of action potential firing of neurons upon optical illumination. In this paper, we combined a low-noise and high input impedance (or low input capacitance) neural recording amplifier, and a high current laser/LED driver in a monolithic integrated circuit (IC) for simultaneous neural recording and optogenetic neural control. The low input capacitance of the amplifier (9.7 pF) was achieved through adding a dedicated unity gain input stage optimized for high impedance metal electrodes. The input referred noise of the amplifier was measured to be 4.57 µVrms, which is lower than the estimated thermal noise of the metal electrode. Thus, action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of ~6.6. The LED/laser current driver delivers a maximum current of 330 mA to generate adequate light for optogenetic control. We experimentally tested the functionality of the IC with an anesthetized Mongolian gerbil and recorded auditory stimulated action potentials from the inferior colliculus. Furthermore, we showed that spontaneous firing of 5th (trigeminal) nerve fibers was inhibited using the optogenetic protein Halorhodopsin. A noise model was also derived including the equivalent electronic components of the metal electrode and the high current driver to guide the design. PMID:28221990

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

PubMed Central

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot”) suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain. PMID:22046178

Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

PubMed

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain.

Gain Modulation by an Urgency Signal Controls the Speed–Accuracy Trade-Off in a Network Model of a Cortical Decision Circuit

PubMed Central

Standage, Dominic; You, Hongzhi; Wang, Da-Hui; Dorris, Michael C.

2011-01-01

The speed–accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time. PMID:21415911

Gain modulation by an urgency signal controls the speed-accuracy trade-off in a network model of a cortical decision circuit.

PubMed

Standage, Dominic; You, Hongzhi; Wang, Da-Hui; Dorris, Michael C

2011-01-01

The speed-accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time.

Expert networks in CLIPS

NASA Technical Reports Server (NTRS)

Hruska, S. I.; Dalke, A.; Ferguson, J. J.; Lacher, R. C.

1991-01-01

Rule-based expert systems may be structurally and functionally mapped onto a special class of neural networks called expert networks. This mapping lends itself to adaptation of connectionist learning strategies for the expert networks. A parsing algorithm to translate C Language Integrated Production System (CLIPS) rules into a network of interconnected assertion and operation nodes has been developed. The translation of CLIPS rules to an expert network and back again is illustrated. Measures of uncertainty similar to those rules in MYCIN-like systems are introduced into the CLIPS system and techniques for combining and hiring nodes in the network based on rule-firing with these certainty factors in the expert system are presented. Several learning algorithms are under study which automate the process of attaching certainty factors to rules.

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks

PubMed Central

Naveros, Francisco; Garrido, Jesus A.; Carrillo, Richard R.; Ros, Eduardo; Luque, Niceto R.

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under increasing levels of neural complexity. PMID:28223930

Inferring multi-scale neural mechanisms with brain network modelling

PubMed Central

Schirner, Michael; McIntosh, Anthony Randal; Jirsa, Viktor; Deco, Gustavo

2018-01-01

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies. PMID:29308767

Neural networks with excitatory and inhibitory components: Direct and inverse problems by a mean-field approach

NASA Astrophysics Data System (ADS)

di Volo, Matteo; Burioni, Raffaella; Casartelli, Mario; Livi, Roberto; Vezzani, Alessandro

2016-01-01

We study the dynamics of networks with inhibitory and excitatory leak-integrate-and-fire neurons with short-term synaptic plasticity in the presence of depressive and facilitating mechanisms. The dynamics is analyzed by a heterogeneous mean-field approximation, which allows us to keep track of the effects of structural disorder in the network. We describe the complex behavior of different classes of excitatory and inhibitory components, which give rise to a rich dynamical phase diagram as a function of the fraction of inhibitory neurons. Using the same mean-field approach, we study and solve a global inverse problem: reconstructing the degree probability distributions of the inhibitory and excitatory components and the fraction of inhibitory neurons from the knowledge of the average synaptic activity field. This approach unveils new perspectives on the numerical study of neural network dynamics and the possibility of using these models as a test bed for the analysis of experimental data.

Neural Network for Positioning Space Station Solar Arrays

NASA Technical Reports Server (NTRS)

Graham, Ronald E.; Lin, Paul P.

1994-01-01

As a shuttle approaches the Space Station Freedom for a rendezvous, the shuttle's reaction control jet firings pose a risk of excessive plume impingement loads on Freedom solar arrays. The current solution to this problem, in which the arrays are locked in a feathered position prior to the approach, may be neither accurate nor robust, and is also expensive. An alternative solution is proposed here: the active control of Freedom's beta gimbals during the approach, positioning the arrays dynamically in such a way that they remain feathered relative to the shuttle jet most likely to cause an impingement load. An artificial neural network is proposed as a means of determining the gimbal angles that would drive plume angle of attack to zero. Such a network would be both accurate and robust, and could be less expensive to implement than the current solution. A network was trained via backpropagation, and results, which compare favorably to the current solution as well as to some other alternatives, are presented. Other training options are currently being evaluated.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

NASA Astrophysics Data System (ADS)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

2018-02-01

Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

PubMed

Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

2017-04-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

A Physiological Neural Controller of a Muscle Fiber Oculomotor Plant in Horizontal Monkey Saccades

PubMed Central

Enderle, John D.

2014-01-01

A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude. PMID:24944832

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

PubMed

Mazzoni, Alberto; Lindén, Henrik; Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T

2015-12-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

PubMed Central

Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T.

2015-01-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo. PMID:26657024

Sunspot prediction using neural networks

NASA Technical Reports Server (NTRS)

Villarreal, James; Baffes, Paul

1990-01-01

The earliest systematic observance of sunspot activity is known to have been discovered by the Chinese in 1382 during the Ming Dynasty (1368 to 1644) when spots on the sun were noticed by looking at the sun through thick, forest fire smoke. Not until after the 18th century did sunspot levels become more than a source of wonderment and curiosity. Since 1834 reliable sunspot data has been collected by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Naval Observatory. Recently, considerable effort has been placed upon the study of the effects of sunspots on the ecosystem and the space environment. The efforts of the Artificial Intelligence Section of the Mission Planning and Analysis Division of the Johnson Space Center involving the prediction of sunspot activity using neural network technologies are described.

Evoking prescribed spike times in stochastic neurons

NASA Astrophysics Data System (ADS)

Doose, Jens; Lindner, Benjamin

2017-09-01

Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

Comparisons of Neuronal and Excitatory Network Properties between the Rat Brainstem Nuclei that Participate in Vertical and Horizontal Gaze Holding

PubMed Central

Sugimura, Taketoshi; Yanagawa, Yuchio

2017-01-01

Gaze holding is primarily controlled by neural structures including the prepositus hypoglossi nucleus (PHN) for horizontal gaze and the interstitial nucleus of Cajal (INC) for vertical and torsional gaze. In contrast to the accumulating findings of the PHN, there is no report regarding the membrane properties of INC neurons or the local networks in the INC. In this study, to verify whether the neural structure of the INC is similar to that of the PHN, we investigated the neuronal and network properties of the INC using whole-cell recordings in rat brainstem slices. Three types of afterhyperpolarization (AHP) profiles and five firing patterns observed in PHN neurons were also observed in INC neurons. However, the overall distributions based on the AHP profile and the firing patterns of INC neurons were different from those of PHN neurons. The application of burst stimulation to a nearby site of a recorded INC neuron induced an increase in the frequency of spontaneous EPSCs. The duration of the increased EPSC frequency of INC neurons was not significantly different from that of PHN neurons. The percent of duration reduction induced by a Ca2+-permeable AMPA (CP-AMPA) receptor antagonist was significantly smaller in the INC than in the PHN. These findings suggest that local excitatory networks that activate sustained EPSC responses also exist in the INC, but their activation mechanisms including the contribution of CP-AMPA receptors differ between the INC and the PHN. PMID:28966973

Neural network for intelligent query of an FBI forensic database

NASA Astrophysics Data System (ADS)

Uvanni, Lee A.; Rainey, Timothy G.; Balasubramanian, Uma; Brettle, Dean W.; Weingard, Fred; Sibert, Robert W.; Birnbaum, Eric

1997-02-01

Examiner is an automated fired cartridge case identification system utilizing a dual-use neural network pattern recognition technology, called the statistical-multiple object detection and location system (S-MODALS) developed by Booz(DOT)Allen & Hamilton, Inc. in conjunction with Rome Laboratory. S-MODALS was originally designed for automatic target recognition (ATR) of tactical and strategic military targets using multisensor fusion [electro-optical (EO), infrared (IR), and synthetic aperture radar (SAR)] sensors. Since S-MODALS is a learning system readily adaptable to problem domains other than automatic target recognition, the pattern matching problem of microscopic marks for firearms evidence was analyzed using S-MODALS. The physics; phenomenology; discrimination and search strategies; robustness requirements; error level and confidence level propagation that apply to the pattern matching problem of military targets were found to be applicable to the ballistic domain as well. The Examiner system uses S-MODALS to rank a set of queried cartridge case images from the most similar to the least similar image in reference to an investigative fired cartridge case image. The paper presents three independent tests and evaluation studies of the Examiner system utilizing the S-MODALS technology for the Federal Bureau of Investigation.

Autoassociative memory retrieval and spontaneous activity bumps in small-world networks of integrate-and-fire neurons.

PubMed

Anishchenko, Anastasia; Treves, Alessandro

2006-10-01

The metric structure of synaptic connections is obviously an important factor in shaping the properties of neural networks, in particular the capacity to retrieve memories, with which are endowed autoassociative nets operating via attractor dynamics. Qualitatively, some real networks in the brain could be characterized as 'small worlds', in the sense that the structure of their connections is intermediate between the extremes of an orderly geometric arrangement and of a geometry-independent random mesh. Small worlds can be defined more precisely in terms of their mean path length and clustering coefficient; but is such a precise description useful for a better understanding of how the type of connectivity affects memory retrieval? We have simulated an autoassociative memory network of integrate-and-fire units, positioned on a ring, with the network connectivity varied parametrically between ordered and random. We find that the network retrieves previously stored memory patterns when the connectivity is close to random, and displays the characteristic behavior of ordered nets (localized 'bumps' of activity) when the connectivity is close to ordered. Recent analytical work shows that these two behaviors can coexist in a network of simple threshold-linear units, leading to localized retrieval states. We find that they tend to be mutually exclusive behaviors, however, with our integrate-and-fire units. Moreover, the transition between the two occurs for values of the connectivity parameter which are not simply related to the notion of small worlds.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation. Copyright © 2015 the authors 0270-6474/15/3510112-23$15.00/0.

Emergent Oscillations in Networks of Stochastic Spiking Neurons

PubMed Central

van Drongelen, Wim; Cowan, Jack D.

2011-01-01

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework. PMID:21573105

Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity.

PubMed

Dummer, Benjamin; Wieland, Stefan; Lindner, Benjamin

2014-01-01

A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i) a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii) a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, 2000) and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide an excellent approximations to the autocorrelation of spike trains in the recurrent network.

Super-linear Precision in Simple Neural Population Codes

NASA Astrophysics Data System (ADS)

Schwab, David; Fiete, Ila

2015-03-01

A widely used tool for quantifying the precision with which a population of noisy sensory neurons encodes the value of an external stimulus is the Fisher Information (FI). Maximizing the FI is also a commonly used objective for constructing optimal neural codes. The primary utility and importance of the FI arises because it gives, through the Cramer-Rao bound, the smallest mean-squared error achievable by any unbiased stimulus estimator. However, it is well-known that when neural firing is sparse, optimizing the FI can result in codes that perform very poorly when considering the resulting mean-squared error, a measure with direct biological relevance. Here we construct optimal population codes by minimizing mean-squared error directly and study the scaling properties of the resulting network, focusing on the optimal tuning curve width. We then extend our results to continuous attractor networks that maintain short-term memory of external stimuli in their dynamics. Here we find similar scaling properties in the structure of the interactions that minimize diffusive information loss.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

PubMed

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

PubMed Central

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A.; Navas, Adrian; Villacorta-Atienza, Jose A.

2015-01-01

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh–Rose neurons. PMID:26648863

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

PubMed

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

2015-01-01

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

The Generation of Antiphase Oscillations and Synchrony by a Rebound-Based Vertebrate Central Pattern Generator

PubMed Central

Merrison-Hort, Robert; Zhang, Hong-Yan; Borisyuk, Roman

2014-01-01

Many neural circuits are capable of generating multiple stereotyped outputs after different sensory inputs or neuromodulation. We have previously identified the central pattern generator (CPG) for Xenopus tadpole swimming that involves antiphase oscillations of activity between the left and right sides. Here we analyze the cellular basis for spontaneous left–right motor synchrony characterized by simultaneous bursting on both sides at twice the swimming frequency. Spontaneous synchrony bouts are rare in most tadpoles, and they instantly emerge from and switch back to swimming, most frequently within the first second after skin stimulation. Analyses show that only neurons that are active during swimming fire action potentials in synchrony, suggesting both output patterns derive from the same neural circuit. The firing of excitatory descending interneurons (dINs) leads that of other types of neurons in synchrony as it does in swimming. During synchrony, the time window between phasic excitation and inhibition is 7.9 ± 1 ms, shorter than that in swimming (41 ± 2.3 ms). The occasional, extra midcycle firing of dINs during swimming may initiate synchrony, and mismatches of timing in the left and right activity can switch synchrony back to swimming. Computer modeling supports these findings by showing that the same neural network, in which reciprocal inhibition mediates rebound firing, can generate both swimming and synchrony without circuit reconfiguration. Modeling also shows that lengthening the time window between phasic excitation and inhibition by increasing dIN synaptic/conduction delay can improve the stability of synchrony. PMID:24760866

Neural correlates of object-in-place learning in hippocampus and prefrontal cortex.

PubMed

Kim, Jangjin; Delcasso, Sébastien; Lee, Inah

2011-11-23

Hippocampus and prefrontal cortex (PFC) process spatiotemporally discrete events while maintaining goal-directed task demands. Although some studies have reported that neural activities in the two regions are coordinated, such observations have rarely been reported in an object-place paired-associate (OPPA) task in which animals must learn an object-in-place rule. In this study, we recorded single units and local field potentials simultaneously from the CA1 subfield of the hippocampus and PFC as rats learned that Object A, but not Object B, was rewarded in Place 1, but not in Place 2 (vice versa for Object B). Both hippocampus and PFC are required for normal performance in this task. PFC neurons fired in association with the regularity of the occurrence of a certain type of event independent of space, whereas neuronal firing in CA1 was spatially localized for representing a discrete place. Importantly, the differential firing patterns were observed in tandem with common learning-related changes in both regions. Specifically, once OPPA learning occurred and rats used an object-in-place strategy, (1) both CA1 and PFC neurons exhibited spatially more similar and temporally more synchronized firing patterns, (2) spiking activities in both regions were more phase locked to theta rhythms, and (3) CA1-medial PFC coherence in theta oscillation was maximal before entering a critical place for decision making. The results demonstrate differential as well as common neural dynamics between hippocampus and PFC in acquiring the OPPA task and strongly suggest that both regions form a unified functional network for processing an episodic event.

Neural correlates of object-in-place learning in hippocampus and prefrontal cortex

PubMed Central

Kim, Jangjin; Delcasso, Sébastien; Lee, Inah

2011-01-01

Hippocampus and prefrontal cortex (PFC) process spatiotemporally discrete events while maintaining goal-directed task demands. Although some studies have reported that neural activities in the two regions are coordinated, such observations have rarely been reported in an object-place paired-associate (OPPA) task in which animals must learn an object-in-place rule. In this study, we recorded single units and local field potentials simultaneously from the CA1 subfield of the hippocampus and PFC as rats learned that object A, but not object B, was rewarded in place 1, but not in place 2 (vice versa for object B). Both hippocampus and PFC are required for normal performance in this task. PFC neurons fired in association with the regularity of the occurrence of a certain type of event independent of space, whereas neuronal firing in CA1 was spatially localized for representing a discrete place. Importantly, the differential firing patterns were observed in tandem with common learning-related changes in both regions. Specifically, once OPPA learning occurred and rats used an object-in-place strategy, (i) both CA1 and PFC neurons exhibited spatially more similar and temporally more synchronized firing patterns, (ii) spiking activities in both regions were more phase-locked to theta rhythms, (iii) CA1-mPFC coherence in theta oscillation was maximal before entering a critical place for decision making. The results demonstrate differential as well as common neural dynamics between hippocampus and PFC in acquiring the OPPA task and strongly suggest that both regions form a unified functional network for processing an episodic event. PMID:22114269

A Balanced Memory Network

PubMed Central

Roudi, Yasser; Latham, Peter E

2007-01-01

A fundamental problem in neuroscience is understanding how working memory—the ability to store information at intermediate timescales, like tens of seconds—is implemented in realistic neuronal networks. The most likely candidate mechanism is the attractor network, and a great deal of effort has gone toward investigating it theoretically. Yet, despite almost a quarter century of intense work, attractor networks are not fully understood. In particular, there are still two unanswered questions. First, how is it that attractor networks exhibit irregular firing, as is observed experimentally during working memory tasks? And second, how many memories can be stored under biologically realistic conditions? Here we answer both questions by studying an attractor neural network in which inhibition and excitation balance each other. Using mean-field analysis, we derive a three-variable description of attractor networks. From this description it follows that irregular firing can exist only if the number of neurons involved in a memory is large. The same mean-field analysis also shows that the number of memories that can be stored in a network scales with the number of excitatory connections, a result that has been suggested for simple models but never shown for realistic ones. Both of these predictions are verified using simulations with large networks of spiking neurons. PMID:17845070

Loss of phase-locking in non-weakly coupled inhibitory networks of type-I model neurons.

PubMed

Oh, Myongkeun; Matveev, Victor

2009-04-01

Synchronization of excitable cells coupled by reciprocal inhibition is a topic of significant interest due to the important role that inhibitory synaptic interaction plays in the generation and regulation of coherent rhythmic activity in a variety of neural systems. While recent work revealed the synchronizing influence of inhibitory coupling on the dynamics of many networks, it is known that strong coupling can destabilize phase-locked firing. Here we examine the loss of synchrony caused by an increase in inhibitory coupling in networks of type-I Morris-Lecar model oscillators, which is characterized by a period-doubling cascade and leads to mode-locked states with alternation in the firing order of the two cells, as reported recently by Maran and Canavier (J Comput Nerosci, 2008) for a network of Wang-Buzsáki model neurons. Although alternating-order firing has been previously reported as a near-synchronous state, we show that the stable phase difference between the spikes of the two Morris-Lecar cells can constitute as much as 70% of the unperturbed oscillation period. Further, we examine the generality of this phenomenon for a class of type-I oscillators that are close to their excitation thresholds, and provide an intuitive geometric description of such "leap-frog" dynamics. In the Morris-Lecar model network, the alternation in the firing order arises under the condition of fast closing of K( + ) channels at hyperpolarized potentials, which leads to slow dynamics of membrane potential upon synaptic inhibition, allowing the presynaptic cell to advance past the postsynaptic cell in each cycle of the oscillation. Further, we show that non-zero synaptic decay time is crucial for the existence of leap-frog firing in networks of phase oscillators. However, we demonstrate that leap-frog spiking can also be obtained in pulse-coupled inhibitory networks of one-dimensional oscillators with a multi-branched phase domain, for instance in a network of quadratic integrate-and-fire model cells. Finally, for the case of a homogeneous network, we establish quantitative conditions on the phase resetting properties of each cell necessary for stable alternating-order spiking, complementing the analysis of Goel and Ermentrout (Physica D 163:191-216, 2002) of the order-preserving phase transition map.

Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons.

PubMed

Echeveste, Rodrigo; Gros, Claudius

2016-01-01

The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving currents, remaining otherwise quiet. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states. We show in particular that asynchronous drifting states may be stabilized in purely excitatory networks whenever a refractory period is present. Other states found are either fully synchronized or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. The asynchronous drifting state is, additionally, amenable to an analytic solution. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike intervals. In this space, we show that a strange attractor with a fractal dimension of about 1.8 is formed in the mentioned mixed state.

Drifting States and Synchronization Induced Chaos in Autonomous Networks of Excitable Neurons

PubMed Central

Echeveste, Rodrigo; Gros, Claudius

2016-01-01

The study of balanced networks of excitatory and inhibitory neurons has led to several open questions. On the one hand it is yet unclear whether the asynchronous state observed in the brain is autonomously generated, or if it results from the interplay between external drivings and internal dynamics. It is also not known, which kind of network variabilities will lead to irregular spiking and which to synchronous firing states. Here we show how isolated networks of purely excitatory neurons generically show asynchronous firing whenever a minimal level of structural variability is present together with a refractory period. Our autonomous networks are composed of excitable units, in the form of leaky integrators spiking only in response to driving currents, remaining otherwise quiet. For a non-uniform network, composed exclusively of excitatory neurons, we find a rich repertoire of self-induced dynamical states. We show in particular that asynchronous drifting states may be stabilized in purely excitatory networks whenever a refractory period is present. Other states found are either fully synchronized or mixed, containing both drifting and synchronized components. The individual neurons considered are excitable and hence do not dispose of intrinsic natural firing frequencies. An effective network-wide distribution of natural frequencies is however generated autonomously through self-consistent feedback loops. The asynchronous drifting state is, additionally, amenable to an analytic solution. We find two types of asynchronous activity, with the individual neurons spiking regularly in the pure drifting state, albeit with a continuous distribution of firing frequencies. The activity of the drifting component, however, becomes irregular in the mixed state, due to the periodic driving of the synchronized component. We propose a new tool for the study of chaos in spiking neural networks, which consists of an analysis of the time series of pairs of consecutive interspike intervals. In this space, we show that a strange attractor with a fractal dimension of about 1.8 is formed in the mentioned mixed state. PMID:27708572

Superconducting Optoelectronic Circuits for Neuromorphic Computing

NASA Astrophysics Data System (ADS)

Shainline, Jeffrey M.; Buckley, Sonia M.; Mirin, Richard P.; Nam, Sae Woo

2017-03-01

Neural networks have proven effective for solving many difficult computational problems, yet implementing complex neural networks in software is computationally expensive. To explore the limits of information processing, it is necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density (1 mW /cm2 at 20-MHz firing rate) necessary to scale to systems with enormous entropy. Estimates comparing the proposed hardware platform to a human brain show that with the same number of neurons (1 011) and 700 independent connections per neuron, the hardware presented here may achieve an order of magnitude improvement in synaptic events per second per watt.

Hysteresis, neural avalanches, and critical behavior near a first-order transition of a spiking neural network

NASA Astrophysics Data System (ADS)

Scarpetta, Silvia; Apicella, Ilenia; Minati, Ludovico; de Candia, Antonio

2018-06-01

Many experimental results, both in vivo and in vitro, support the idea that the brain cortex operates near a critical point and at the same time works as a reservoir of precise spatiotemporal patterns. However, the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first-order transition, with many spatiotemporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate-and-fire neurons whose connections are the result of the learning of multiple spatiotemporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first-order transition between a low-spiking-rate disordered state (down), and a high-rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

Prediction of the physical properties of barium titanates using an artificial neural network

NASA Astrophysics Data System (ADS)

Al-Jabar, Ahmed Jaafar Abed; Al-dujaili, Mohammed Assi Ahmed; Al-hydary, Imad Ali Disher

2017-04-01

Barium titanate is one of the most important ceramics amongst those that are widely used in the electronic industry because of their dielectric properties. These properties are related to the physical properties of the material, namely, the density and the porosity. Thus, the prediction of these properties is highly desirable. The aim of the current work is to develop models that can predict the density, porosity, firing shrinkage, and the green density of barium titanate BaTiO3. An artificial neural network was used to fulfill this aim. The modified pechini method was used to prepare barium titanate powders with five different particle size distributions. Eighty samples were prepared using different processing parameters including the pressing rate, pressing pressure, heating rate, sintering temperature, and soaking time. In the artificial neural network (ANN) model, the experimental data set consisted of these 80 samples, 70 samples were used for training the network and 10 samples were employed for testing. A comparison was made between the experimental and the predicted data. Good performance of the ANN model was achieved, in which the results showed that the mean error for the density, porosity, shrinkage, and green density are 0.02, 0.06, 0.04, and 0.002, respectively.

Improved discriminability of spatiotemporal neural patterns in rat motor cortical areas as directional choice learning progresses

PubMed Central

Mao, Hongwei; Yuan, Yuan; Si, Jennie

2015-01-01

Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively) areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2–3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats' behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task. PMID:25798093

Suprathreshold stochastic resonance in neural processing tuned by correlation.

PubMed

Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng

2011-07-01

Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.

A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties.

PubMed

Burkitt, A N

2006-08-01

The integrate-and-fire neuron model describes the state of a neuron in terms of its membrane potential, which is determined by the synaptic inputs and the injected current that the neuron receives. When the membrane potential reaches a threshold, an action potential (spike) is generated. This review considers the model in which the synaptic input varies periodically and is described by an inhomogeneous Poisson process, with both current and conductance synapses. The focus is on the mathematical methods that allow the output spike distribution to be analyzed, including first passage time methods and the Fokker-Planck equation. Recent interest in the response of neurons to periodic input has in part arisen from the study of stochastic resonance, which is the noise-induced enhancement of the signal-to-noise ratio. Networks of integrate-and-fire neurons behave in a wide variety of ways and have been used to model a variety of neural, physiological, and psychological phenomena. The properties of the integrate-and-fire neuron model with synaptic input described as a temporally homogeneous Poisson process are reviewed in an accompanying paper (Burkitt in Biol Cybern, 2006).

Suprathreshold stochastic resonance in neural processing tuned by correlation

NASA Astrophysics Data System (ADS)

Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng

2011-07-01

Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.

Deep Belief Networks Learn Context Dependent Behavior

DTIC Science & Technology

2014-03-26

cortical mechanisms for goal- directed behavior. J Cogn Neurosci 17: 1115–1129. 13. Koene RA, Hasselmo ME (2005) An integrate-and-fire model of prefrontal... Neuroscience and Neural Technology, Boston University, Boston, Massachusetts, United States of America, 2 Center of Excellence for Learning in Education...America, 4 Department of Psychology and Graduate Program for Neuroscience , Boston University, Boston, Massachusetts, United States of America Abstract With

Spike frequency adaptation is a possible mechanism for control of attractor preference in auto-associative neural networks

NASA Astrophysics Data System (ADS)

Roach, James; Sander, Leonard; Zochowski, Michal

Auto-associative memory is the ability to retrieve a pattern from a small fraction of the pattern and is an important function of neural networks. Within this context, memories that are stored within the synaptic strengths of networks act as dynamical attractors for network firing patterns. In networks with many encoded memories, some attractors will be stronger than others. This presents the problem of how networks switch between attractors depending on the situation. We suggest that regulation of neuronal spike-frequency adaptation (SFA) provides a universal mechanism for network-wide attractor selectivity. Here we demonstrate in a Hopfield type attractor network that neurons minimal SFA will reliably activate in the pattern corresponding to a local attractor and that a moderate increase in SFA leads to the network to converge to the strongest attractor state. Furthermore, we show that on long time scales SFA allows for temporal sequences of activation to emerge. Finally, using a model of cholinergic modulation within the cortex we argue that dynamic regulation of attractor preference by SFA could be critical for the role of acetylcholine in attention or for arousal states in general. This work was supported by: NSF Graduate Research Fellowship Program under Grant No. DGE 1256260 (JPR), NSF CMMI 1029388 (MRZ) and NSF PoLS 1058034 (MRZ & LMS).

Receptive field optimisation and supervision of a fuzzy spiking neural network.

PubMed

Glackin, Cornelius; Maguire, Liam; McDaid, Liam; Sayers, Heather

2011-04-01

This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train firing rates and behave in a similar manner as fuzzy membership functions. The connectivity of the hidden and output layers in the fuzzy spiking neural network (FSNN) is representative of a fuzzy rule base. Fuzzy C-Means clustering is utilised to produce clusters that represent the antecedent part of the fuzzy rule base that aid classification of the feature data. Suitable cluster widths are determined using two strategies; subjective thresholding and evolutionary thresholding respectively. The former technique typically results in compact solutions in terms of the number of neurons, and is shown to be particularly suited to small data sets. In the latter technique a pool of cluster candidates is generated using Fuzzy C-Means clustering and then a genetic algorithm is employed to select the most suitable clusters and to specify cluster widths. In both scenarios, the network is supervised but learning only occurs locally as in the biological case. The advantages and disadvantages of the network topology for the Fisher Iris and Wisconsin Breast Cancer benchmark classification tasks are demonstrated and directions of current and future work are discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

STDP-based spiking deep convolutional neural networks for object recognition.

PubMed

Kheradpisheh, Saeed Reza; Ganjtabesh, Mohammad; Thorpe, Simon J; Masquelier, Timothée

2018-03-01

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware solutions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developmental metaplasticity in neural circuit codes of firing and structure.

PubMed

Baram, Yoram

2017-01-01

Firing-rate dynamics have been hypothesized to mediate inter-neural information transfer in the brain. While the Hebbian paradigm, relating learning and memory to firing activity, has put synaptic efficacy variation at the center of cortical plasticity, we suggest that the external expression of plasticity by changes in the firing-rate dynamics represents a more general notion of plasticity. Hypothesizing that time constants of plasticity and firing dynamics increase with age, and employing the filtering property of the neuron, we obtain the elementary code of global attractors associated with the firing-rate dynamics in each developmental stage. We define a neural circuit connectivity code as an indivisible set of circuit structures generated by membrane and synapse activation and silencing. Synchronous firing patterns under parameter uniformity, and asynchronous circuit firing are shown to be driven, respectively, by membrane and synapse silencing and reactivation, and maintained by the neuronal filtering property. Analytic, graphical and simulation representation of the discrete iteration maps and of the global attractor codes of neural firing rate are found to be consistent with previous empirical neurobiological findings, which have lacked, however, a specific correspondence between firing modes, time constants, circuit connectivity and cortical developmental stages. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities

NASA Astrophysics Data System (ADS)

Tsigkri-DeSmedt, Nefeli Dimitra; Hizanidis, Johanne; Schöll, Eckehard; Hövel, Philipp; Provata, Astero

2017-07-01

The effects of attracting-nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which model the exchange of electrical signals between neurons. Earlier investigations have demonstrated that repulsive-nonlocal and hierarchical network connectivity can induce complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are nonlocally linked with positive diffusive coupling on a ring network, the system splits into a number of alternating domains. Half of these domains contain elements whose potential stays near the threshold and they are interrupted by active domains where the elements perform regular LIF oscillations. The active domains travel along the ring with constant velocity, depending on the system parameters. When we introduce reflecting coupling in LIF networks unexpected complex spatio-temporal structures arise. For relatively extensive ranges of parameter values, the system splits into two coexisting domains: one where all elements stay near the threshold and one where incoherent states develop, characterized by multi-leveled mean phase velocity profiles.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

PubMed Central

Gerstner, Wulfram

2017-01-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

Optogenetic feedback control of neural activity

PubMed Central

Newman, Jonathan P; Fong, Ming-fai; Millard, Daniel C; Whitmire, Clarissa J; Stanley, Garrett B; Potter, Steve M

2015-01-01

Optogenetic techniques enable precise excitation and inhibition of firing in specified neuronal populations and artifact-free recording of firing activity. Several studies have suggested that optical stimulation provides the precision and dynamic range requisite for closed-loop neuronal control, but no approach yet permits feedback control of neuronal firing. Here we present the ‘optoclamp’, a feedback control technology that provides continuous, real-time adjustments of bidirectional optical stimulation in order to lock spiking activity at specified targets over timescales ranging from seconds to days. We demonstrate how this system can be used to decouple neuronal firing levels from ongoing changes in network excitability due to multi-hour periods of glutamatergic or GABAergic neurotransmission blockade in vitro as well as impinging vibrissal sensory drive in vivo. This technology enables continuous, precise optical control of firing in neuronal populations in order to disentangle causally related variables of circuit activation in a physiologically and ethologically relevant manner. DOI: http://dx.doi.org/10.7554/eLife.07192.001 PMID:26140329

Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET.

PubMed

Dutta, Sangya; Kumar, Vinay; Shukla, Aditya; Mohapatra, Nihar R; Ganguly, Udayan

2017-08-15

Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI- MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (~10 11 neuron based) large neural networks.

A neurally inspired musical instrument classification system based upon the sound onset.

PubMed

Newton, Michael J; Smith, Leslie S

2012-06-01

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

Mean Field Analysis of Stochastic Neural Network Models with Synaptic Depression

NASA Astrophysics Data System (ADS)

Yasuhiko Igarashi,; Masafumi Oizumi,; Masato Okada,

2010-08-01

We investigated the effects of synaptic depression on the macroscopic behavior of stochastic neural networks. Dynamical mean field equations were derived for such networks by taking the average of two stochastic variables: a firing-state variable and a synaptic variable. In these equations, the average product of thesevariables is decoupled as the product of their averages because the two stochastic variables are independent. We proved the independence of these two stochastic variables assuming that the synaptic weight Jij is of the order of 1/N with respect to the number of neurons N. Using these equations, we derived macroscopic steady-state equations for a network with uniform connections and for a ring attractor network with Mexican hat type connectivity and investigated the stability of the steady-state solutions. An oscillatory uniform state was observed in the network with uniform connections owing to a Hopf instability. For the ring network, high-frequency perturbations were shown not to affect system stability. Two mechanisms destabilize the inhomogeneous steady state, leading to two oscillatory states. A Turing instability leads to a rotating bump state, while a Hopf instability leads to an oscillatory bump state, which was previously unreported. Various oscillatory states take place in a network with synaptic depression depending on the strength of the interneuron connections.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.

PubMed

Probst, Dimitri; Petrovici, Mihai A; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons

PubMed Central

Probst, Dimitri; Petrovici, Mihai A.; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems. PMID:25729361

Simulation of networks of spiking neurons: A review of tools and strategies

PubMed Central

Brette, Romain; Rudolph, Michelle; Carnevale, Ted; Hines, Michael; Beeman, David; Bower, James M.; Diesmann, Markus; Morrison, Abigail; Goodman, Philip H.; Harris, Frederick C.; Zirpe, Milind; Natschläger, Thomas; Pecevski, Dejan; Ermentrout, Bard; Djurfeldt, Mikael; Lansner, Anders; Rochel, Olivier; Vieville, Thierry; Muller, Eilif; Davison, Andrew P.; El Boustani, Sami

2009-01-01

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks. PMID:17629781

Signal propagation and logic gating in networks of integrate-and-fire neurons.

PubMed

Vogels, Tim P; Abbott, L F

2005-11-16

Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in synaptic strengths. We investigate the factors affecting transmission and show that multiple signals can propagate simultaneously along different pathways. Using this feature, we show how different types of logic gates can arise within the architecture of the random network through the strengthening of specific synapses.

Comparative analysis of spectral unmixing and neural networks for estimating small diameter tree above-ground biomass in the State of Mississippi

Treesearch

Moham P. Tiruveedhula; Joseph Fan; Ravi R. Sadasivuni; Surya S. Durbha; David L. Evans

2010-01-01

The accumulation of small diameter trees (SDTs) is becoming a nationwide concern. Forest management practices such as fire suppression and selective cutting of high grade timber have contributed to an overabundance of SDTs in many areas. Alternative value-added utilization of SDTs (for composite wood products and biofuels) has prompted the need to estimate their...

Stochastic inference with spiking neurons in the high-conductance state

NASA Astrophysics Data System (ADS)

Petrovici, Mihai A.; Bill, Johannes; Bytschok, Ilja; Schemmel, Johannes; Meier, Karlheinz

2016-10-01

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.

Estimation of Scale Deposition in the Water Walls of an Operating Indian Coal Fired Boiler: Predictive Modeling Approach Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Kumari, Amrita; Das, Suchandan Kumar; Srivastava, Prem Kumar

2016-04-01

Application of computational intelligence for predicting industrial processes has been in extensive use in various industrial sectors including power sector industry. An ANN model using multi-layer perceptron philosophy has been proposed in this paper to predict the deposition behaviors of oxide scale on waterwall tubes of a coal fired boiler. The input parameters comprises of boiler water chemistry and associated operating parameters, such as, pH, alkalinity, total dissolved solids, specific conductivity, iron and dissolved oxygen concentration of the feed water and local heat flux on boiler tube. An efficient gradient based network optimization algorithm has been employed to minimize neural predictions errors. Effects of heat flux, iron content, pH and the concentrations of total dissolved solids in feed water and other operating variables on the scale deposition behavior have been studied. It has been observed that heat flux, iron content and pH of the feed water have a relatively prime influence on the rate of oxide scale deposition in water walls of an Indian boiler. Reasonably good agreement between ANN model predictions and the measured values of oxide scale deposition rate has been observed which is corroborated by the regression fit between these values.

Expectancy, ambiguity, and behavioral flexibility: separable and complementary roles of the orbital frontal cortex and amygdala in processing reward expectancies.

PubMed

Pauli, Wolfgang M; Hazy, Thomas E; O'Reilly, Randall C

2012-02-01

Appetitive goal-directed behavior can be associated with a cue-triggered expectancy that it will lead to a particular reward, a process thought to depend on the OFC and basolateral amygdala complex. We developed a biologically informed neural network model of this system to investigate the separable and complementary roles of these areas as the main components of a flexible expectancy system. These areas of interest are part of a neural network with additional subcortical areas, including the central nucleus of amygdala, ventral (limbic) and dorsomedial (associative) striatum. Our simulations are consistent with the view that the amygdala maintains Pavlovian associations through incremental updating of synaptic strength and that the OFC supports flexibility by maintaining an activation-based working memory of the recent reward history. Our model provides a mechanistic explanation for electrophysiological evidence that cue-related firing in OFC neurons is nonselectively early after a contingency change and why this nonselective firing is critical for promoting plasticity in the amygdala. This ambiguous activation results from the simultaneous maintenance of recent outcomes and obsolete Pavlovian contingencies in working memory. Furthermore, at the beginning of reversal, the OFC is critical for supporting responses that are no longer inappropriate. This result is inconsistent with an exclusive inhibitory account of OFC function.

Local linear approximation of the Jacobian matrix better captures phase resetting of neural limit cycle oscillators.

PubMed

Oprisan, Sorinel Adrian

2014-01-01

One effect of any external perturbations, such as presynaptic inputs, received by limit cycle oscillators when they are part of larger neural networks is a transient change in their firing rate, or phase resetting. A brief external perturbation moves the figurative point outside the limit cycle, a geometric perturbation that we mapped into a transient change in the firing rate, or a temporal phase resetting. In order to gain a better qualitative understanding of the link between the geometry of the limit cycle and the phase resetting curve (PRC), we used a moving reference frame with one axis tangent and the others normal to the limit cycle. We found that the stability coefficients associated with the unperturbed limit cycle provided good quantitative predictions of both the tangent and the normal geometric displacements induced by external perturbations. A geometric-to-temporal mapping allowed us to correctly predict the PRC while preserving the intuitive nature of this geometric approach.

AEGIS: a wildfire prevention and management information system

NASA Astrophysics Data System (ADS)

Kalabokidis, K.; Ager, A.; Finney, M.; Athanasis, N.; Palaiologou, P.; Vasilakos, C.

2015-10-01

A Web-GIS wildfire prevention and management platform (AEGIS) was developed as an integrated and easy-to-use decision support tool (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing access to information that is essential for wildfire management. Databases were created with spatial and non-spatial data to support key system functionalities. Updated land use/land cover maps were produced by combining field inventory data with high resolution multispectral satellite images (RapidEye) to be used as inputs in fire propagation modeling with the Minimum Travel Time algorithm. End users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations; i.e. single-fire propagations, conditional burn probabilities and at the landscape-level, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANN) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps produced an integrated output map for fire danger prediction. The system also incorporates weather measurements from remote automatic weather stations and weather forecast maps. The structure of the algorithms relies on parallel processing techniques (i.e. High Performance Computing and Cloud Computing) that ensure computational power and speed. All AEGIS functionalities are accessible to authorized end users through a web-based graphical user interface. An innovative mobile application, AEGIS App, acts as a complementary tool to the web-based version of the system.

Self-control with spiking and non-spiking neural networks playing games.

PubMed

Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

2010-01-01

Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the payoffs for the dilemma cases in the IPD payoff matrix are differentially biased (increased or decreased), it is shown that increasing the precommitment effect (through increasing the differential bias) increases the probability of cooperating with oneself in the future, irrespective of whether the implementation is with spiking or non-spiking neural networks. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Activity of cardiorespiratory networks revealed by transsynaptic virus expressing GFP.

PubMed

Irnaten, M; Neff, R A; Wang, J; Loewy, A D; Mettenleiter, T C; Mendelowitz, D

2001-01-01

A fluorescent transneuronal marker capable of labeling individual neurons in a central network while maintaining their normal physiology would permit functional studies of neurons within entire networks responsible for complex behaviors such as cardiorespiratory reflexes. The Bartha strain of pseudorabies virus (PRV), an attenuated swine alpha herpesvirus, can be used as a transsynaptic marker of neural circuits. Bartha PRV invades neuronal networks in the CNS through peripherally projecting axons, replicates in these parent neurons, and then travels transsynaptically to continue labeling the second- and higher-order neurons in a time-dependent manner. A Bartha PRV mutant that expresses green fluorescent protein (GFP) was used to visualize and record from neurons that determine the vagal motor outflow to the heart. Here we show that Bartha PRV-GFP-labeled neurons retain their normal electrophysiological properties and that the labeled baroreflex pathways that control heart rate are unaltered by the virus. This novel transynaptic virus permits in vitro studies of identified neurons within functionally defined neuronal systems including networks that mediate cardiovascular and respiratory function and interactions. We also demonstrate superior laryngeal motorneurons fire spontaneously and synapse on cardiac vagal neurons in the nucleus ambiguus. This cardiorespiratory pathway provides a neural basis of respiratory sinus arrhythmias.

Maladaptive Neural Synchrony in Tinnitus: Origin and Restoration

PubMed Central

Eggermont, Jos J.; Tass, Peter A.

2015-01-01

Tinnitus is the conscious perception of sound heard in the absence of physical sound sources external or internal to the body, reflected in aberrant neural synchrony of spontaneous or resting-state brain activity. Neural synchrony is generated by the nearly simultaneous firing of individual neurons, of the synchronization of membrane-potential changes in local neural groups as reflected in the local field potentials, resulting in the presence of oscillatory brain waves in the EEG. Noise-induced hearing loss, often resulting in tinnitus, causes a reorganization of the tonotopic map in auditory cortex and increased spontaneous firing rates and neural synchrony. Spontaneous brain rhythms rely on neural synchrony. Abnormal neural synchrony in tinnitus appears to be confined to specific frequency bands of brain rhythms. Increases in delta-band activity are generated by deafferented/deprived neuronal networks resulting from hearing loss. Coordinated reset (CR) stimulation was developed in order to specifically counteract such abnormal neuronal synchrony by desynchronization. The goal of acoustic CR neuromodulation is to desynchronize tinnitus-related abnormal delta-band oscillations. CR neuromodulation does not require permanent stimulus delivery in order to achieve long-lasting desynchronization or even a full-blown anti-kindling but may have cumulative effects, i.e., the effect of different CR epochs separated by pauses may accumulate. Unlike other approaches, acoustic CR neuromodulation does not intend to reduce tinnitus-related neuronal activity by employing lateral inhibition. The potential efficacy of acoustic CR modulation was shown in a clinical proof of concept trial, where effects achieved in 12 weeks of treatment delivered 4–6 h/day persisted through a preplanned 4-week therapy pause and showed sustained long-term effects after 10 months of therapy, leading to 75% responders. PMID:25741316

Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation.

PubMed

Werkle-Bergner, Markus; Grandy, Thomas H; Chicherio, Christian; Schmiedek, Florian; Lövdén, Martin; Lindenberger, Ulman

2014-06-18

Higher cognitive functions, such as human perceptual decision making, require information processing and transmission across wide-spread cortical networks. Temporally synchronized neural firing patterns are advantageous for efficiently representing and transmitting information within and between assemblies. Computational, empirical, and conceptual considerations all lead to the expectation that the informational redundancy of neural firing rates is positively related to their synchronization. Recent theorizing and initial evidence also suggest that the coding of stimulus characteristics and their integration with behavioral goal states require neural interactions across a hierarchy of timescales. However, most studies thus have focused on neural activity in a single frequency range or on a restricted set of brain regions. Here we provide evidence for cooperative spatiotemporal dynamics of slow and fast EEG signals during perceptual decision making at the single-trial level. Participants performed three masked two-choice decision tasks, one each with numerical, verbal, or figural content. Decrements in posterior α power (8-14 Hz) were paralleled by increments in high-frequency (>30 Hz) signal entropy in trials demanding active sensory processing. Simultaneously, frontocentral θ power (4-7 Hz) increased, indicating evidence integration. The coordinated α/θ dynamics were tightly linked to decision speed and remarkably similar across tasks, suggesting a domain-general mechanism. In sum, we demonstrate an inverse association between decision-related changes in widespread low-frequency power and local high-frequency entropy. The cooperation among mechanisms captured by these changes enhances the informational density of neural response patterns and qualifies as a neural coding system in the service of perceptual decision making. Copyright © 2014 the authors 0270-6474/14/348519-10$15.00/0.

An intelligent emissions controller for fuel lean gas reburn in coal-fired power plants.

PubMed

Reifman, J; Feldman, E E; Wei, T Y; Glickert, R W

2000-02-01

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NOx) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NOx reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach. Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NOx emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.

Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads

PubMed Central

Lee, Tae-Ho; Miernicki, Michelle E.; Telzer, Eva H.

2017-01-01

Despite emerging evidence suggesting a biological basis to our social tiles, our understanding of the neural processes which link two minds is unknown. We implemented a novel approach, which included connectome similarity analysis using resting state intrinsic networks of parent-child dyads as well as daily diaries measured across 14 days. Intrinsic resting-state networks for both parents and their adolescent child were identified using independent component analysis (ICA). Results indicate that parents and children who had more similar RSN connectome also had more similar day-to-day emotional synchrony. Furthermore, dyadic RSN connectome similarity was associated with children’s emotional competence, suggesting that being neurally in-tune with their parents confers emotional benefits. We provide the first evidence that dyadic RSN similarity is associated with emotional synchrony in what is often our first and most essential social bond, the parent-child relationship. PMID:28254510

Adaptive Control of Synchronization in Delay-Coupled Heterogeneous Networks of FitzHugh-Nagumo Nodes

NASA Astrophysics Data System (ADS)

Plotnikov, S. A.; Lehnert, J.; Fradkov, A. L.; Schöll, E.

We study synchronization in delay-coupled neural networks of heterogeneous nodes. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. We show that an adaptive tuning of the overall coupling strength can be used to counteract the effect of the heterogeneity. Our adaptive controller is demonstrated on ring networks of FitzHugh-Nagumo systems which are paradigmatic for excitable dynamics but can also — depending on the system parameters — exhibit self-sustained periodic firing. We show that the adaptively tuned time-delayed coupling enables synchronization even if parameter heterogeneities are so large that excitable nodes coexist with oscillatory ones.

Geometric properties-dependent neural synchrony modulated by extracellular subthreshold electric field

NASA Astrophysics Data System (ADS)

Wei, Xile; Si, Kaili; Yi, Guosheng; Wang, Jiang; Lu, Meili

2016-07-01

In this paper, we use a reduced two-compartment neuron model to investigate the interaction between extracellular subthreshold electric field and synchrony in small world networks. It is observed that network synchronization is closely related to the strength of electric field and geometric properties of the two-compartment model. Specifically, increasing the electric field induces a gradual improvement in network synchrony, while increasing the geometric factor results in an abrupt decrease in synchronization of network. In addition, increasing electric field can make the network become synchronous from asynchronous when the geometric parameter is set to a given value. Furthermore, it is demonstrated that network synchrony can also be affected by the firing frequency and dynamical bifurcation feature of single neuron. These results highlight the effect of weak field on network synchrony from the view of biophysical model, which may contribute to further understanding the effect of electric field on network activity.

Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity

PubMed Central

Waddington, Amelia; Appleby, Peter A.; De Kamps, Marc; Cohen, Netta

2012-01-01

Synfire chains have long been proposed to generate precisely timed sequences of neural activity. Such activity has been linked to numerous neural functions including sensory encoding, cognitive and motor responses. In particular, it has been argued that synfire chains underlie the precise spatiotemporal firing patterns that control song production in a variety of songbirds. Previous studies have suggested that the development of synfire chains requires either initial sparse connectivity or strong topological constraints, in addition to any synaptic learning rules. Here, we show that this necessity can be removed by using a previously reported but hitherto unconsidered spike-timing-dependent plasticity (STDP) rule and activity-dependent excitability. Under this rule the network develops stable synfire chains that possess a non-trivial, scalable multi-layer structure, in which relative layer sizes appear to follow a universal function. Using computational modeling and a coarse grained random walk model, we demonstrate the role of the STDP rule in growing, molding and stabilizing the chain, and link model parameters to the resulting structure. PMID:23162457

Data collapse and critical dynamics in neuronal avalanche data

NASA Astrophysics Data System (ADS)

Butler, Thomas; Friedman, Nir; Dahmen, Karin; Beggs, John; Deville, Lee; Ito, Shinya

2012-02-01

The tasks of information processing, computation, and response to stimuli require neural computation to be remarkably flexible and diverse. To optimally satisfy the demands of neural computation, neuronal networks have been hypothesized to operate near a non-equilibrium critical point. In spite of their importance for neural dynamics, experimental evidence for critical dynamics has been primarily limited to power law statistics that can also emerge from non-critical mechanisms. By tracking the firing of large numbers of synaptically connected cortical neurons and comparing the resulting data to the predictions of critical phenomena, we show that cortical tissues in vitro can function near criticality. Among the most striking predictions of critical dynamics is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function (data collapse). We show for the first time that this prediction is confirmed in neuronal networks. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.

Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

PubMed Central

Pilly, Praveen K.; Grossberg, Stephen

2013-01-01

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation. PMID:23577130

Emergence of small-world structure in networks of spiking neurons through STDP plasticity.

PubMed

Basalyga, Gleb; Gleiser, Pablo M; Wennekers, Thomas

2011-01-01

In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.

Auditory short-term memory in the primate auditory cortex

PubMed Central

Scott, Brian H.; Mishkin, Mortimer

2015-01-01

Sounds are fleeting, and assembling the sequence of inputs at the ear into a coherent percept requires auditory memory across various time scales. Auditory short-term memory comprises at least two components: an active ‘working memory’ bolstered by rehearsal, and a sensory trace that may be passively retained. Working memory relies on representations recalled from long-term memory, and their rehearsal may require phonological mechanisms unique to humans. The sensory component, passive short-term memory (pSTM), is tractable to study in nonhuman primates, whose brain architecture and behavioral repertoire are comparable to our own. This review discusses recent advances in the behavioral and neurophysiological study of auditory memory with a focus on single-unit recordings from macaque monkeys performing delayed-match-to-sample (DMS) tasks. Monkeys appear to employ pSTM to solve these tasks, as evidenced by the impact of interfering stimuli on memory performance. In several regards, pSTM in monkeys resembles pitch memory in humans, and may engage similar neural mechanisms. Neural correlates of DMS performance have been observed throughout the auditory and prefrontal cortex, defining a network of areas supporting auditory STM with parallels to that supporting visual STM. These correlates include persistent neural firing, or a suppression of firing, during the delay period of the memory task, as well as suppression or (less commonly) enhancement of sensory responses when a sound is repeated as a ‘match’ stimulus. Auditory STM is supported by a distributed temporo-frontal network in which sensitivity to stimulus history is an intrinsic feature of auditory processing. PMID:26541581

Exact simulation of integrate-and-fire models with exponential currents.

PubMed

Brette, Romain

2007-10-01

Neural networks can be simulated exactly using event-driven strategies, in which the algorithm advances directly from one spike to the next spike. It applies to neuron models for which we have (1) an explicit expression for the evolution of the state variables between spikes and (2) an explicit test on the state variables that predicts whether and when a spike will be emitted. In a previous work, we proposed a method that allows exact simulation of an integrate-and-fire model with exponential conductances, with the constraint of a single synaptic time constant. In this note, we propose a method, based on polynomial root finding, that applies to integrate-and-fire models with exponential currents, with possibly many different synaptic time constants. Models can include biexponential synaptic currents and spike-triggered adaptation currents.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

PubMed

Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

2016-10-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

PubMed Central

Staras, Kevin

2016-01-01

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

AEGIS: a wildfire prevention and management information system

NASA Astrophysics Data System (ADS)

Kalabokidis, Kostas; Ager, Alan; Finney, Mark; Athanasis, Nikos; Palaiologou, Palaiologos; Vasilakos, Christos

2016-03-01

We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the web-based version of the system.

Design strategies for dynamic closed-loop optogenetic neurocontrol in vivo

NASA Astrophysics Data System (ADS)

Bolus, M. F.; Willats, A. A.; Whitmire, C. J.; Rozell, C. J.; Stanley, G. B.

2018-04-01

Objective. Controlling neural activity enables the possibility of manipulating sensory perception, cognitive processes, and body movement, in addition to providing a powerful framework for functionally disentangling the neural circuits that underlie these complex phenomena. Over the last decade, optogenetic stimulation has become an increasingly important and powerful tool for understanding neural circuit function, owing to the ability to target specific cell types and bidirectionally modulate neural activity. To date, most stimulation has been provided in open-loop or in an on/off closed-loop fashion, where previously-determined stimulation is triggered by an event. Here, we describe and demonstrate a design approach for precise optogenetic control of neuronal firing rate modulation using feedback to guide stimulation continuously. Approach. Using the rodent somatosensory thalamus as an experimental testbed for realizing desired time-varying patterns of firing rate modulation, we utilized a moving average exponential filter to estimate firing rate online from single-unit spiking measured extracellularly. This estimate of instantaneous rate served as feedback for a proportional integral (PI) controller, which was designed during the experiment based on a linear-nonlinear Poisson (LNP) model of the neuronal response to light. Main results. The LNP model fit during the experiment enabled robust closed-loop control, resulting in good tracking of sinusoidal and non-sinusoidal targets, and rejection of unmeasured disturbances. Closed-loop control also enabled manipulation of trial-to-trial variability. Significance. Because neuroscientists are faced with the challenge of dissecting the functions of circuit components, the ability to maintain control of a region of interest in spite of changes in ongoing neural activity will be important for disambiguating function within networks. Closed-loop stimulation strategies are ideal for control that is robust to such changes, and the employment of continuous feedback to adjust stimulation in real-time can improve the quality of data collected using optogenetic manipulation.

Stimuli Reduce the Dimensionality of Cortical Activity

PubMed Central

Mazzucato, Luca; Fontanini, Alfredo; La Camera, Giancarlo

2016-01-01

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models. PMID:26924968

Stimuli Reduce the Dimensionality of Cortical Activity.

PubMed

Mazzucato, Luca; Fontanini, Alfredo; La Camera, Giancarlo

2016-01-01

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.

Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks

PubMed Central

Burroni, Javier; Taylor, P.; Corey, Cassian; Vachnadze, Tengiz; Siegelmann, Hava T.

2017-01-01

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs. Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available. Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates. Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications. PMID:28289370

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Super-pixel extraction based on multi-channel pulse coupled neural network

NASA Astrophysics Data System (ADS)

Xu, GuangZhu; Hu, Song; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

2018-04-01

Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of superpixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.

Neuromodulation: Selected approaches and challenges

PubMed Central

Parpura, Vladimir; Silva, Gabriel A.; Tass, Peter A.; Bennet, Kevin E.; Meyyappan, Meyya; Koehne, Jessica; Lee, Kendall H.; Andrews, Russell J.

2012-01-01

The brain operates through complex interactions in the flow of information and signal processing within neural networks. The “wiring” of such networks, being neuronal or glial, can physically and/or functionally go rogue in various pathological states. Neuromodulation, as a multidisciplinary venture, attempts to correct such faulty nets. In this review, selected approaches and challenges in neuromoduation are discussed. The use of water-dispersible carbon nanotubes have proven effective in modulation of neurite outgrowth in culture as well as in aiding regeneration after spinal cord injury in vivo. Studying neural circuits using computational biology and analytical engineering approaches brings to light geometrical mapping of dynamics within neural networks, much needed information for stimulation interventions in medical practice. Indeed, sophisticated desynchronization approaches used for brain stimulation have been successful in coaxing “misfiring” neuronal circuits to resume productive firing patterns in various human disorders. Devices have been developed for the real time measurement of various neurotransmitters as well as electrical activity in the human brain during electrical deep brain stimulation. Such devices can establish the dynamics of electrochemical changes in the brain during stimulation. With increasing application of nanomaterials in devices for electrical and chemical recording and stimulating in the brain, the era of cellular, and even intracellular, precision neuromodulation will soon be upon us. PMID:23190025

Place Cell Networks in Pre-weanling Rats Show Associative Memory Properties from the Onset of Exploratory Behavior.

PubMed

Muessig, L; Hauser, J; Wills, T J; Cacucci, F

2016-08-01

Place cells are hippocampal pyramidal cells that are active when an animal visits a restricted area of the environment, and collectively their activity constitutes a neural representation of space. Place cell populations in the adult rat hippocampus display fundamental properties consistent with an associative memory network: the ability to 1) generate new and distinct spatial firing patterns when encountering novel spatial contexts or changes in sensory input ("remapping") and 2) reinstate previously stored firing patterns when encountering a familiar context, including on the basis of an incomplete/degraded set of sensory cues ("pattern completion"). To date, it is unknown when these spatial memory responses emerge during brain development. Here, we show that, from the age of first exploration (postnatal day 16) onwards, place cell populations already exhibit these key features: they generate new representations upon exposure to a novel context and can reactivate familiar representations on the basis of an incomplete set of sensory cues. These results demonstrate that, as early as exploratory behaviors emerge, and despite the absence of an adult-like grid cell network, the developing hippocampus processes incoming sensory information as an associative memory network. © The Author 2016. Published by Oxford University Press.

Balanced Cortical Microcircuitry for Spatial Working Memory Based on Corrective Feedback Control

PubMed Central

2014-01-01

A hallmark of working memory is the ability to maintain graded representations of both the spatial location and amplitude of a memorized stimulus. Previous work has identified a neural correlate of spatial working memory in the persistent maintenance of spatially specific patterns of neural activity. How such activity is maintained by neocortical circuits remains unknown. Traditional models of working memory maintain analog representations of either the spatial location or the amplitude of a stimulus, but not both. Furthermore, although most previous models require local excitation and lateral inhibition to maintain spatially localized persistent activity stably, the substrate for lateral inhibitory feedback pathways is unclear. Here, we suggest an alternative model for spatial working memory that is capable of maintaining analog representations of both the spatial location and amplitude of a stimulus, and that does not rely on long-range feedback inhibition. The model consists of a functionally columnar network of recurrently connected excitatory and inhibitory neural populations. When excitation and inhibition are balanced in strength but offset in time, drifts in activity trigger spatially specific negative feedback that corrects memory decay. The resulting networks can temporally integrate inputs at any spatial location, are robust against many commonly considered perturbations in network parameters, and, when implemented in a spiking model, generate irregular neural firing characteristic of that observed experimentally during persistent activity. This work suggests balanced excitatory–inhibitory memory circuits implementing corrective negative feedback as a substrate for spatial working memory. PMID:24828633

Decorrelation of Neural-Network Activity by Inhibitory Feedback

PubMed Central

Einevoll, Gaute T.; Diesmann, Markus

2012-01-01

Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between excitatory (E) and inhibitory (I) neurons, but a consequence of a particular structure of correlations among the three possible pairings (EE, EI, II). PMID:23133368

Mapping auditory nerve firing density using high-level compound action potentials and high-pass noise masking a

PubMed Central

Earl, Brian R.; Chertoff, Mark E.

2012-01-01

Future implementation of regenerative treatments for sensorineural hearing loss may be hindered by the lack of diagnostic tools that specify the target(s) within the cochlea and auditory nerve for delivery of therapeutic agents. Recent research has indicated that the amplitude of high-level compound action potentials (CAPs) is a good predictor of overall auditory nerve survival, but does not pinpoint the location of neural damage. A location-specific estimate of nerve pathology may be possible by using a masking paradigm and high-level CAPs to map auditory nerve firing density throughout the cochlea. This initial study in gerbil utilized a high-pass masking paradigm to determine normative ranges for CAP-derived neural firing density functions using broadband chirp stimuli and low-frequency tonebursts, and to determine if cochlear outer hair cell (OHC) pathology alters the distribution of neural firing in the cochlea. Neural firing distributions for moderate-intensity (60 dB pSPL) chirps were affected by OHC pathology whereas those derived with high-level (90 dB pSPL) chirps were not. These results suggest that CAP-derived neural firing distributions for high-level chirps may provide an estimate of auditory nerve survival that is independent of OHC pathology. PMID:22280596

Frank Beach Award Winner: Steroids as Neuromodulators of Brain Circuits and Behavior

PubMed Central

Remage-Healey, Luke

2014-01-01

Neurons communicate primarily via action potentials that transmit information on the timescale of milliseconds. Neurons also integrate information via alterations in gene transcription and protein translation that are sustained for hours to days after initiation. Positioned between these two signaling timescales are the minute-by-minute actions of neuromodulators. Over the course of minutes, the classical neuromodulators (such as serotonin, dopamine, octopamine, and norepinephrine) can alter and/or stabilize neural circuit patterning as well as behavioral states. Neuromodulators allow many flexible outputs from neural circuits and can encode information content into the firing state of neural networks. The idea that steroid molecules can operate as genuine behavioral neuromodulators - synthesized by and acting within brain circuits on a minute-by-minute timescale - has gained traction in recent years. Evidence for brain steroid synthesis at synaptic terminals has converged with evidence for the rapid actions of brain-derived steroids on neural circuits and behavior. The general principle emerging from this work is that the production of steroid hormones within brain circuits can alter their functional connectivity and shift sensory representations by enhancing their information coding. Steroids produced in the brain can therefore change the information content of neuronal networks to rapidly modulate sensory experience and sensorimotor functions. PMID:25110187

Delayed excitatory and inhibitory feedback shape neural information transmission

NASA Astrophysics Data System (ADS)

Chacron, Maurice J.; Longtin, André; Maler, Leonard

2005-11-01

Feedback circuitry with conduction and synaptic delays is ubiquitous in the nervous system. Yet the effects of delayed feedback on sensory processing of natural signals are poorly understood. This study explores the consequences of delayed excitatory and inhibitory feedback inputs on the processing of sensory information. We show, through numerical simulations and theory, that excitatory and inhibitory feedback can alter the firing frequency response of stochastic neurons in opposite ways by creating dynamical resonances, which in turn lead to information resonances (i.e., increased information transfer for specific ranges of input frequencies). The resonances are created at the expense of decreased information transfer in other frequency ranges. Using linear response theory for stochastically firing neurons, we explain how feedback signals shape the neural transfer function for a single neuron as a function of network size. We also find that balanced excitatory and inhibitory feedback can further enhance information tuning while maintaining a constant mean firing rate. Finally, we apply this theory to in vivo experimental data from weakly electric fish in which the feedback loop can be opened. We show that it qualitatively predicts the observed effects of inhibitory feedback. Our study of feedback excitation and inhibition reveals a possible mechanism by which optimal processing may be achieved over selected frequency ranges.

Feedback Enhances Feedforward Figure-Ground Segmentation by Changing Firing Mode

PubMed Central

Supèr, Hans; Romeo, August

2011-01-01

In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforwardspiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses withthe responses to a homogenous texture. We propose that feedback controlsfigure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons. PMID:21738747

Feedback enhances feedforward figure-ground segmentation by changing firing mode.

PubMed

Supèr, Hans; Romeo, August

2011-01-01

In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.

Life cycle assessment of coal-fired power plants and sensitivity analysis of CO2 emissions from power generation side

NASA Astrophysics Data System (ADS)

Yin, Libao; Liao, Yanfen; Zhou, Lianjie; Wang, Zhao; Ma, Xiaoqian

2017-05-01

The life cycle assessment and environmental impacts of a 1000MW coal-fired power plant were carried out in this paper. The results showed that the operation energy consumption and pollutant emission of the power plant are the highest in all sub-process, which accounts for 93.93% of the total energy consumption and 92.20% of the total emission. Compared to other pollutant emissions from the coal-fired power plant, CO2 reached up to 99.28%. Therefore, the control of CO2 emission from the coal-fired power plants was very important. Based on the BP neural network, the amount of CO2 emission from the generation side of coal-fired power plants was calculated via carbon balance method. The results showed that unit capacity, coal quality and unit operation load had great influence on the CO2 emission from coal-fired power plants in Guangdong Province. The use of high volatile and high heat value of coal also can reduce the CO2 emissions. What’s more, under higher operation load condition, the CO2 emissions of 1 kWh electric energy was less.

Effective deep brain stimulation suppresses low-frequency network oscillations in the basal ganglia by regularizing neural firing patterns.

PubMed

McConnell, George C; So, Rosa Q; Hilliard, Justin D; Lopomo, Paola; Grill, Warren M

2012-11-07

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinson's disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90 Hz) improve motor symptoms, while low frequencies (<50 Hz) are either ineffective or exacerbate symptoms. The neuronal basis for these frequency-dependent effects of DBS is unclear. The effects of different frequencies of STN-DBS on behavior and single-unit neuronal activity in the basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high-frequency DBS reversed motor symptoms, and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high-frequency DBS, but not low-frequency DBS, reduced pathological low-frequency oscillations (∼9 Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low-frequency band to the stimulation frequency during high-frequency DBS, but not during low-frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high-frequency DBS is more effective than low-frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low-frequency network oscillations with a regularized pattern of neuronal firing.

Effective deep brain stimulation suppresses low frequency network oscillations in the basal ganglia by regularizing neural firing patterns

PubMed Central

McConnell, George C.; So, Rosa Q.; Hilliard, Justin D; Lopomo, Paola; Grill, Warren M.

2012-01-01

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinson’s disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90Hz) improve motor symptoms, while low frequencies (<50Hz) are either ineffective or exacerbate symptoms. The neuronal basis for these frequency-dependent effects of DBS is unclear. The effects of different frequencies of STN-DBS on behavior and single-unit neuronal activity in the basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high frequency DBS reversed motor symptoms and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high frequency DBS, but not low frequency DBS, reduced pathological low frequency oscillations (~9Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low frequency band to the stimulation frequency during high frequency DBS, but not during low frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high frequency DBS is more effective than low frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low frequency network oscillations with a regularized pattern of neuronal firing. PMID:23136407

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

PubMed

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.

PubMed

Trapp, Philip; Echeveste, Rodrigo; Gros, Claudius

2018-06-12

Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

PubMed

El-Nagar, Ahmad M

2018-01-01

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Acute ethanol effects on neural encoding of reward size and delay in the nucleus accumbens

PubMed Central

Gutman, Andrea L.

2016-01-01

Acute ethanol administration can cause impulsivity, resulting in increased preference for immediately available rewards over delayed but more valuable alternatives. The manner in which reward size and delay are represented in neural firing is not fully understood, and very little is known about ethanol effects on this encoding. To address this issue, we used in vivo electrophysiology to characterize neural firing in the core of the nucleus accumbens (NAcc) in rats responding for rewards that varied in size or delay after vehicle or ethanol administration. The NAcc is a central element in the circuit that governs decision-making and importantly, promotes choice of delayed rewards. We found that NAcc firing in response to reward-predictive cues encoded anticipated reward value after vehicle administration, but ethanol administration disrupted this encoding, resulting in a loss of discrimination between immediate and delayed rewards in cue-evoked neural responses. In addition, NAcc firing occurring at the time of the operant response (lever pressing) was inversely correlated with behavioral response latency, such that increased firing rates were associated with decreased latencies to lever press. Ethanol administration selectively attenuated this lever press-evoked firing when delayed but not immediate rewards were expected. These effects on neural firing were accompanied by increased behavioral latencies to respond for delayed rewards. Our results suggest that ethanol effects on NAcc cue- and lever press-evoked encoding may contribute to ethanol-induced impulsivity. PMID:27169507

Auditory short-term memory in the primate auditory cortex.

PubMed

Scott, Brian H; Mishkin, Mortimer

2016-06-01

Sounds are fleeting, and assembling the sequence of inputs at the ear into a coherent percept requires auditory memory across various time scales. Auditory short-term memory comprises at least two components: an active ׳working memory' bolstered by rehearsal, and a sensory trace that may be passively retained. Working memory relies on representations recalled from long-term memory, and their rehearsal may require phonological mechanisms unique to humans. The sensory component, passive short-term memory (pSTM), is tractable to study in nonhuman primates, whose brain architecture and behavioral repertoire are comparable to our own. This review discusses recent advances in the behavioral and neurophysiological study of auditory memory with a focus on single-unit recordings from macaque monkeys performing delayed-match-to-sample (DMS) tasks. Monkeys appear to employ pSTM to solve these tasks, as evidenced by the impact of interfering stimuli on memory performance. In several regards, pSTM in monkeys resembles pitch memory in humans, and may engage similar neural mechanisms. Neural correlates of DMS performance have been observed throughout the auditory and prefrontal cortex, defining a network of areas supporting auditory STM with parallels to that supporting visual STM. These correlates include persistent neural firing, or a suppression of firing, during the delay period of the memory task, as well as suppression or (less commonly) enhancement of sensory responses when a sound is repeated as a ׳match' stimulus. Auditory STM is supported by a distributed temporo-frontal network in which sensitivity to stimulus history is an intrinsic feature of auditory processing. This article is part of a Special Issue entitled SI: Auditory working memory. Published by Elsevier B.V.

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator

PubMed Central

Wang, Runchun M.; Thakur, Chetan S.; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks. PMID:29692702

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator.

PubMed

Wang, Runchun M; Thakur, Chetan S; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

Sparsely-synchronized brain rhythm in a small-world neural network

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2013-07-01

Sparsely-synchronized cortical rhythms, associated with diverse cognitive functions, have been observed in electric recordings of brain activity. At the population level, cortical rhythms exhibit small-amplitude fast oscillations while at the cellular level, individual neurons show stochastic firings sparsely at a much lower rate than the population rate. We study the effect of network architecture on sparse synchronization in an inhibitory population of subthreshold Morris-Lecar neurons (which cannot fire spontaneously without noise). Previously, sparse synchronization was found to occur for cases of both global coupling ( i.e., regular all-to-all coupling) and random coupling. However, a real neural network is known to be non-regular and non-random. Here, we consider sparse Watts-Strogatz small-world networks which interpolate between a regular lattice and a random graph via rewiring. We start from a regular lattice with only short-range connections and then investigate the emergence of sparse synchronization by increasing the rewiring probability p for the short-range connections. For p = 0, the average synaptic path length between pairs of neurons becomes long; hence, only an unsynchronized population state exists because the global efficiency of information transfer is low. However, as p is increased, long-range connections begin to appear, and global effective communication between distant neurons may be available via shorter synaptic paths. Consequently, as p passes a threshold p th (}~ 0.044), sparsely-synchronized population rhythms emerge. However, with increasing p, longer axon wirings become expensive because of their material and energy costs. At an optimal value p* DE (}~ 0.24) of the rewiring probability, the ratio of the synchrony degree to the wiring cost is found to become maximal. In this way, an optimal sparse synchronization is found to occur at a minimal wiring cost in an economic small-world network through trade-off between synchrony and wiring cost.

The relevance of network micro-structure for neural dynamics.

PubMed

Pernice, Volker; Deger, Moritz; Cardanobile, Stefano; Rotter, Stefan

2013-01-01

The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits.

Enhanced polychronization in a spiking network with metaplasticity.

PubMed

Guise, Mira; Knott, Alistair; Benuskova, Lubica

2015-01-01

Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002). In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004). Polychronous groups (PNGs) develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP), but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.

Families that fire together smile together: Resting state connectome similarity and daily emotional synchrony in parent-child dyads.

PubMed

Lee, Tae-Ho; Miernicki, Michelle E; Telzer, Eva H

2017-05-15

Despite emerging evidence suggesting a biological basis to our social tiles, our understanding of the neural processes which link two minds is unknown. We implemented a novel approach, which included connectome similarity analysis using resting state intrinsic networks of parent-child dyads as well as daily diaries measured across 14 days. Intrinsic resting-state networks for both parents and their adolescent child were identified using independent component analysis (ICA). Results indicate that parents and children who had more similar RSN connectome also had more similar day-to-day emotional synchrony. Furthermore, dyadic RSN connectome similarity was associated with children's emotional competence, suggesting that being neurally in-tune with their parents confers emotional benefits. We provide the first evidence that dyadic RSN similarity is associated with emotional synchrony in what is often our first and most essential social bond, the parent-child relationship. Copyright © 2017 Elsevier Inc. All rights reserved.

Transition to Chaos in Random Neuronal Networks

NASA Astrophysics Data System (ADS)

Kadmon, Jonathan; Sompolinsky, Haim

2015-10-01

Firing patterns in the central nervous system often exhibit strong temporal irregularity and considerable heterogeneity in time-averaged response properties. Previous studies suggested that these properties are the outcome of the intrinsic chaotic dynamics of the neural circuits. Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e., connection variance) is sufficiently large. In the limit of an infinitely large network, there is a sharp transition from a fixed point to chaos, as the synaptic gain reaches a critical value. Near the onset, chaotic fluctuations are slow, analogous to the ubiquitous, slow irregular fluctuations observed in the firing rates of many cortical circuits. However, the existence of a transition from a fixed point to chaos in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work, we investigate rate-based dynamics of neuronal circuits composed of several subpopulations with randomly diluted connections. Nonzero connections are either positive for excitatory neurons or negative for inhibitory ones, while single neuron output is strictly positive with output rates rising as a power law above threshold, in line with known constraints in many biological systems. Using dynamic mean field theory, we find the phase diagram depicting the regimes of stable fixed-point, unstable-dynamic, and chaotic-rate fluctuations. We focus on the latter and characterize the properties of systems near this transition. We show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as the single population with Gaussian connectivity. In these architectures, the large mean excitatory and inhibitory inputs dynamically balance each other, amplifying the effect of the residual fluctuations. Importantly, the existence of a transition to chaos and its critical properties depend on the shape of the single-neuron nonlinear input-output transfer function, near firing threshold. In particular, for nonlinear transfer functions with a sharp rise near threshold, the transition to chaos disappears in the limit of a large network; instead, the system exhibits chaotic fluctuations even for small synaptic gain. Finally, we investigate transition to chaos in network models with spiking dynamics. We show that when synaptic time constants are slow relative to the mean inverse firing rates, the network undergoes a transition from fast spiking fluctuations with constant rates to a state where the firing rates exhibit chaotic fluctuations, similar to the transition predicted by rate-based dynamics. Systems with finite synaptic time constants and firing rates exhibit a smooth transition from a regime dominated by stationary firing rates to a regime of slow rate fluctuations. This smooth crossover obeys scaling properties, similar to crossover phenomena in statistical mechanics. The theoretical results are supported by computer simulations of several neuronal architectures and dynamics. Consequences for cortical circuit dynamics are discussed. These results advance our understanding of the properties of intrinsic dynamics in realistic neuronal networks and their functional consequences.

Balanced cortical microcircuitry for spatial working memory based on corrective feedback control.

PubMed

Lim, Sukbin; Goldman, Mark S

2014-05-14

A hallmark of working memory is the ability to maintain graded representations of both the spatial location and amplitude of a memorized stimulus. Previous work has identified a neural correlate of spatial working memory in the persistent maintenance of spatially specific patterns of neural activity. How such activity is maintained by neocortical circuits remains unknown. Traditional models of working memory maintain analog representations of either the spatial location or the amplitude of a stimulus, but not both. Furthermore, although most previous models require local excitation and lateral inhibition to maintain spatially localized persistent activity stably, the substrate for lateral inhibitory feedback pathways is unclear. Here, we suggest an alternative model for spatial working memory that is capable of maintaining analog representations of both the spatial location and amplitude of a stimulus, and that does not rely on long-range feedback inhibition. The model consists of a functionally columnar network of recurrently connected excitatory and inhibitory neural populations. When excitation and inhibition are balanced in strength but offset in time, drifts in activity trigger spatially specific negative feedback that corrects memory decay. The resulting networks can temporally integrate inputs at any spatial location, are robust against many commonly considered perturbations in network parameters, and, when implemented in a spiking model, generate irregular neural firing characteristic of that observed experimentally during persistent activity. This work suggests balanced excitatory-inhibitory memory circuits implementing corrective negative feedback as a substrate for spatial working memory. Copyright © 2014 the authors 0270-6474/14/346790-17$15.00/0.

Inferring Single Neuron Properties in Conductance Based Balanced Networks

PubMed Central

Pool, Román Rossi; Mato, Germán

2011-01-01

Balanced states in large networks are a usual hypothesis for explaining the variability of neural activity in cortical systems. In this regime the statistics of the inputs is characterized by static and dynamic fluctuations. The dynamic fluctuations have a Gaussian distribution. Such statistics allows to use reverse correlation methods, by recording synaptic inputs and the spike trains of ongoing spontaneous activity without any additional input. By using this method, properties of the single neuron dynamics that are masked by the balanced state can be quantified. To show the feasibility of this approach we apply it to large networks of conductance based neurons. The networks are classified as Type I or Type II according to the bifurcations which neurons of the different populations undergo near the firing onset. We also analyze mixed networks, in which each population has a mixture of different neuronal types. We determine under which conditions the intrinsic noise generated by the network can be used to apply reverse correlation methods. We find that under realistic conditions we can ascertain with low error the types of neurons present in the network. We also find that data from neurons with similar firing rates can be combined to perform covariance analysis. We compare the results of these methods (that do not requite any external input) to the standard procedure (that requires the injection of Gaussian noise into a single neuron). We find a good agreement between the two procedures. PMID:22016730

A feedback model of figure-ground assignment.

PubMed

Domijan, Drazen; Setić, Mia

2008-05-30

A computational model is proposed in order to explain how bottom-up and top-down signals are combined into a unified perception of figure and background. The model is based on the interaction between the ventral and the dorsal stream. The dorsal stream computes saliency based on boundary signals provided by the simple and the complex cortical cells. Output from the dorsal stream is projected to the surface network which serves as a blackboard on which the surface representation is formed. The surface network is a recurrent network which segregates different surfaces by assigning different firing rates to them. The figure is labeled by the maximal firing rate. Computer simulations showed that the model correctly assigns figural status to the surface with a smaller size, a greater contrast, convexity, surroundedness, horizontal-vertical orientation and a higher spatial frequency content. The simple gradient of activity in the dorsal stream enables the simulation of the new principles of the lower region and the top-bottom polarity. The model also explains how the exogenous attention and the endogenous attention may reverse the figural assignment. Due to the local excitation in the surface network, neural activity at the cued region will spread over the whole surface representation. Therefore, the model implements the object-based attentional selection.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

PubMed Central

Lindén, Henrik; Lansner, Anders

2016-01-01

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison. PMID:27213810

Brain mechanisms for perceptual and reward-related decision-making.

PubMed

Deco, Gustavo; Rolls, Edmund T; Albantakis, Larissa; Romo, Ranulfo

2013-04-01

Phenomenological models of decision-making, including the drift-diffusion and race models, are compared with mechanistic, biologically plausible models, such as integrate-and-fire attractor neuronal network models. The attractor network models show how decision confidence is an emergent property; and make testable predictions about the neural processes (including neuronal activity and fMRI signals) involved in decision-making which indicate that the medial prefrontal cortex is involved in reward value-based decision-making. Synaptic facilitation in these models can help to account for sequential vibrotactile decision-making, and for how postponed decision-related responses are made. The randomness in the neuronal spiking-related noise that makes the decision-making probabilistic is shown to be increased by the graded firing rate representations found in the brain, to be decreased by the diluted connectivity, and still to be significant in biologically large networks with thousands of synapses onto each neuron. The stability of these systems is shown to be influenced in different ways by glutamatergic and GABAergic efficacy, leading to a new field of dynamical neuropsychiatry with applications to understanding schizophrenia and obsessive-compulsive disorder. The noise in these systems is shown to be advantageous, and to apply to similar attractor networks involved in short-term memory, long-term memory, attention, and associative thought processes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Directed functional connectivity matures with motor learning in a cortical pattern generator.

PubMed

Day, Nancy F; Terleski, Kyle L; Nykamp, Duane Q; Nick, Teresa A

2013-02-01

Sequential motor skills may be encoded by feedforward networks that consist of groups of neurons that fire in sequence (Abeles 1991; Long et al. 2010). However, there has been no evidence of an anatomic map of activation sequence in motor control circuits, which would be potentially detectable as directed functional connectivity of coactive neuron groups. The proposed pattern generator for birdsong, the HVC (Long and Fee 2008; Vu et al. 1994), contains axons that are preferentially oriented in the rostrocaudal axis (Nottebohm et al. 1982; Stauffer et al. 2012). We used four-tetrode recordings to assess the activity of ensembles of single neurons along the rostrocaudal HVC axis in anesthetized zebra finches. We found an axial, polarized neural network in which sequential activity is directionally organized along the rostrocaudal axis in adult males, who produce a stereotyped song. Principal neurons fired in rostrocaudal order and with interneurons that were rostral to them, suggesting that groups of excitatory neurons fire at the leading edge of travelling waves of inhibition. Consistent with the synchronization of neurons by caudally travelling waves of inhibition, the activity of interneurons was more coherent in the orthogonal mediolateral axis than in the rostrocaudal axis. If directed functional connectivity within the HVC is important for stereotyped, learned song, then it may be lacking in juveniles, which sing a highly variable song. Indeed, we found little evidence for network directionality in juveniles. These data indicate that a functionally directed network within the HVC matures during sensorimotor learning and may underlie vocal patterning.

Directed functional connectivity matures with motor learning in a cortical pattern generator

PubMed Central

Day, Nancy F.; Terleski, Kyle L.; Nykamp, Duane Q.

2013-01-01

Sequential motor skills may be encoded by feedforward networks that consist of groups of neurons that fire in sequence (Abeles 1991; Long et al. 2010). However, there has been no evidence of an anatomic map of activation sequence in motor control circuits, which would be potentially detectable as directed functional connectivity of coactive neuron groups. The proposed pattern generator for birdsong, the HVC (Long and Fee 2008; Vu et al. 1994), contains axons that are preferentially oriented in the rostrocaudal axis (Nottebohm et al. 1982; Stauffer et al. 2012). We used four-tetrode recordings to assess the activity of ensembles of single neurons along the rostrocaudal HVC axis in anesthetized zebra finches. We found an axial, polarized neural network in which sequential activity is directionally organized along the rostrocaudal axis in adult males, who produce a stereotyped song. Principal neurons fired in rostrocaudal order and with interneurons that were rostral to them, suggesting that groups of excitatory neurons fire at the leading edge of travelling waves of inhibition. Consistent with the synchronization of neurons by caudally travelling waves of inhibition, the activity of interneurons was more coherent in the orthogonal mediolateral axis than in the rostrocaudal axis. If directed functional connectivity within the HVC is important for stereotyped, learned song, then it may be lacking in juveniles, which sing a highly variable song. Indeed, we found little evidence for network directionality in juveniles. These data indicate that a functionally directed network within the HVC matures during sensorimotor learning and may underlie vocal patterning. PMID:23175804

Dynamics of Multistable States during Ongoing and Evoked Cortical Activity

PubMed Central

Mazzucato, Luca

2015-01-01

Single-trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory, and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single-neuron multistability represents a challenge to existing spiking network models, where typically each neuron is at most bistable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multistability in single-neuron firing rates. Each state results from the activation of neural clusters with potentiated intracluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the model's ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single-neuron multistability into bistability and the reduction of trial-by-trial variability. Both predictions were confirmed in the data. Together, these results provide a theoretical framework that captures both ongoing and evoked network dynamics in a single mechanistic model. PMID:26019337

Defining toxicological tipping points in neuronal network development.

PubMed

Frank, Christopher L; Brown, Jasmine P; Wallace, Kathleen; Wambaugh, John F; Shah, Imran; Shafer, Timothy J

2018-02-02

Measuring electrical activity of neural networks by microelectrode array (MEA) has recently shown promise for screening level assessments of chemical toxicity on network development and function. Important aspects of interneuronal communication can be quantified from a single MEA recording, including individual firing rates, coordinated bursting, and measures of network synchrony, providing rich datasets to evaluate chemical effects. Further, multiple recordings can be made from the same network, including during the formation of these networks in vitro. The ability to perform multiple recording sessions over the in vitro development of network activity may provide further insight into developmental effects of neurotoxicants. In the current study, a recently described MEA-based screen of 86 compounds in primary rat cortical cultures over 12 days in vitro was revisited to establish a framework that integrates all available primary measures of electrical activity from MEA recordings into a composite metric for deviation from normal activity (total scalar perturbation). Examining scalar perturbations over time and increasing concentration of compound allowed for definition of critical concentrations or "tipping points" at which the neural networks switched from recovery to non-recovery trajectories for 42 compounds. These tipping point concentrations occurred at predominantly lower concentrations than those causing overt cell viability loss or disrupting individual network parameters, suggesting tipping points may be a more sensitive measure of network functional loss. Comparing tipping points for six compounds with plasma concentrations known to cause developmental neurotoxicity in vivo demonstrated strong concordance and suggests there is potential for using tipping points for chemical prioritization. Published by Elsevier Inc.

Effects of Morphology Constraint on Electrophysiological Properties of Cortical Neurons

NASA Astrophysics Data System (ADS)

Zhu, Geng; Du, Liping; Jin, Lei; Offenhäusser, Andreas

2016-04-01

There is growing interest in engineering nerve cells in vitro to control architecture and connectivity of cultured neuronal networks or to build neuronal networks with predictable computational function. Pattern technologies, such as micro-contact printing, have been developed to design ordered neuronal networks. However, electrophysiological characteristics of the single patterned neuron haven’t been reported. Here, micro-contact printing, using polyolefine polymer (POP) stamps with high resolution, was employed to grow cortical neurons in a designed structure. The results demonstrated that the morphology of patterned neurons was well constrained, and the number of dendrites was decreased to be about 2. Our electrophysiological results showed that alterations of dendritic morphology affected firing patterns of neurons and neural excitability. When stimulated by current, though both patterned and un-patterned neurons presented regular spiking, the dynamics and strength of the response were different. The un-patterned neurons exhibited a monotonically increasing firing frequency in response to injected current, while the patterned neurons first exhibited frequency increase and then a slow decrease. Our findings indicate that the decrease in dendritic complexity of cortical neurons will influence their electrophysiological characteristics and alter their information processing activity, which could be considered when designing neuronal circuitries.

Burnt area mapping from ERS-SAR time series using the principal components transformation

NASA Astrophysics Data System (ADS)

Gimeno, Meritxell; San-Miguel Ayanz, Jesus; Barbosa, Paulo M.; Schmuck, Guido

2003-03-01

Each year thousands of hectares of forest burnt across Southern Europe. To date, remote sensing assessments of this phenomenon have focused on the use of optical satellite imagery. However, the presence of clouds and smoke prevents the acquisition of this type of data in some areas. It is possible to overcome this problem by using synthetic aperture radar (SAR) data. Principal component analysis (PCA) was performed to quantify differences between pre- and post- fire images and to investigate the separability over a European Remote Sensing (ERS) SAR time series. Moreover, the transformation was carried out to determine the best conditions to acquire optimal SAR imagery according to meteorological parameters and the procedures to enhance burnt area discrimination for the identification of fire damage assessment. A comparative neural network classification was performed in order to map and to assess the burnts using a complete ERS time series or just an image before and an image after the fire according to the PCA. The results suggest that ERS is suitable to highlight areas of localized changes associated with forest fire damage in Mediterranean landcover.

Editor's highlight: Evaluation of a Microelectrode Array-based ...

EPA Pesticide Factsheets

Thousands of compounds in the environment have not been characterized for developmental neurotoxicity (DNT) hazard. To address this issue, methods to screen compounds rapidly for DNT hazard evaluation are necessary and are being developed for key neurodevelopmental processes. In order to develop an assay for network formation, the current study evaluated effects of a training set of chemicals on network ontogeny by measuring spontaneous electrical activity in neural networks grown on microelectrode arrays (MEA). Rat (0-24 h old) primary cortical cells were plated in 48 well MEA plates and exposed to six compounds: acetaminophen, bisindolylmaleimide-1 (Bis-1), domoic acid, mevastatin, sodium orthovanadate, and loperamide for a period of 12 days. Spontaneous network activity was recorded on days 2, 5, 7, 9, and 12 and viability was assessed using the Cell Titer Blue® assay on day 12. Network activity (e.g. mean firing rate (MFR), burst rate (BR), etc), increased between days 5 and 12. Random Forest analysis indicated that across all compounds and times, temporal correlation of firing patterns (r), MFR, BR, #of active electrodes and % of spikes in a burst were the most influential parameters in separating control from treated wells. All compounds except acetaminophen (≤ 30 µM) caused concentration-related effects on one or more of these parameters. Domoic acid and sodium orthovanadate altered several of these parameters in the absence of cytotoxicity. Although

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights

PubMed Central

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks. PMID:26973503

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights.

PubMed

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks.

Single neuron firing properties impact correlation-based population coding

PubMed Central

Hong, Sungho; Ratté, Stéphanie; Prescott, Steven A.; De Schutter, Erik

2012-01-01

Correlated spiking has been widely observed but its impact on neural coding remains controversial. Correlation arising from co-modulation of rates across neurons has been shown to vary with the firing rates of individual neurons. This translates into rate and correlation being equivalently tuned to the stimulus; under those conditions, correlated spiking does not provide information beyond that already available from individual neuron firing rates. Such correlations are irrelevant and can reduce coding efficiency by introducing redundancy. Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate co-modulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate co-modulation whereas “ideal” coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. Pyramidal neurons are sensitive to both stimulus mean and variance, and thus exhibit both types of output correlation proportioned according to which operating mode is dominant. Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate co-modulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. PMID:22279226

Learning about memory from (very) large scale hippocampal networks

NASA Astrophysics Data System (ADS)

Meshulam, Leenoy; Gauthier, Jeffrey; Brody, Carlos; Tank, David; Bialek, William

Recent technological progress has dramatically increased our access to the neural activity underlying memory-related tasks. These complex high-dimensional data call for theories that allow us to identify signatures of collective activity in the networks that are crucial for the emergence of cognitive functions. As an example, we study the neural activity in dorsal hippocampus as a mouse runs along a virtual linear track. One of the dominant features of this data is the activity of place cells, which fire when the animal visits particular locations. During the first stage of our work we used a maximum entropy framework to characterize the probability distribution of the joint activity patterns observed across ensembles of up to 100 cells. These models, which are equivalent to Ising models with competing interactions, make surprisingly accurate predictions for the activity of individual neurons given the state of the rest of the network, and this is true both for place cells and for non-place cells. Additionally, the model captures the high-order structure in the data, which cannot be explained by place-related activity alone. For the second stage of our work we study networks of 2000 neurons. To address this much larger system, we are exploring different methods of coarse graining, in the spirit of the renormalization group, searching for simplified models.

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data

PubMed Central

Jang, Min Jee; Nam, Yoonkey

2015-01-01

Abstract. Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of ∼1000 neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements. PMID:26229973

Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

PubMed

Han, Bing; Taha, Tarek M

2010-04-01

There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

Efficient “Communication through Coherence” Requires Oscillations Structured to Minimize Interference between Signals

PubMed Central

Akam, Thomas E.; Kullmann, Dimitri M.

2012-01-01

The ‘communication through coherence’ (CTC) hypothesis proposes that selective communication among neural networks is achieved by coherence between firing rate oscillation in a sending region and gain modulation in a receiving region. Although this hypothesis has stimulated extensive work, it remains unclear whether the mechanism can in principle allow reliable and selective information transfer. Here we use a simple mathematical model to investigate how accurately coherent gain modulation can filter a population-coded target signal from task-irrelevant distracting inputs. We show that selective communication can indeed be achieved, although the structure of oscillatory activity in the target and distracting networks must satisfy certain previously unrecognized constraints. Firstly, the target input must be differentiated from distractors by the amplitude, phase or frequency of its oscillatory modulation. When distracting inputs oscillate incoherently in the same frequency band as the target, communication accuracy is severely degraded because of varying overlap between the firing rate oscillations of distracting inputs and the gain modulation in the receiving region. Secondly, the oscillatory modulation of the target input must be strong in order to achieve a high signal-to-noise ratio relative to stochastic spiking of individual neurons. Thus, whilst providing a quantitative demonstration of the power of coherent oscillatory gain modulation to flexibly control information flow, our results identify constraints imposed by the need to avoid interference between signals, and reveal a likely organizing principle for the structure of neural oscillations in the brain. PMID:23144603

Synchronization in Random Pulse Oscillator Networks

NASA Astrophysics Data System (ADS)

Brown, Kevin; Hermundstad, Ann

Motivated by synchronization phenomena in neural systems, we study synchronization of random networks of coupled pulse oscillators. We begin by considering binomial random networks whose nodes have intrinsic linear dynamics. We quantify order in the network spiking dynamics using a new measure: the normalized Lev-Zimpel complexity (LZC) of the nodes' spike trains. Starting from a globally-synchronized state, we see two broad classes of behaviors. In one (''temporally random''), the LZC is high and nodes spike independently with no coherent pattern. In another (''temporally regular''), the network does not globally synchronize but instead forms coherent, repeating population firing patterns with low LZC. No topological feature of the network reliably predicts whether an individual network will show temporally random or regular behavior; however, we find evidence that degree heterogeneity in binomial networks has a strong effect on the resulting state. To confirm these findings, we generate random networks with independently-adjustable degree mean and variance. We find that the likelihood of temporally-random behavior increases as degree variance increases. Our results indicate the subtle and complex relationship between network structure and dynamics.

Conjunctive coding in an evolved spiking model of retrosplenial cortex.

PubMed

Rounds, Emily L; Alexander, Andrew S; Nitz, Douglas A; Krichmar, Jeffrey L

2018-06-04

Retrosplenial cortex (RSC) is an association cortex supporting spatial navigation and memory. However, critical issues remain concerning the forms by which its ensemble spiking patterns register spatial relationships that are difficult for experimental techniques to fully address. We therefore applied an evolutionary algorithmic optimization technique to create spiking neural network models that matched electrophysiologically observed spiking dynamics in rat RSC neuronal ensembles. Virtual experiments conducted on the evolved networks revealed a mixed selectivity coding capability that was not built into the optimization method, but instead emerged as a consequence of replicating biological firing patterns. The experiments reveal several important outcomes of mixed selectivity that may subserve flexible navigation and spatial representation: (a) robustness to loss of specific inputs, (b) immediate and stable encoding of novel routes and route locations, (c) automatic resolution of input variable conflicts, and (d) dynamic coding that allows rapid adaptation to changing task demands without retraining. These findings suggest that biological retrosplenial cortex can generate unique, first-trial, conjunctive encodings of spatial positions and actions that can be used by downstream brain regions for navigation and path integration. Moreover, these results are consistent with the proposed role for the RSC in the transformation of representations between reference frames and navigation strategy deployment. Finally, the specific modeling framework used for evolving synthetic retrosplenial networks represents an important advance for computational modeling by which synthetic neural networks can encapsulate, describe, and predict the behavior of neural circuits at multiple levels of function. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

PubMed

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

Faulty node detection in wireless sensor networks using a recurrent neural network

NASA Astrophysics Data System (ADS)

Atiga, Jamila; Mbarki, Nour Elhouda; Ejbali, Ridha; Zaied, Mourad

2018-04-01

The wireless sensor networks (WSN) consist of a set of sensors that are more and more used in surveillance applications on a large scale in different areas: military, Environment, Health ... etc. Despite the minimization and the reduction of the manufacturing costs of the sensors, they can operate in places difficult to access without the possibility of reloading of battery, they generally have limited resources in terms of power of emission, of processing capacity, data storage and energy. These sensors can be used in a hostile environment, such as, for example, on a field of battle, in the presence of fires, floods, earthquakes. In these environments the sensors can fail, even in a normal operation. It is therefore necessary to develop algorithms tolerant and detection of defects of the nodes for the network of sensor without wires, therefore, the faults of the sensor can reduce the quality of the surveillance if they are not detected. The values that are measured by the sensors are used to estimate the state of the monitored area. We used the Non-linear Auto- Regressive with eXogeneous (NARX), the recursive architecture of the neural network, to predict the state of a node of a sensor from the previous values described by the functions of time series. The experimental results have verified that the prediction of the State is enhanced by our proposed model.

Common therapeutic mechanisms of pallidal deep brain stimulation for hypo- and hyperkinetic movement disorders

PubMed Central

Iriki, Atsushi; Isoda, Masaki

2015-01-01

Abnormalities in cortico-basal ganglia (CBG) networks can cause a variety of movement disorders ranging from hypokinetic disorders, such as Parkinson's disease (PD), to hyperkinetic conditions, such as Tourette syndrome (TS). Each condition is characterized by distinct patterns of abnormal neural discharge (dysrhythmia) at both the local single-neuron level and the global network level. Despite divergent etiologies, behavioral phenotypes, and neurophysiological profiles, high-frequency deep brain stimulation (HF-DBS) in the basal ganglia has been shown to be effective for both hypo- and hyperkinetic disorders. The aim of this review is to compare and contrast the electrophysiological hallmarks of PD and TS phenotypes in nonhuman primates and discuss why the same treatment (HF-DBS targeted to the globus pallidus internus, GPi-DBS) is capable of ameliorating both symptom profiles. Recent studies have shown that therapeutic GPi-DBS entrains the spiking of neurons located in the vicinity of the stimulating electrode, resulting in strong stimulus-locked modulations in firing probability with minimal changes in the population-scale firing rate. This stimulus effect normalizes/suppresses the pathological firing patterns and dysrhythmia that underlie specific phenotypes in both the PD and TS models. We propose that the elimination of pathological states via stimulus-driven entrainment and suppression, while maintaining thalamocortical network excitability within a normal physiological range, provides a common therapeutic mechanism through which HF-DBS permits information transfer for purposive motor behavior through the CBG while ameliorating conditions with widely different symptom profiles. PMID:26180116

Desynchronization of slow oscillations in the basal ganglia during natural sleep.

PubMed

Mizrahi-Kliger, Aviv D; Kaplan, Alexander; Israel, Zvi; Bergman, Hagai

2018-05-01

Slow oscillations of neuronal activity alternating between firing and silence are a hallmark of slow-wave sleep (SWS). These oscillations reflect the default activity present in all mammalian species, and are ubiquitous to anesthesia, brain slice preparations, and neuronal cultures. In all these cases, neuronal firing is highly synchronous within local circuits, suggesting that oscillation-synchronization coupling may be a governing principle of sleep physiology regardless of anatomical connectivity. To investigate whether this principle applies to overall brain organization, we recorded the activity of individual neurons from basal ganglia (BG) structures and the thalamocortical (TC) network over 70 full nights of natural sleep in two vervet monkeys. During SWS, BG neurons manifested slow oscillations (∼0.5 Hz) in firing rate that were as prominent as in the TC network. However, in sharp contrast to any neural substrate explored thus far, the slow oscillations in all BG structures were completely desynchronized between individual neurons. Furthermore, whereas in the TC network single-cell spiking was locked to slow oscillations in the local field potential (LFP), the BG LFP exhibited only weak slow oscillatory activity and failed to entrain nearby cells. We thus show that synchrony is not inherent to slow oscillations, and propose that the BG desynchronization of slow oscillations could stem from its unique anatomy and functional connectivity. Finally, we posit that BG slow-oscillation desynchronization may further the reemergence of slow-oscillation traveling waves from multiple independent origins in the frontal cortex, thus significantly contributing to normal SWS.

Neural reactivations during sleep determine network credit assignment

PubMed Central

Gulati, Tanuj; Guo, Ling; Ramanathan, Dhakshin S.; Bodepudi, Anitha; Ganguly, Karunesh

2018-01-01

A fundamental goal of motor learning is to establish neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this “credit–assignment” problem. Using neuroprosthetic learning where we could control the causal relationship between neurons and behavior, here we show that sleep–dependent processing is required for credit-assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Importantly, we found a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non–causal activity. Strikingly, decoupling of spiking to slow–oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep plays an essential role in establishing sparse activation patterns that reflect the causal neuron–behavior relationship. PMID:28692062

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

PubMed Central

Maass, Wolfgang

2008-01-01

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics. PMID:18846203

Neural correlates of continuous causal word generation.

PubMed

Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

2012-09-01

Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.

From neural plate to cortical arousal-a neuronal network theory of sleep derived from in vitro "model" systems for primordial patterns of spontaneous bioelectric activity in the vertebrate central nervous system.

PubMed

Corner, Michael A

2013-05-22

In the early 1960s intrinsically generated widespread neuronal discharges were discovered to be the basis for the earliest motor behavior throughout the animal kingdom. The pattern generating system is in fact programmed into the developing nervous system, in a regionally specific manner, already at the early neural plate stage. Such rhythmically modulated phasic bursts were next discovered to be a general feature of developing neural networks and, largely on the basis of experimental interventions in cultured neural tissues, to contribute significantly to their morpho-physiological maturation. In particular, the level of spontaneous synchronized bursting is homeostatically regulated, and has the effect of constraining the development of excessive network excitability. After birth or hatching, this "slow-wave" activity pattern becomes sporadically suppressed in favor of sensory oriented "waking" behaviors better adapted to dealing with environmental contingencies. It nevertheless reappears periodically as "sleep" at several species-specific points in the diurnal/nocturnal cycle. Although this "default" behavior pattern evolves with development, its essential features are preserved throughout the life cycle, and are based upon a few simple mechanisms which can be both experimentally demonstrated and simulated by computer modeling. In contrast, a late onto- and phylogenetic aspect of sleep, viz., the intermittent "paradoxical" activation of the forebrain so as to mimic waking activity, is much less well understood as regards its contribution to brain development. Some recent findings dealing with this question by means of cholinergically induced "aroused" firing patterns in developing neocortical cell cultures, followed by quantitative electrophysiological assays of immediate and longterm sequelae, will be discussed in connection with their putative implications for sleep ontogeny.

Population equations for degree-heterogenous neural networks

NASA Astrophysics Data System (ADS)

Kähne, M.; Sokolov, I. M.; Rüdiger, S.

2017-11-01

We develop a statistical framework for studying recurrent networks with broad distributions of the number of synaptic links per neuron. We treat each group of neurons with equal input degree as one population and derive a system of equations determining the population-averaged firing rates. The derivation rests on an assumption of a large number of neurons and, additionally, an assumption of a large number of synapses per neuron. For the case of binary neurons, analytical solutions can be constructed, which correspond to steps in the activity versus degree space. We apply this theory to networks with degree-correlated topology and show that complex, multi-stable regimes can result for increasing correlations. Our work is motivated by the recent finding of subnetworks of highly active neurons and the fact that these neurons tend to be connected to each other with higher probability.

An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

DTIC Science & Technology

1991-12-01

neural network and the feedforward neural network studied is the single layer perceptron artificial neural network . The recurrent artificial neural network input...features are the wavefront sensor slope outputs and neighboring actuator feedback commands. The feedforward artificial neural network input

The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data

PubMed Central

O'Donnell, Cian; alves, J. Tiago Gonç; Whiteley, Nick; Portera-Cailliau, Carlos; Sejnowski, Terrence J.

2017-01-01

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded (∼2Neurons). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca2+ and voltage imaging tools. PMID:27870612

Classification of 2-dimensional array patterns: assembling many small neural networks is better than using a large one.

PubMed

Chen, Liang; Xue, Wei; Tokuda, Naoyuki

2010-08-01

In many pattern classification/recognition applications of artificial neural networks, an object to be classified is represented by a fixed sized 2-dimensional array of uniform type, which corresponds to the cells of a 2-dimensional grid of the same size. A general neural network structure, called an undistricted neural network, which takes all the elements in the array as inputs could be used for problems such as these. However, a districted neural network can be used to reduce the training complexity. A districted neural network usually consists of two levels of sub-neural networks. Each of the lower level neural networks, called a regional sub-neural network, takes the elements in a region of the array as its inputs and is expected to output a temporary class label, called an individual opinion, based on the partial information of the entire array. The higher level neural network, called an assembling sub-neural network, uses the outputs (opinions) of regional sub-neural networks as inputs, and by consensus derives the label decision for the object. Each of the sub-neural networks can be trained separately and thus the training is less expensive. The regional sub-neural networks can be trained and performed in parallel and independently, therefore a high speed can be achieved. We prove theoretically in this paper, using a simple model, that a districted neural network is actually more stable than an undistricted neural network in noisy environments. We conjecture that the result is valid for all neural networks. This theory is verified by experiments involving gender classification and human face recognition. We conclude that a districted neural network is highly recommended for neural network applications in recognition or classification of 2-dimensional array patterns in highly noisy environments. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery.

PubMed

Zhao, Yi; Ma, Jiale; Li, Xiaohui; Zhang, Jie

2018-02-27

An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset 'UAV_Fire'. A 15-layered self-learning DCNN architecture named 'Fire_Net' is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, 'Fire_Net' guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified.

Attentional modulation of neuronal variability in circuit models of cortex

PubMed Central

Kanashiro, Tatjana; Ocker, Gabriel Koch; Cohen, Marlene R; Doiron, Brent

2017-01-01

The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition. DOI: http://dx.doi.org/10.7554/eLife.23978.001 PMID:28590902

Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.

PubMed

Cantley, Kurtis D; Subramaniam, Anand; Stiegler, Harvey J; Chapman, Richard A; Vogel, Eric M

2012-04-01

Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology. Changes in the average firing rate learning rule depending on various circuit parameters are also presented. The subcircuits are then connected into larger neural networks that demonstrate fundamental properties including associative learning and pulse coincidence detection. Learned extraction of a fundamental frequency component from noisy inputs is demonstrated. It is then shown that if the fundamental sinusoid of one neuron input is out of phase with the rest, its synaptic connection changes differently than the others. Such behavior indicates that the system can learn to detect which signals are important in the general population, and that there is a spike-timing-dependent component of the learning mechanism. Finally, future circuit design and considerations are discussed, including requirements for the memristive device.

Advanced Aeroservoelastic Testing and Data Analysis (Les Essais Aeroservoelastiques et l’Analyse des Donnees).

DTIC Science & Technology

1995-11-01

network - based AFS concepts. Neural networks can addition of vanes in each engine exhaust for thrust provide...parameter estimation programs 19-11 8.6 Neural Network Based Methods unknown parameters of the postulated state space model Artificial neural network ...Forward Neural Network the network that the applicability of the recurrent neural and ii) Recurrent Neural Network [117-119]. network to

Phencyclidine Discoordinates Hippocampal Network Activity But Not Place Fields

PubMed Central

Kao, Hsin-Yi; Kenney, Jana; Kelemen, Eduard

2017-01-01

We used the psychotomimetic phencyclidine (PCP) to investigate the relationships among cognitive behavior, coordinated neural network function, and information processing within the hippocampus place cell system. We report in rats that PCP (5 mg/kg, i.p.) impairs a well learned, hippocampus-dependent place avoidance behavior in rats that requires cognitive control even when PCP is injected directly into dorsal hippocampus. PCP increases 60–100 Hz medium-freguency gamma oscillations in hippocampus CA1 and these increases correlate with the cognitive impairment caused by systemic PCP administration. PCP discoordinates theta-modulated medium-frequency and slow gamma oscillations in CA1 LFPs such that medium-frequency gamma oscillations become more theta-organized than slow gamma oscillations. CA1 place cell firing fields are preserved under PCP, but the drug discoordinates the subsecond temporal organization of discharge among place cells. This discoordination causes place cell ensemble representations of a familiar space to cease resembling pre-PCP representations despite preserved place fields. These findings point to the cognitive impairments caused by PCP arising from neural discoordination. PCP disrupts the timing of discharge with respect to the subsecond timescales of theta and gamma oscillations in the LFP. Because these oscillations arise from local inhibitory synaptic activity, these findings point to excitation–inhibition discoordination as the root of PCP-induced cognitive impairment. SIGNIFICANCE STATEMENT Hippocampal neural discharge is temporally coordinated on timescales of theta and gamma oscillations in the LFP and the discharge of a subset of pyramidal neurons called “place cells” is spatially organized such that discharge is restricted to locations called a cell's “place field.” Because this temporal coordination and spatial discharge organization is thought to represent spatial knowledge, we used the psychotomimetic phencyclidine (PCP) to disrupt cognitive behavior and assess the importance of neural coordination and place fields for spatial cognition. PCP impaired the judicious use of spatial information and discoordinated hippocampal discharge without disrupting firing fields. These findings dissociate place fields from spatial cognitive behavior and suggest that hippocampus discharge coordination is crucial to spatial cognition. PMID:29118102

Neural networks for aircraft control

NASA Technical Reports Server (NTRS)

Linse, Dennis

1990-01-01

Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

Complexity of VTA DA neural activities in response to PFC transection in nicotine treated rats.

PubMed

Chen, Ting Y; Zhang, Die; Dragomir, Andrei; Akay, Yasemin M; Akay, Metin

2011-02-27

The dopaminergic (DA) neurons in the ventral tegmental area (VTA) are widely implicated in the addiction and natural reward circuitry of the brain. These neurons project to several areas of the brain, including prefrontal cortex (PFC), nucleus accubens (NAc) and amygdala. The functional coupling between PFC and VTA has been demonstrated, but little is known about how PFC mediates nicotinic modulation in VTA DA neurons. The objectives of this study were to investigate the effect of acute nicotine exposure on the VTA DA neuronal firing and to understand how the disruption of communication from PFC affects the firing patterns of VTA DA neurons. Extracellular single-unit recordings were performed on Sprague-Dawley rats and nicotine was administered after stable recording was established as baseline. In order to test how input from PFC affects the VTA DA neuronal firing, bilateral transections were made immediate caudal to PFC to mechanically delete the interaction between VTA and PFC. The complexity of the recorded neural firing was subsequently assessed using a method based on the Lempel-Ziv estimator. The results were compared with those obtained when computing the entropy of neural firing. Exposure to nicotine triggered a significant increase in VTA DA neurons firing complexity when communication between PFC and VTA was present, while transection obliterated the effect of nicotine. Similar results were obtained when entropy values were estimated. Our findings suggest that PFC plays a vital role in mediating VTA activity. We speculate that increased firing complexity with acute nicotine administration in PFC intact subjects is due to the close functional coupling between PFC and VTA. This hypothesis is supported by the fact that deletion of PFC results in minor alterations of VTA DA neural firing when nicotine is acutely administered.

Neurophysiology of Flight in Wild-Type and a Mutant Drosophila

PubMed Central

Levine, Jon D.; Wyman, Robert J.

1973-01-01

We report the flight motor output pattern in Drosophila melanogaster and the neural network responsible for it, and describe the bursting motor output pattern in a mutant. There are 26 singly-innervated muscle fibers. There are two basic firing patterns: phase progression, shown by units that receive a common input but have no cross-connections, and phase stability, in which synergic units, receiving a common input and inhibiting each other, fire in a repeating sequence. Flies carrying the mutation stripe cannot fly. Their motor output is reduced to a short duration, high-frequency burst, but the patterning within bursts shows many of the characteristics of the wild type. The mutation is restricted in its effect, as the nervous system has normal morphology by light microscopy and other behaviors of the mutant are normal. Images PMID:4197927

Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language

NASA Astrophysics Data System (ADS)

Tanadi, Theo

2018-03-01

Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.

Locking of correlated neural activity to ongoing oscillations

PubMed Central

Helias, Moritz

2017-01-01

Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis. PMID:28604771

Spiking Neurons for Analysis of Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

PubMed Central

Callister, Kate E.; Griffioen, Peter A.; Avitabile, Sarah C.; Haslem, Angie; Kelly, Luke T.; Kenny, Sally A.; Nimmo, Dale G.; Farnsworth, Lisa M.; Taylor, Rick S.; Watson, Simon J.; Bennett, Andrew F.; Clarke, Michael F.

2016-01-01

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km2 study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery. PMID:27029046

Holding multiple items in short term memory: a neural mechanism.

PubMed

Rolls, Edmund T; Dempere-Marco, Laura; Deco, Gustavo

2013-01-01

Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging.

Holding Multiple Items in Short Term Memory: A Neural Mechanism

PubMed Central

Rolls, Edmund T.; Dempere-Marco, Laura; Deco, Gustavo

2013-01-01

Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging. PMID:23613789

Macroscopic neural mass model constructed from a current-based network model of spiking neurons.

PubMed

Umehara, Hiroaki; Okada, Masato; Teramae, Jun-Nosuke; Naruse, Yasushi

2017-02-01

Neural mass models (NMMs) are efficient frameworks for describing macroscopic cortical dynamics including electroencephalogram and magnetoencephalogram signals. Originally, these models were formulated on an empirical basis of synaptic dynamics with relatively long time constants. By clarifying the relations between NMMs and the dynamics of microscopic structures such as neurons and synapses, we can better understand cortical and neural mechanisms from a multi-scale perspective. In a previous study, the NMMs were analytically derived by averaging the equations of synaptic dynamics over the neurons in the population and further averaging the equations of the membrane-potential dynamics. However, the averaging of synaptic current assumes that the neuron membrane potentials are nearly time invariant and that they remain at sub-threshold levels to retain the conductance-based model. This approximation limits the NMM to the non-firing state. In the present study, we newly propose a derivation of a NMM by alternatively approximating the synaptic current which is assumed to be independent of the membrane potential, thus adopting a current-based model. Our proposed model releases the constraint of the nearly constant membrane potential. We confirm that the obtained model is reducible to the previous model in the non-firing situation and that it reproduces the temporal mean values and relative power spectrum densities of the average membrane potentials for the spiking neurons. It is further ensured that the existing NMM properly models the averaged dynamics over individual neurons even if they are spiking in the populations.

Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications

NASA Astrophysics Data System (ADS)

Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.

2011-08-01

Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

Low and High-Frequency Field Potentials of Cortical Networks ...

EPA Pesticide Factsheets

Neural networks grown on microelectrode arrays (MEAs) have become an important, high content in vitro assay for assessing neuronal function. MEA experiments typically examine high- frequency (HF) (>200 Hz) spikes, and bursts which can be used to discriminate between different pharmacological agents/chemicals. However, normal brain activity is additionally composed of integrated low-frequency (0.5-100 Hz) field potentials (LFPs) which are filtered out of MEA recordings. The objective of this study was to characterize the relationship between HF and LFP neural network signals, and to assess the relative sensitivity of LFPs to selected neurotoxicants. Rat primary cortical cultures were grown on glass, single-well MEA chips. Spontaneous activity was sampled at 25 kHz and recorded (5 min) (Multi-Channel Systems) from mature networks (14 days in vitro). HF (spike, mean firing rate, MFR) and LF (power spectrum, amplitude) components were extracted from each network and served as its baseline (BL). Next, each chip was treated with either 1) a positive control, bicuculline (BIC, 25μM) or domoic acid (DA, 0.3μM), 2) or a negative control, acetaminophen (ACE, 100μM) or glyphosate (GLY, 100μM), 3) a solvent control (H2O or DMSO:EtOH), or 4) a neurotoxicant, (carbaryl, CAR 5, 30μM ; lindane, LIN 1, 10μM; permethrin, PERM 25, 50μM; triadimefon, TRI 5, 65μM). Post treatment, 5 mins of spontaneous activity was recorded and analyzed. As expected posit

Selection of neural network structure for system error correction of electro-optical tracker system with horizontal gimbal

NASA Astrophysics Data System (ADS)

Liu, Xing-fa; Cen, Ming

2007-12-01

Neural Network system error correction method is more precise than lest square system error correction method and spheric harmonics function system error correction method. The accuracy of neural network system error correction method is mainly related to the frame of Neural Network. Analysis and simulation prove that both BP neural network system error correction method and RBF neural network system error correction method have high correction accuracy; it is better to use RBF Network system error correction method than BP Network system error correction method for little studying stylebook considering training rate and neural network scale.

Resonant Interneurons Can Increase Robustness of Gamma Oscillations.

PubMed

Tikidji-Hamburyan, Ruben A; Martínez, Joan José; White, John A; Canavier, Carmen C

2015-11-25

Gamma oscillations are believed to play a critical role in in information processing, encoding, and retrieval. Inhibitory interneuronal network gamma (ING) oscillations may arise from a coupled oscillator mechanism in which individual neurons oscillate or from a population oscillator in which individual neurons fire sparsely and stochastically. All ING mechanisms, including the one proposed herein, rely on alternating waves of inhibition and windows of opportunity for spiking. The coupled oscillator model implemented with Wang-Buzsáki model neurons is not sufficiently robust to heterogeneity in excitatory drive, and therefore intrinsic frequency, to account for in vitro models of ING. Similarly, in a tightly synchronized regime, the stochastic population oscillator model is often characterized by sparse firing, whereas interneurons both in vivo and in vitro do not fire sparsely during gamma, but rather on average every other cycle. We substituted so-called resonator neural models, which exhibit class 2 excitability and postinhibitory rebound (PIR), for the integrators that are typically used. This results in much greater robustness to heterogeneity that actually increases as the average participation in spikes per cycle approximates physiological levels. Moreover, dynamic clamp experiments that show autapse-induced firing in entorhinal cortical interneurons support the idea that PIR can serve as a network gamma mechanism. Furthermore, parvalbumin-positive (PV(+)) cells were much more likely to display both PIR and autapse-induced firing than GAD2(+) cells, supporting the view that PV(+) fast-firing basket cells are more likely to exhibit class 2 excitability than other types of inhibitory interneurons. Gamma oscillations are believed to play a critical role in information processing, encoding, and retrieval. Networks of inhibitory interneurons are thought to be essential for these oscillations. We show that one class of interneurons with an abrupt onset of firing at a threshold frequency may allow more robust synchronization in the presence of noise and heterogeneity. The mechanism for this robustness depends on the intrinsic resonance at this threshold frequency. Moreover, we show experimentally the feasibility of the proposed mechanism and suggest a way to distinguish between this mechanism and another proposed mechanism: that of a stochastic population oscillator independent of the dynamics of individual neurons. Copyright © 2015 the authors 0270-6474/15/3515682-14$15.00/0.

A novel recurrent neural network with finite-time convergence for linear programming.

PubMed

Liu, Qingshan; Cao, Jinde; Chen, Guanrong

2010-11-01

In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.

Prefrontal Cortex HCN1 Channels Enable Intrinsic Persistent Neural Firing and Executive Memory Function

PubMed Central

Thuault, Sébastien J.; Malleret, Gaël; Constantinople, Christine M.; Nicholls, Russell; Chen, Irene; Zhu, Judy; Panteleyev, Andrey; Vronskaya, Svetlana; Nolan, Matthew F.; Bruno, Randy

2013-01-01

In many cortical neurons, HCN1 channels are the major contributors to Ih, the hyperpolarization-activated current, which regulates the intrinsic properties of neurons and shapes their integration of synaptic inputs, paces rhythmic activity, and regulates synaptic plasticity. Here, we examine the physiological role of Ih in deep layer pyramidal neurons in mouse prefrontal cortex (PFC), focusing on persistent activity, a form of sustained firing thought to be important for the behavioral function of the PFC during working memory tasks. We find that HCN1 contributes to the intrinsic persistent firing that is induced by a brief depolarizing current stimulus in the presence of muscarinic agonists. Deletion of HCN1 or acute pharmacological blockade of Ih decreases the fraction of neurons capable of generating persistent firing. The reduction in persistent firing is caused by the membrane hyperpolarization that results from the deletion of HCN1 or Ih blockade, rather than a specific role of the hyperpolarization-activated current in generating persistent activity. In vivo recordings show that deletion of HCN1 has no effect on up states, periods of enhanced synaptic network activity. Parallel behavioral studies demonstrate that HCN1 contributes to the PFC-dependent resolution of proactive interference during working memory. These results thus provide genetic evidence demonstrating the importance of HCN1 to intrinsic persistent firing and the behavioral output of the PFC. The causal role of intrinsic persistent firing in PFC-mediated behavior remains an open question. PMID:23966682

Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery

PubMed Central

Zhao, Yi; Ma, Jiale; Li, Xiaohui

2018-01-01

An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset ‘UAV_Fire’. A 15-layered self-learning DCNN architecture named ‘Fire_Net’ is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, ‘Fire_Net’ guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified. PMID:29495504

Modular, Hierarchical Learning By Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Baldi, Pierre F.; Toomarian, Nikzad

1996-01-01

Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

A network of spiking neurons for computing sparse representations in an energy efficient way

PubMed Central

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B.

2013-01-01

Computing sparse redundant representations is an important problem both in applied mathematics and neuroscience. In many applications, this problem must be solved in an energy efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating via low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, such operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We compare the numerical performance of HDA with existing algorithms and show that in the asymptotic regime the representation error of HDA decays with time, t, as 1/t. We show that HDA is stable against time-varying noise, specifically, the representation error decays as 1/t for Gaussian white noise. PMID:22920853

A neuronal network model with simplified tonotopicity for tinnitus generation and its relief by sound therapy.

PubMed

Nagashino, Hirofumi; Kinouchi, Yohsuke; Danesh, Ali A; Pandya, Abhijit S

2013-01-01

Tinnitus is the perception of sound in the ears or in the head where no external source is present. Sound therapy is one of the most effective techniques for tinnitus treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy, we have proposed conceptual and computational models with plasticity using a neural oscillator or a neuronal network model. In the present paper, we propose a neuronal network model with simplified tonotopicity of the auditory system as more detailed structure. In this model an integrate-and-fire neuron model is employed and homeostatic plasticity is incorporated. The computer simulation results show that the present model can show the generation of oscillation and its cessation by external input. It suggests that the present framework is promising as a modeling for the tinnitus generation and the effects of sound therapy.

A network of spiking neurons for computing sparse representations in an energy-efficient way.

PubMed

Hu, Tao; Genkin, Alexander; Chklovskii, Dmitri B

2012-11-01

Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/√t for gaussian white noise.

Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior.

PubMed

Portugues, Ruben; Feierstein, Claudia E; Engert, Florian; Orger, Michael B

2014-03-19

Most behaviors, even simple innate reflexes, are mediated by circuits of neurons spanning areas throughout the brain. However, in most cases, the distribution and dynamics of firing patterns of these neurons during behavior are not known. We imaged activity, with cellular resolution, throughout the whole brains of zebrafish performing the optokinetic response. We found a sparse, broadly distributed network that has an elaborate but ordered pattern, with a bilaterally symmetrical organization. Activity patterns fell into distinct clusters reflecting sensory and motor processing. By correlating neuronal responses with an array of sensory and motor variables, we find that the network can be clearly divided into distinct functional modules. Comparing aligned data from multiple fish, we find that the spatiotemporal activity dynamics and functional organization are highly stereotyped across individuals. These experiments systematically reveal the functional architecture of neural circuits underlying a sensorimotor behavior in a vertebrate brain. Copyright © 2014 Elsevier Inc. All rights reserved.

Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior

PubMed Central

Portugues, Ruben; Feierstein, Claudia E.; Engert, Florian; Orger, Michael B.

2014-01-01

Summary Most behaviors, even simple innate reflexes, are mediated by circuits of neurons spanning areas throughout the brain. However, in most cases, the distribution and dynamics of firing patterns of these neurons during behavior are not known. We imaged activity, with cellular resolution, throughout the whole brains of zebrafish performing the optokinetic response. We found a sparse, broadly distributed network that has an elaborate, but ordered, pattern, with a bilaterally symmetrical organization. Activity patterns fell into distinct clusters reflecting sensory and motor processing. By correlating neuronal responses with an array of sensory and motor variables, we find that the network can be clearly divided into distinct functional modules. Comparing aligned data from multiple fish, we find that the spatiotemporal activity dynamics and functional organization are highly stereotyped across individuals. These experiments reveal, for the first time in a vertebrate, the comprehensive functional architecture of the neural circuits underlying a sensorimotor behavior. PMID:24656252

Executive Functions and Prefrontal Cortex: A Matter of Persistence?

PubMed Central

Ball, Gareth; Stokes, Paul R.; Rhodes, Rebecca A.; Bose, Subrata K.; Rezek, Iead; Wink, Alle-Meije; Lord, Louis-David; Mehta, Mitul A.; Grasby, Paul M.; Turkheimer, Federico E.

2011-01-01

Executive function is thought to originates from the dynamics of frontal cortical networks. We examined the dynamic properties of the blood oxygen level dependent time-series measured with functional MRI (fMRI) within the prefrontal cortex (PFC) to test the hypothesis that temporally persistent neural activity underlies performance in three tasks of executive function. A numerical estimate of signal persistence, the Hurst exponent, postulated to represent the coherent firing of cortical networks, was determined and correlated with task performance. Increasing persistence in the lateral PFC was shown to correlate with improved performance during an n-back task. Conversely, we observed a correlation between persistence and increasing commission error – indicating a failure to inhibit a prepotent response – during a Go/No-Go task. We propose that persistence within the PFC reflects dynamic network formation and these findings underline the importance of frequency analysis of fMRI time-series in the study of executive functions. PMID:21286223

Noise adaptation in integrate-and fire neurons.

PubMed

Rudd, M E; Brown, L G

1997-07-01

The statistical spiking response of an ensemble of identically prepared stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate-and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. Noise adaptation is characterized by a decrease in the average neural firing rate and an accompanying decrease in the average value of the generator potential, both of which can be attributed to noise-induced resets of the generator potential mediated by the integrate-and-fire mechanism. A quantitative theory of noise adaptation in stochastic integrate-and-fire neurons is developed. It is shown that integrate-and-fire neurons, on average, produce transient spiking activity whenever there is an increase in the level of their input noise. This transient noise response is either reduced or eliminated over time, depending on the parameters of the model neuron. Analytical methods are used to prove that nonleaky integrate-and-fire neurons totally adapt to any constant input noise level, in the sense that their asymptotic spiking rates are independent of the magnitude of their input noise. For leaky integrate-and-fire neurons, the long-run noise adaptation is not total, but the response to noise is partially eliminated. Expressions for the probability density function of the generator potential and the first two moments of the potential distribution are derived for the particular case of a nonleaky neuron driven by gaussian white noise of mean zero and constant variance. The functional significance of noise adaptation for the performance of networks comprising integrate-and-fire neurons is discussed.

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

NASA Astrophysics Data System (ADS)

Wu, Wei; Cui, Bao-Tong

2007-07-01

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

Cracking the barcode of fullerene-like cortical microcolumns.

PubMed

Tozzi, Arturo; Peters, James F; Ori, Ottorino

2017-03-22

Artificial neural systems and nervous graph theoretical analysis rely upon the stance that the neural code is embodied in logic circuits, e.g., spatio-temporal sequences of ON/OFF spiking neurons. Nevertheless, this assumption does not fully explain complex brain functions. Here we show how nervous activity, other than logic circuits, could instead depend on topological transformations and symmetry constraints occurring at the micro-level of the cortical microcolumn, i.e., the embryological, anatomical and functional basic unit of the brain. Tubular microcolumns can be flattened in fullerene-like two-dimensional lattices, equipped with about 80 nodes standing for pyramidal neurons where neural computations take place. We show how the countless possible combinations of activated neurons embedded in the lattice resemble a barcode. Despite the fact that further experimental verification is required in order to validate our claim, different assemblies of firing neurons might have the appearance of diverse codes, each one responsible for a single mental activity. A two-dimensional fullerene-like lattice, grounded on simple topological changes standing for pyramidal neurons' activation, not just displays analogies with the real microcolumn's microcircuitry and the neural connectome, but also the potential for the manufacture of plastic, robust and fast artificial networks in robotic forms of full-fledged neural systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Thakoor, Anil

1990-01-01

Viewgraphs on electronic neural networks for space station are presented. Topics covered include: electronic neural networks; electronic implementations; VLSI/thin film hybrid hardware for neurocomputing; computations with analog parallel processing; features of neuroprocessors; applications of neuroprocessors; neural network hardware for terrain trafficability determination; a dedicated processor for path planning; neural network system interface; neural network for robotic control; error backpropagation algorithm for learning; resource allocation matrix; global optimization neuroprocessor; and electrically programmable read only thin-film synaptic array.

The neural network to determine the mechanical properties of the steels

NASA Astrophysics Data System (ADS)

Yemelyanov, Vitaliy; Yemelyanova, Nataliya; Safonova, Marina; Nedelkin, Aleksey

2018-04-01

The authors describe the neural network structure and software that is designed and developed to determine the mechanical properties of steels. The neural network is developed to refine upon the values of the steels properties. The results of simulations of the developed neural network are shown. The authors note the low standard error of the proposed neural network. To realize the proposed neural network the specialized software has been developed.

Dynamics of Large Systems of Nonlinearly Evolving Units

NASA Astrophysics Data System (ADS)

Lu, Zhixin

The dynamics of large systems of many nonlinearly evolving units is a general research area that has great importance for many areas in science and technology, including biology, computation by artificial neural networks, statistical mechanics, flocking in animal groups, the dynamics of coupled neurons in the brain, and many others. While universal principles and techniques are largely lacking in this broad area of research, there is still one particular phenomenon that seems to be broadly applicable. In particular, this is the idea of emergence, by which is meant macroscopic behaviors that "emerge" from a large system of many "smaller or simpler entities such that...large entities" [i.e., macroscopic behaviors] arise which "exhibit properties the smaller/simpler entities do not exhibit." In this thesis we investigate mechanisms and manifestations of emergence in four dynamical systems consisting many nonlinearly evolving units. These four systems are as follows. (a) We first study the motion of a large ensemble of many noninteracting particles in a slowly changing Hamiltonian system that undergoes a separatrix crossing. In such systems, we find that separatrix-crossing induces a counterintuitive effect. Specifically, numerical simulation of two sets of densely sprinkled initial conditions on two energy curves appears to suggest that the two energy curves, one originally enclosing the other, seemingly interchange their positions. This, however, is topologically forbidden. We resolve this paradox by introducing a numerical simulation method we call "robust" and study its consequences. (b) We next study the collective dynamics of oscillatory pacemaker neurons in Suprachiasmatic Nucleus (SCN), which, through synchrony, govern the circadian rhythm of mammals. We start from a high-dimensional description of the many coupled oscillatory neuronal units within the SCN. This description is based on a forced Kuramoto model. We then reduce the system dimensionality by using the Ott Antonsen Ansatz and obtain a low-dimensional macroscopic description. Using this reduced macroscopic system, we explain the east-west asymmetry of jet-lag recovery and discus the consequences of our findings. (c) Thirdly, we study neuron firing in integrate-and-fire neural networks. We build a discrete-state/discrete-time model with both excitatory and inhibitory neurons and find a phase transition between avalanching dynamics and ceaseless firing dynamics. Power-law firing avalanche size/duration distributions are observed at critical parameter values. Furthermore, in this critical regime we find the same power law exponents as those observed from experiments and previous, more restricted, simulation studies. We also employ a mean-field method and show that inhibitory neurons in this system promote robustness of the criticality (i.e., an enhanced range of system parameter where power-law avalanche statistics applies). (d) Lastly, we study the dynamics of "reservoir computing networks" (RCN's), which is a recurrent neural network (RNN) scheme for machine learning. The advantage of RCN's over traditional RNN's is that the training is done only on the output layer, usually via a simple least-square method. We show that RCN's are very effective for inferring unmeasured state variables of dynamical systems whose system state is only partially measured. Using the examples of the Lorenz system and the Rossler system we demonstrate the potential of an RCN to perform as an universal model-free "observer".

Vibration control of uncertain multiple launch rocket system using radial basis function neural network

NASA Astrophysics Data System (ADS)

Li, Bo; Rui, Xiaoting

2018-01-01

Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

Clique of Functional Hubs Orchestrates Population Bursts in Developmentally Regulated Neural Networks

PubMed Central

Luccioli, Stefano; Ben-Jacob, Eshel; Barzilai, Ari; Bonifazi, Paolo; Torcini, Alessandro

2014-01-01

It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. PMID:25255443

Chaos and Correlated Avalanches in Excitatory Neural Networks with Synaptic Plasticity

NASA Astrophysics Data System (ADS)

Pittorino, Fabrizio; Ibáñez-Berganza, Miguel; di Volo, Matteo; Vezzani, Alessandro; Burioni, Raffaella

2017-03-01

A collective chaotic phase with power law scaling of activity events is observed in a disordered mean field network of purely excitatory leaky integrate-and-fire neurons with short-term synaptic plasticity. The dynamical phase diagram exhibits two transitions from quasisynchronous and asynchronous regimes to the nontrivial, collective, bursty regime with avalanches. In the homogeneous case without disorder, the system synchronizes and the bursty behavior is reflected into a period doubling transition to chaos for a two dimensional discrete map. Numerical simulations show that the bursty chaotic phase with avalanches exhibits a spontaneous emergence of persistent time correlations and enhanced Kolmogorov complexity. Our analysis reveals a mechanism for the generation of irregular avalanches that emerges from the combination of disorder and deterministic underlying chaotic dynamics.

Unfolding an electronic integrate-and-fire circuit.

PubMed

Carrillo, Humberto; Hoppensteadt, Frank

2010-01-01

Many physical and biological phenomena involve accumulation and discharge processes that can occur on significantly different time scales. Models of these processes have contributed to understand excitability self-sustained oscillations and synchronization in arrays of oscillators. Integrate-and-fire (I+F) models are popular minimal fill-and-flush mathematical models. They are used in neuroscience to study spiking and phase locking in single neuron membranes, large scale neural networks, and in a variety of applications in physics and electrical engineering. We show here how the classical first-order I+F model fits into the theory of nonlinear oscillators of van der Pol type by demonstrating that a particular second-order oscillator having small parameters converges in a singular perturbation limit to the I+F model. In this sense, our study provides a novel unfolding of such models and it identifies a constructible electronic circuit that is closely related to I+F.

Using Grid Cells for Navigation

PubMed Central

Bush, Daniel; Barry, Caswell; Manson, Daniel; Burgess, Neil

2015-01-01

Summary Mammals are able to navigate to hidden goal locations by direct routes that may traverse previously unvisited terrain. Empirical evidence suggests that this “vector navigation” relies on an internal representation of space provided by the hippocampal formation. The periodic spatial firing patterns of grid cells in the hippocampal formation offer a compact combinatorial code for location within large-scale space. Here, we consider the computational problem of how to determine the vector between start and goal locations encoded by the firing of grid cells when this vector may be much longer than the largest grid scale. First, we present an algorithmic solution to the problem, inspired by the Fourier shift theorem. Second, we describe several potential neural network implementations of this solution that combine efficiency of search and biological plausibility. Finally, we discuss the empirical predictions of these implementations and their relationship to the anatomy and electrophysiology of the hippocampal formation. PMID:26247860

Object localization in handheld thermal images for fireground understanding

NASA Astrophysics Data System (ADS)

Vandecasteele, Florian; Merci, Bart; Jalalvand, Azarakhsh; Verstockt, Steven

2017-05-01

Despite the broad application of the handheld thermal imaging cameras in firefighting, its usage is mostly limited to subjective interpretation by the person carrying the device. As remedies to overcome this limitation, object localization and classification mechanisms could assist the fireground understanding and help with the automated localization, characterization and spatio-temporal (spreading) analysis of the fire. An automated understanding of thermal images can enrich the conventional knowledge-based firefighting techniques by providing the information from the data and sensing-driven approaches. In this work, transfer learning is applied on multi-labeling convolutional neural network architectures for object localization and recognition in monocular visual, infrared and multispectral dynamic images. Furthermore, the possibility of analyzing fire scene images is studied and their current limitations are discussed. Finally, the understanding of the room configuration (i.e., objects location) for indoor localization in reduced visibility environments and the linking with Building Information Models (BIM) are investigated.

A cortical integrate-and-fire neural network model for blind decoding of visual prosthetic stimulation.

PubMed

Eiber, Calvin D; Morley, John W; Lovell, Nigel H; Suaning, Gregg J

2014-01-01

We present a computational model of the optic pathway which has been adapted to simulate cortical responses to visual-prosthetic stimulation. This model reproduces the statistically observed distributions of spikes for cortical recordings of sham and maximum-intensity stimuli, while simultaneously generating cellular receptive fields consistent with those observed using traditional visual neuroscience methods. By inverting this model to generate candidate phosphenes which could generate the responses observed to novel stimulation strategies, we hope to aid the development of said strategies in-vivo before being deployed in clinical settings.

Spatial gradients and multidimensional dynamics in a neural integrator circuit

PubMed Central

Miri, Andrew; Daie, Kayvon; Arrenberg, Aristides B.; Baier, Herwig; Aksay, Emre; Tank, David W.

2011-01-01

In a neural integrator, the variability and topographical organization of neuronal firing rate persistence can provide information about the circuit’s functional architecture. Here we use optical recording to measure the time constant of decay of persistent firing (“persistence time”) across a population of neurons comprising the larval zebrafish oculomotor velocity-to-position neural integrator. We find extensive persistence time variation (10-fold; coefficients of variation 0.58–1.20) across cells within individual larvae. We also find that the similarity in firing between two neurons decreased as the distance between them increased and that a gradient in persistence time was mapped along the rostrocaudal and dorsoventral axes. This topography is consistent with the emergence of persistence time heterogeneity from a circuit architecture in which nearby neurons are more strongly interconnected than distant ones. Collectively, our results can be accounted for by integrator circuit models characterized by multiple dimensions of slow firing rate dynamics. PMID:21857656

Region stability analysis and tracking control of memristive recurrent neural network.

PubMed

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neuroestrogen signaling in the songbird auditory cortex propagates into a sensorimotor network via an `interface' nucleus

PubMed Central

Pawlisch, Benjamin A.; Remage-Healey, Luke

2014-01-01

Neuromodulators rapidly alter activity of neural circuits and can therefore shape higher-order functions, such as sensorimotor integration. Increasing evidence suggests that brain-derived estrogens, such as 17-β-estradiol, can act rapidly to modulate sensory processing. However, less is known about how rapid estrogen signaling can impact downstream circuits. Past studies have demonstrated that estradiol levels increase within the songbird auditory cortex (the caudomedial nidopallium, NCM) during social interactions. Local estradiol signaling enhances the auditory-evoked firing rate of neurons in NCM to a variety of stimuli, while also enhancing the selectivity of auditory-evoked responses of neurons in a downstream sensorimotor brain region, HVC (proper name). Since these two brain regions are not directly connected, we employed dual extracellular recordings in HVC and the upstream nucleus interfacialis of the nidopallium (NIf) during manipulations of estradiol within NCM to better understand the pathway by which estradiol signaling propagates to downstream circuits. NIf has direct input into HVC, passing auditory information into the vocal motor output pathway, and is a possible source of the neural selectivity within HVC. Here, during acute estradiol administration in NCM, NIf neurons showed increases in baseline firing rates and auditory-evoked firing rates to all stimuli. Furthermore, when estradiol synthesis was blocked in NCM, we observed simultaneous decreases in the selectivity of NIf and HVC neurons. These effects were not due to direct estradiol actions because NIf has little to no capability for local estrogen synthesis or estrogen receptors, and these effects were specific to NIf because other neurons immediately surrounding NIf did not show these changes. Our results demonstrate that transsynaptic, rapid fluctuations in neuroestrogens are transmitted into NIf and subsequently HVC, both regions important for sensorimotor integration. Overall, these findings support the hypothesis that acute neurosteroid actions can propagate within and between neural circuits to modulate their functional connectivity. PMID:25453773

Neuroestrogen signaling in the songbird auditory cortex propagates into a sensorimotor network via an 'interface' nucleus.

PubMed

Pawlisch, B A; Remage-Healey, L

2015-01-22

Neuromodulators rapidly alter activity of neural circuits and can therefore shape higher order functions, such as sensorimotor integration. Increasing evidence suggests that brain-derived estrogens, such as 17-β-estradiol, can act rapidly to modulate sensory processing. However, less is known about how rapid estrogen signaling can impact downstream circuits. Past studies have demonstrated that estradiol levels increase within the songbird auditory cortex (the caudomedial nidopallium, NCM) during social interactions. Local estradiol signaling enhances the auditory-evoked firing rate of neurons in NCM to a variety of stimuli, while also enhancing the selectivity of auditory-evoked responses of neurons in a downstream sensorimotor brain region, HVC (proper name). Since these two brain regions are not directly connected, we employed dual extracellular recordings in HVC and the upstream nucleus interfacialis of the nidopallium (NIf) during manipulations of estradiol within NCM to better understand the pathway by which estradiol signaling propagates to downstream circuits. NIf has direct input into HVC, passing auditory information into the vocal motor output pathway, and is a possible source of the neural selectivity within HVC. Here, during acute estradiol administration in NCM, NIf neurons showed increases in baseline firing rates and auditory-evoked firing rates to all stimuli. Furthermore, when estradiol synthesis was blocked in NCM, we observed simultaneous decreases in the selectivity of NIf and HVC neurons. These effects were not due to direct estradiol actions because NIf has little to no capability for local estrogen synthesis or estrogen receptors, and these effects were specific to NIf because other neurons immediately surrounding NIf did not show these changes. Our results demonstrate that transsynaptic, rapid fluctuations in neuroestrogens are transmitted into NIf and subsequently HVC, both regions important for sensorimotor integration. Overall, these findings support the hypothesis that acute neurosteroid actions can propagate within and between neural circuits to modulate their functional connectivity. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints.

PubMed

Liang, X B; Wang, J

2000-01-01

This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense that any optimum of the objective function with bound constraints is also an equilibrium point of the neural network. If the objective function to be minimized is convex, then the recurrent neural network is complete in the sense that the set of optima of the function with bound constraints coincides with the set of equilibria of the neural network. 2) The recurrent neural network is primal and quasiconvergent in the sense that its trajectory cannot escape from the feasible region and will converge to the set of equilibria of the neural network for any initial point in the feasible bound region. 3) The recurrent neural network has an attractivity property in the sense that its trajectory will eventually converge to the feasible region for any initial states even at outside of the bounded feasible region. 4) For minimizing any strictly convex quadratic objective function subject to bound constraints, the recurrent neural network is globally exponentially stable for almost any positive network parameters. Simulation results are given to demonstrate the convergence and performance of the proposed recurrent neural network for nonlinear optimization with bound constraints.

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

An Introduction to Neural Networks for Hearing Aid Noise Recognition.

ERIC Educational Resources Information Center

Kim, Jun W.; Tyler, Richard S.

1995-01-01

This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

The Shift from a Response Strategy to Object-in-Place Strategy during Learning Is Accompanied by a Matching Shift in Neural Firing Correlates in the Hippocampus

ERIC Educational Resources Information Center

Lee, Inah; Kim, Jangjin

2010-01-01

Hippocampal-dependent tasks often involve specific associations among stimuli (including egocentric information), and such tasks are therefore prone to interference from irrelevant task strategies before a correct strategy is found. Using an object-place paired-associate task, we investigated changes in neural firing patterns in the hippocampus in…

An input-representing interneuron regulates spike timing and thereby phase switching in a motor network.

PubMed

Sasaki, Kosei; Jing, Jian; Due, Michael R; Weiss, Klaudiusz R

2008-02-20

Despite the importance of spike-timing regulation in network functioning, little is known about this regulation at the cellular level. In the Aplysia feeding network, we show that interneuron B65 regulates the timing of the spike initiation of phase-switch neurons B64 and cerebral-buccal interneuron-5/6 (CBI-5/6), and thereby determines the identity of the neuron that acts as a protraction terminator. Previous work showed that B64 begins to fire before the end of protraction phase and terminates protraction in CBI-2-elicited ingestive, but not in CBI-2-elicited egestive programs, thus indicating that the spike timing and phase-switching function of B64 depend on the type of the central pattern generator (CPG)-elicited response rather than on the input used to activate the CPG. Here, we find that CBI-5/6 is a protraction terminator in egestive programs elicited by the esophageal nerve (EN), but not by CBI-2, thus indicating that, in contrast to B64, the spike timing and protraction-terminating function of CBI-5/6 depends on the input to the CPG rather than the response type. Interestingly, B65 activity also depends on the input in that B65 is highly active in EN-elicited programs, but not in CBI-2-elicited programs independent of whether the programs are ingestive or egestive. Notably, during EN-elicited egestive programs, hyperpolarization of B65 delays the onset of CBI-5/6 firing, whereas in CBI-2-elicited ingestive programs, B65 stimulation simultaneously advances CBI-5/6 firing and delays B64 firing, thereby substituting CBI-5/6 for B64 as the protraction terminator. Thus, we identified a neural mechanism that, in an input-dependent manner, regulates spike timing and thereby the functional role of specific neurons.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

PubMed

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.

Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

NASA Astrophysics Data System (ADS)

Sengupta, A.; Ranjan, P.

2002-07-01

Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double network and the principal component network involve the minimum computation time, although the conventional network also performs well enough to be used in real time.

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

PubMed

Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-07-20

Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

A Million-Plus Neuron Model of the Hippocampal Dentate Gyrus: Critical Role for Topography in Determining Spatiotemporal Network Dynamics.

PubMed

Hendrickson, Phillip J; Yu, Gene J; Song, Dong; Berger, Theodore W

2016-01-01

This paper describes a million-plus granule cell compartmental model of the rat hippocampal dentate gyrus, including excitatory, perforant path input from the entorhinal cortex, and feedforward and feedback inhibitory input from dentate interneurons. The model includes experimentally determined morphological and biophysical properties of granule cells, together with glutamatergic AMPA-like EPSP and GABAergic GABAA-like IPSP synaptic excitatory and inhibitory inputs, respectively. Each granule cell was composed of approximately 200 compartments having passive and active conductances distributed throughout the somatic and dendritic regions. Modeling excitatory input from the entorhinal cortex was guided by axonal transport studies documenting the topographical organization of projections from subregions of the medial and lateral entorhinal cortex, plus other important details of the distribution of glutamatergic inputs to the dentate gyrus. Information contained within previously published maps of this major hippocampal afferent were systematically converted to scales that allowed the topographical distribution and relative synaptic densities of perforant path inputs to be quantitatively estimated for inclusion in the current model. Results showed that when medial and lateral entorhinal cortical neurons maintained Poisson random firing, dentate granule cells expressed, throughout the million-cell network, a robust nonrandom pattern of spiking best described as a spatiotemporal "clustering." To identify the network property or properties responsible for generating such firing "clusters," we progressively eliminated from the model key mechanisms, such as feedforward and feedback inhibition, intrinsic membrane properties underlying rhythmic burst firing, and/or topographical organization of entorhinal afferents. Findings conclusively identified topographical organization of inputs as the key element responsible for generating a spatiotemporal distribution of clustered firing. These results uncover a functional organization of perforant path afferents to the dentate gyrus not previously recognized: topography-dependent clusters of granule cell activity as "functional units" or "channels" that organize the processing of entorhinal signals. This modeling study also reveals for the first time how a global signal processing feature of a neural network can evolve from one of its underlying structural characteristics.

A Million-Plus Neuron Model of the Hippocampal Dentate Gyrus: Critical Role for Topography in Determining Spatio-Temporal Network Dynamics

PubMed Central

Hendrickson, Phillip J.; Yu, Gene J.; Song, Dong; Berger, Theodore W.

2016-01-01

Goal This manuscript describes a million-plus granule cell compartmental model of the rat hippocampal dentate gyrus, including excitatory, perforant path input from the entorhinal cortex, and feedforward and feedback inhibitory input from dentate interneurons. Methods The model includes experimentally determined morphological and biophysical properties of granule cells, together with glutamatergic AMPA-like EPSP and GABAergic GABAA-like IPSP synaptic excitatory and inhibitory inputs, respectively. Each granule cell was composed of approximately 200 compartments having passive and active conductances distributed throughout the somatic and dendritic regions. Modeling excitatory input from the entorhinal cortex was guided by axonal transport studies documenting the topographical organization of projections from subregions of the medial and lateral entorhinal cortex, plus other important details of the distribution of glutamatergic inputs to the dentate gyrus. Information contained within previously published maps of this major hippocampal afferent were systematically converted to scales that allowed the topographical distribution and relative synaptic densities of perforant path inputs to be quantitatively estimated for inclusion in the current model. Results Results showed that when medial and lateral entorhinal cortical neurons maintained Poisson random firing, dentate granule cells expressed, throughout the million-cell network, a robust, non-random pattern of spiking best described as spatio-temporal “clustering”. To identify the network property or properties responsible for generating such firing “clusters”, we progressively eliminated from the model key mechanisms such as feedforward and feedback inhibition, intrinsic membrane properties underlying rhythmic burst firing, and/or topographical organization of entorhinal afferents. Conclusion Findings conclusively identified topographical organization of inputs as the key element responsible for generating a spatio-temporal distribution of clustered firing. These results uncover a functional organization of perforant path afferents to the dentate gyrus not previously recognized: topography-dependent clusters of granule cell activity as “functional units” or “channels” that organize the processing of entorhinal signals. This modeling study also reveals for the first time how a global signal processing feature of a neural network can evolve from one of its underlying structural characteristics. PMID:26087482

Nested Neural Networks

NASA Technical Reports Server (NTRS)

Baram, Yoram

1992-01-01

Report presents analysis of nested neural networks, consisting of interconnected subnetworks. Analysis based on simplified mathematical models more appropriate for artificial electronic neural networks, partly applicable to biological neural networks. Nested structure allows for retrieval of individual subpatterns. Requires fewer wires and connection devices than fully connected networks, and allows for local reconstruction of damaged subnetworks without rewiring entire network.

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

PubMed

Mocanu, Decebal Constantin; Mocanu, Elena; Stone, Peter; Nguyen, Phuong H; Gibescu, Madeleine; Liotta, Antonio

2018-06-19

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős-Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible.

Quantum neural networks: Current status and prospects for development

NASA Astrophysics Data System (ADS)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Signatures of criticality arise from random subsampling in simple population models.

PubMed

Nonnenmacher, Marcel; Behrens, Christian; Berens, Philipp; Bethge, Matthias; Macke, Jakob H

2017-10-01

The rise of large-scale recordings of neuronal activity has fueled the hope to gain new insights into the collective activity of neural ensembles. How can one link the statistics of neural population activity to underlying principles and theories? One attempt to interpret such data builds upon analogies to the behaviour of collective systems in statistical physics. Divergence of the specific heat-a measure of population statistics derived from thermodynamics-has been used to suggest that neural populations are optimized to operate at a "critical point". However, these findings have been challenged by theoretical studies which have shown that common inputs can lead to diverging specific heat. Here, we connect "signatures of criticality", and in particular the divergence of specific heat, back to statistics of neural population activity commonly studied in neural coding: firing rates and pairwise correlations. We show that the specific heat diverges whenever the average correlation strength does not depend on population size. This is necessarily true when data with correlations is randomly subsampled during the analysis process, irrespective of the detailed structure or origin of correlations. We also show how the characteristic shape of specific heat capacity curves depends on firing rates and correlations, using both analytically tractable models and numerical simulations of a canonical feed-forward population model. To analyze these simulations, we develop efficient methods for characterizing large-scale neural population activity with maximum entropy models. We find that, consistent with experimental findings, increases in firing rates and correlation directly lead to more pronounced signatures. Thus, previous reports of thermodynamical criticality in neural populations based on the analysis of specific heat can be explained by average firing rates and correlations, and are not indicative of an optimized coding strategy. We conclude that a reliable interpretation of statistical tests for theories of neural coding is possible only in reference to relevant ground-truth models.

Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

PubMed

Cannon, Jonathan; Kopell, Nancy; Gardner, Timothy; Markowitz, Jeffrey

2015-11-01

Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

PubMed

Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

Neural network approaches to capture temporal information

NASA Astrophysics Data System (ADS)

van Veelen, Martijn; Nijhuis, Jos; Spaanenburg, Ben

2000-05-01

The automated design and construction of neural networks receives growing attention of the neural networks community. Both the growing availability of computing power and development of mathematical and probabilistic theory have had severe impact on the design and modelling approaches of neural networks. This impact is most apparent in the use of neural networks to time series prediction. In this paper, we give our views on past, contemporary and future design and modelling approaches to neural forecasting.

The role of symmetry in neural networks and their Laplacian spectra.

PubMed

de Lange, Siemon C; van den Heuvel, Martijn P; de Reus, Marcel A

2016-11-01

Human and animal nervous systems constitute complexly wired networks that form the infrastructure for neural processing and integration of information. The organization of these neural networks can be analyzed using the so-called Laplacian spectrum, providing a mathematical tool to produce systems-level network fingerprints. In this article, we examine a characteristic central peak in the spectrum of neural networks, including anatomical brain network maps of the mouse, cat and macaque, as well as anatomical and functional network maps of human brain connectivity. We link the occurrence of this central peak to the level of symmetry in neural networks, an intriguing aspect of network organization resulting from network elements that exhibit similar wiring patterns. Specifically, we propose a measure to capture the global level of symmetry of a network and show that, for both empirical networks and network models, the height of the main peak in the Laplacian spectrum is strongly related to node symmetry in the underlying network. Moreover, examination of spectra of duplication-based model networks shows that neural spectra are best approximated using a trade-off between duplication and diversification. Taken together, our results facilitate a better understanding of neural network spectra and the importance of symmetry in neural networks. Copyright © 2016 Elsevier Inc. All rights reserved.

Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices.

PubMed

Zhang, Junfeng; Zhao, Xudong; Huang, Jun

2016-11-01

This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

PubMed Central

Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

2016-01-01

Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

The Laplacian spectrum of neural networks

PubMed Central

de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

2014-01-01

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location.

PubMed

Crago, Patrick E; Makowski, Nathaniel S

2014-10-01

Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

Introduction to Neural Networks.

DTIC Science & Technology

1992-03-01

parallel processing of information that can greatly reduce the time required to perform operations which are needed in pattern recognition. Neural network, Artificial neural network , Neural net, ANN.

A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.

PubMed

Zhang, J W; Rangan, A V

2015-04-01

In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Intelligent image processing for vegetation classification using multispectral LANDSAT data

NASA Astrophysics Data System (ADS)

Santos, Stewart R.; Flores, Jorge L.; Garcia-Torales, G.

2015-09-01

We propose an intelligent computational technique for analysis of vegetation imaging, which are acquired with multispectral scanner (MSS) sensor. This work focuses on intelligent and adaptive artificial neural network (ANN) methodologies that allow segmentation and classification of spectral remote sensing (RS) signatures, in order to obtain a high resolution map, in which we can delimit the wooded areas and quantify the amount of combustible materials present into these areas. This could provide important information to prevent fires and deforestation of wooded areas. The spectral RS input data, acquired by the MSS sensor, are considered in a random propagation remotely sensed scene with unknown statistics for each Thematic Mapper (TM) band. Performing high-resolution reconstruction and adding these spectral values with neighbor pixels information from each TM band, we can include contextual information into an ANN. The biggest challenge in conventional classifiers is how to reduce the number of components in the feature vector, while preserving the major information contained in the data, especially when the dimensionality of the feature space is high. Preliminary results show that the Adaptive Modified Neural Network method is a promising and effective spectral method for segmentation and classification in RS images acquired with MSS sensor.

Network recruitment to coherent oscillations in a hippocampal computer model

PubMed Central

Krieger, Abba; Litt, Brian

2011-01-01

Coherent neural oscillations represent transient synchronization of local neuronal populations in both normal and pathological brain activity. These oscillations occur at or above gamma frequencies (>30 Hz) and often are propagated to neighboring tissue under circumstances that are both normal and abnormal, such as gamma binding or seizures. The mechanisms that generate and propagate these oscillations are poorly understood. In the present study we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of stochastic resonance and coherence resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. “Epileptic” high-frequency oscillations (HFOs), produced by pathologies such as increased synaptic activity and recurrent connections, were superior at recruiting neighboring tissue. “Normal” HFOs, associated with fast firing of inhibitory cells and sparse pyramidal cell firing, tended to suppress surrounding cells and showed very limited ability to recruit. These findings point to synaptic noise and physiological coupling as important targets for understanding the generation and propagation of both normal and pathological HFOs, suggesting potential new diagnostic and therapeutic approaches to human disorders such as epilepsy. PMID:21273309

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

Medical image analysis with artificial neural networks.

PubMed

Jiang, J; Trundle, P; Ren, J

2010-12-01

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

Importance of the cutoff value in the quadratic adaptive integrate-and-fire model.

PubMed

Touboul, Jonathan

2009-08-01

The quadratic adaptive integrate-and-fire model (Izhikevich, 2003 , 2007 ) is able to reproduce various firing patterns of cortical neurons and is widely used in large-scale simulations of neural networks. This model describes the dynamics of the membrane potential by a differential equation that is quadratic in the voltage, coupled to a second equation for adaptation. Integration is stopped during the rise phase of a spike at a voltage cutoff value V(c) or when it blows up. Subsequently the membrane potential is reset, and the adaptation variable is increased by a fixed amount. We show in this note that in the absence of a cutoff value, not only the voltage but also the adaptation variable diverges in finite time during spike generation in the quadratic model. The divergence of the adaptation variable makes the system very sensitive to the cutoff: changing V(c) can dramatically alter the spike patterns. Furthermore, from a computational viewpoint, the divergence of the adaptation variable implies that the time steps for numerical simulation need to be small and adaptive. However, divergence of the adaptation variable does not occur for the quartic model (Touboul, 2008 ) and the adaptive exponential integrate-and-fire model (Brette & Gerstner, 2005 ). Hence, these models are robust to changes in the cutoff value.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

The effect of the neural activity on topological properties of growing neural networks.

PubMed

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

LavaNet—Neural network development environment in a general mine planning package

NASA Astrophysics Data System (ADS)

Kapageridis, Ioannis Konstantinou; Triantafyllou, A. G.

2011-04-01

LavaNet is a series of scripts written in Perl that gives access to a neural network simulation environment inside a general mine planning package. A well known and a very popular neural network development environment, the Stuttgart Neural Network Simulator, is used as the base for the development of neural networks. LavaNet runs inside VULCAN™—a complete mine planning package with advanced database, modelling and visualisation capabilities. LavaNet is taking advantage of VULCAN's Perl based scripting environment, Lava, to bring all the benefits of neural network development and application to geologists, mining engineers and other users of the specific mine planning package. LavaNet enables easy development of neural network training data sets using information from any of the data and model structures available, such as block models and drillhole databases. Neural networks can be trained inside VULCAN™ and the results be used to generate new models that can be visualised in 3D. Direct comparison of developed neural network models with conventional and geostatistical techniques is now possible within the same mine planning software package. LavaNet supports Radial Basis Function networks, Multi-Layer Perceptrons and Self-Organised Maps.

Creative-Dynamics Approach To Neural Intelligence

NASA Technical Reports Server (NTRS)

Zak, Michail A.

1992-01-01

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

PubMed Central

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

How Neural Networks Learn from Experience.

ERIC Educational Resources Information Center

Hinton, Geoffrey E.

1992-01-01

Discusses computational studies of learning in artificial neural networks and findings that may provide insights into the learning abilities of the human brain. Describes efforts to test theories about brain information processing, using artificial neural networks. Vignettes include information concerning how a neural network represents…

Multistability, local pattern formation, and global collective firing in a small-world network of nonleaky integrate-and-fire neurons.

PubMed

Rothkegel, Alexander; Lehnertz, Klaus

2009-03-01

We investigate numerically the collective dynamical behavior of pulse-coupled nonleaky integrate-and-fire neurons that are arranged on a two-dimensional small-world network. To ensure ongoing activity, we impose a probability for spontaneous firing for each neuron. We study network dynamics evolving from different sets of initial conditions in dependence on coupling strength and rewiring probability. Besides a homogeneous equilibrium state for low coupling strength, we observe different local patterns including cyclic waves, spiral waves, and turbulentlike patterns, which-depending on network parameters-interfere with the global collective firing of the neurons. We attribute the various network dynamics to distinct regimes in the parameter space. For the same network parameters different network dynamics can be observed depending on the set of initial conditions only. Such a multistable behavior and the interplay between local pattern formation and global collective firing may be attributable to the spatiotemporal dynamics of biological networks.

Neural network to diagnose lining condition

NASA Astrophysics Data System (ADS)

Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

2018-03-01

The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

[Measurement and performance analysis of functional neural network].

PubMed

Li, Shan; Liu, Xinyu; Chen, Yan; Wan, Hong

2018-04-01

The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.

Intelligent image capture of cartridge cases for firearms examiners

NASA Astrophysics Data System (ADS)

Jones, Brett C.; Guerci, Joseph R.

1997-02-01

The FBI's DRUGFIRETM system is a nationwide computerized networked image database of ballistic forensic evidence. This evidence includes images of cartridge cases and bullets obtained from both crime scenes and controlled test firings of seized weapons. Currently, the system is installed in over 80 forensic labs across the country and has enjoyed a high degree of success. In this paper, we discuss some of the issues and methods associated with providing a front-end semi-automated image capture system that simultaneously satisfies the often conflicting criteria of the many human examiners visual perception versus the criteria associated with optimizing autonomous digital image correlation. Specifically, we detail the proposed processing chain of an intelligent image capture system (IICS), involving a real- time capture 'assistant,' which assesses the quality of the image under test utilizing a custom designed neural network.

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Artificial and Bayesian Neural Networks

PubMed

Korhani Kangi, Azam; Bahrampour, Abbas

2018-02-26

Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively. The aim of the present study was to assess the value of an artificial neural network and a Bayesian neural network for modeling and predicting of probability of gastric cancer patient death. Materials and Methods: In this study, we used information on 339 patients aged from 20 to 90 years old with positive gastric cancer, referred to Afzalipoor and Shahid Bahonar Hospitals in Kerman City from 2001 to 2015. The three layers perceptron neural network (ANN) and the Bayesian neural network (BNN) were used for predicting the probability of mortality using the available data. To investigate differences between the models, sensitivity, specificity, accuracy and the area under receiver operating characteristic curves (AUROCs) were generated. Results: In this study, the sensitivity and specificity of the artificial neural network and Bayesian neural network models were 0.882, 0.903 and 0.954, 0.909, respectively. Prediction accuracy and the area under curve ROC for the two models were 0.891, 0.944 and 0.935, 0.961. The age at diagnosis of gastric cancer was most important for predicting survival, followed by tumor grade, morphology, gender, smoking history, opium consumption, receiving chemotherapy, presence of metastasis, tumor stage, receiving radiotherapy, and being resident in a village. Conclusion: The findings of the present study indicated that the Bayesian neural network is preferable to an artificial neural network for predicting survival of gastric cancer patients in Iran. Creative Commons Attribution License

Model Of Neural Network With Creative Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail; Barhen, Jacob

1993-01-01

Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

2017-05-01

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

Determining geophysical properties from well log data using artificial neural networks and fuzzy inference systems

NASA Astrophysics Data System (ADS)

Chang, Hsien-Cheng

Two novel synergistic systems consisting of artificial neural networks and fuzzy inference systems are developed to determine geophysical properties by using well log data. These systems are employed to improve the determination accuracy in carbonate rocks, which are generally more complex than siliciclastic rocks. One system, consisting of a single adaptive resonance theory (ART) neural network and three fuzzy inference systems (FISs), is used to determine the permeability category. The other system, which is composed of three ART neural networks and a single FIS, is employed to determine the lithofacies. The geophysical properties studied in this research, permeability category and lithofacies, are treated as categorical data. The permeability values are transformed into a "permeability category" to account for the effects of scale differences between core analyses and well logs, and heterogeneity in the carbonate rocks. The ART neural networks dynamically cluster the input data sets into different groups. The FIS is used to incorporate geologic experts' knowledge, which is usually in linguistic forms, into systems. These synergistic systems thus provide viable alternative solutions to overcome the effects of heterogeneity, the uncertainties of carbonate rock depositional environments, and the scarcity of well log data. The results obtained in this research show promising improvements over backpropagation neural networks. For the permeability category, the prediction accuracies are 68.4% and 62.8% for the multiple-single ART neural network-FIS and a single backpropagation neural network, respectively. For lithofacies, the prediction accuracies are 87.6%, 79%, and 62.8% for the single-multiple ART neural network-FIS, a single ART neural network, and a single backpropagation neural network, respectively. The sensitivity analysis results show that the multiple-single ART neural networks-FIS and a single ART neural network possess the same matching trends in determining lithofacies. This research shows that the adaptive resonance theory neural networks enable decision-makers to clearly distinguish the importance of different pieces of data which are useful in three-dimensional subsurface modeling. Geologic experts' knowledge can be easily applied and maintained by using the fuzzy inference systems.

Reducing neural network training time with parallel processing

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Lamarsh, William J., II

1995-01-01

Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

Application of the ANNA neural network chip to high-speed character recognition.

PubMed

Sackinger, E; Boser, B E; Bromley, J; Lecun, Y; Jackel, L D

1992-01-01

A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precision.

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

A neural network application to classification of health status of HIV/AIDS patients.

PubMed

Kwak, N K; Lee, C

1997-04-01

This paper presents an application of neural networks to classify and to predict the health status of HIV/AIDS patients. A neural network model in classifying both the well and not-well health status of HIV/AIDS patients is developed and evaluated in terms of validity and reliability of the test. Several different neural network topologies are applied to AIDS Cost and Utilization Survey (ACSUS) datasets in order to demonstrate the neural network's capability.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2008-01-01

An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

Improvement of the Hopfield Neural Network by MC-Adaptation Rule

NASA Astrophysics Data System (ADS)

Zhou, Zhen; Zhao, Hong

2006-06-01

We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

PubMed

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

PubMed

Lin, Chuan-Kai; Wang, Sheng-De

2004-11-01

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

PubMed

Yang, S; Wang, D

2000-01-01

This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

Financial time series prediction using spiking neural networks.

PubMed

Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

2014-01-01

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

Neural activity in the prelimbic and infralimbic cortices of freely moving rats during social interaction: Effect of isolation rearing.

PubMed

Minami, Chihiro; Shimizu, Tomoko; Mitani, Akira

2017-01-01

Sociability promotes a sound daily life for individuals. Reduced sociability is a central symptom of various neuropsychiatric disorders, and yet the neural mechanisms underlying reduced sociability remain unclear. The prelimbic cortex (PL) and infralimbic cortex (IL) have been suggested to play an important role in the neural mechanisms underlying sociability because isolation rearing in rats results in impairment of social behavior and structural changes in the PL and IL. One possible mechanism underlying reduced sociability involves dysfunction of the PL and IL. We made a wireless telemetry system to record multiunit activity in the PL and IL of pairs of freely moving rats during social interaction and examined the influence of isolation rearing on this activity. In group-reared rats, PL neurons increased firing when the rat showed approaching behavior and also contact behavior, especially when the rat attacked the partner. Conversely, IL neurons increased firing when the rat exhibited leaving behavior, especially when the partner left on its own accord. In social interaction, the PL may be involved in active actions toward others, whereas the IL may be involved in passive relief from cautionary subjects. Isolation rearing altered social behavior and neural activity. Isolation-reared rats showed an increased frequency and decreased duration of contact behavior. The increased firing of PL neurons during approaching and contact behavior, observed in group-reared rats, was preserved in isolation-reared rats, whereas the increased firing of IL neurons during leaving behavior, observed in group-reared rats, was suppressed in isolation-reared rats. This result indicates that isolation rearing differentially alters neural activity in the PL and IL during social behavior. The differential influence of isolation rearing on neural activity in the PL and IL may be one of the neural bases of isolation rearing-induced behavior.

Optimization of waste heat utilization in cold end system of thermal power station based on neural network algorithm

NASA Astrophysics Data System (ADS)

Du, Zenghui

2018-04-01

At present, the flue gas waste heat utilization projects of coal-fired boilers are often limited by low temperature corrosion problems and conventional PID control. The flue gas temperature cannot be reduced to the best efficiency temperature of wet desulphurization, resulting in the failure of heat recovery to be the maximum. Therefore, this paper analyzes, researches and solves the remaining problems of the cold end system of thermal power station, so as to provide solutions and theoretical support for energy saving and emission reduction and upgrading and the improvement of the comprehensive efficiency of the units.

Non-Intrusive Gaze Tracking Using Artificial Neural Networks

DTIC Science & Technology

1994-01-05

We have developed an artificial neural network based gaze tracking, system which can be customized to individual users. A three layer feed forward...empirical analysis of the performance of a large number of artificial neural network architectures for this task. Suggestions for further explorations...for neurally based gaze trackers are presented, and are related to other similar artificial neural network applications such as autonomous road following.

Neural dynamics based on the recognition of neural fingerprints

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2015-01-01

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. PMID:25852531

Structural reliability calculation method based on the dual neural network and direct integration method.

PubMed

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks.

PubMed

Aguiar, Manuela A D; Dias, Ana Paula S; Ferreira, Flora

2017-01-01

We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

PubMed

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

Adaptive robotic control driven by a versatile spiking cerebellar network.

PubMed

Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio

2014-01-01

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

Tracing Activity Across the Whole Brain Neural Network with Optogenetic Functional Magnetic Resonance Imaging

PubMed Central

Lee, Jin Hyung

2011-01-01

Despite the overwhelming need, there has been a relatively large gap in our ability to trace network level activity across the brain. The complex dense wiring of the brain makes it extremely challenging to understand cell-type specific activity and their communication beyond a few synapses. Recent development of the optogenetic functional magnetic resonance imaging (ofMRI) provides a new impetus for the study of brain circuits by enabling causal tracing of activities arising from defined cell types and firing patterns across the whole brain. Brain circuit elements can be selectively triggered based on their genetic identity, cell body location, and/or their axonal projection target with temporal precision while the resulting network response is monitored non-invasively with unprecedented spatial and temporal accuracy. With further studies including technological innovations to bring ofMRI to its full potential, ofMRI is expected to play an important role in our system-level understanding of the brain circuit mechanism. PMID:22046160

Closing the Loop for Memory Prostheses: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

PubMed Central

Hampson, Robert E.; Song, Dong; Chan, Rosa H.M.; Sweatt, Andrew J.; Riley, Mitchell R.; Goonawardena, Anushka V.; Marmarelis, Vasilis Z.; Gerhardt, Greg A.; Berger, Theodore W.; Deadwyler, Sam A.

2012-01-01

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatiotemporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the “strength” of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary “normal” encoding as a means of understanding how neural ensembles can be “tuned” to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry. PMID:22498704

Thermoelastic steam turbine rotor control based on neural network

NASA Astrophysics Data System (ADS)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

The use of artificial neural networks in experimental data acquisition and aerodynamic design

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1991-01-01

It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application.

PubMed

Li, Shuai; Li, Yangming; Wang, Zheng

2013-03-01

This paper presents a class of recurrent neural networks to solve quadratic programming problems. Different from most existing recurrent neural networks for solving quadratic programming problems, the proposed neural network model converges in finite time and the activation function is not required to be a hard-limiting function for finite convergence time. The stability, finite-time convergence property and the optimality of the proposed neural network for solving the original quadratic programming problem are proven in theory. Extensive simulations are performed to evaluate the performance of the neural network with different parameters. In addition, the proposed neural network is applied to solving the k-winner-take-all (k-WTA) problem. Both theoretical analysis and numerical simulations validate the effectiveness of our method for solving the k-WTA problem. Copyright © 2012 Elsevier Ltd. All rights reserved.

Satellite image analysis using neural networks

NASA Technical Reports Server (NTRS)

Sheldon, Roger A.

1990-01-01

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

PubMed

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Applications of artificial neural nets in clinical biomechanics.

PubMed

Schöllhorn, W I

2004-11-01

The purpose of this article is to provide an overview of current applications of artificial neural networks in the area of clinical biomechanics. The body of literature on artificial neural networks grew intractably vast during the last 15 years. Conventional statistical models may present certain limitations that can be overcome by neural networks. Artificial neural networks in general are introduced, some limitations, and some proven benefits are discussed.

Neural Networks for Rapid Design and Analysis

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.; Maghami, Peiman G.

1998-01-01

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Generalized Adaptive Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1993-01-01

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

Optimal input sizes for neural network de-interlacing

NASA Astrophysics Data System (ADS)

Choi, Hyunsoo; Seo, Guiwon; Lee, Chulhee

2009-02-01

Neural network de-interlacing has shown promising results among various de-interlacing methods. In this paper, we investigate the effects of input size for neural networks for various video formats when the neural networks are used for de-interlacing. In particular, we investigate optimal input sizes for CIF, VGA and HD video formats.

Impact of leakage delay on bifurcation in high-order fractional BAM neural networks.

PubMed

Huang, Chengdai; Cao, Jinde

2018-02-01

The effects of leakage delay on the dynamics of neural networks with integer-order have lately been received considerable attention. It has been confirmed that fractional neural networks more appropriately uncover the dynamical properties of neural networks, but the results of fractional neural networks with leakage delay are relatively few. This paper primarily concentrates on the issue of bifurcation for high-order fractional bidirectional associative memory(BAM) neural networks involving leakage delay. The first attempt is made to tackle the stability and bifurcation of high-order fractional BAM neural networks with time delay in leakage terms in this paper. The conditions for the appearance of bifurcation for the proposed systems with leakage delay are firstly established by adopting time delay as a bifurcation parameter. Then, the bifurcation criteria of such system without leakage delay are successfully acquired. Comparative analysis wondrously detects that the stability performance of the proposed high-order fractional neural networks is critically weakened by leakage delay, they cannot be overlooked. Numerical examples are ultimately exhibited to attest the efficiency of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Coronary Artery Diagnosis Aided by Neural Network

NASA Astrophysics Data System (ADS)

Stefko, Kamil

2007-01-01

Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. Application of optimised feed forward multi-layer back propagation neural network (MLBP) for detection of narrowing in coronary artery vessels is presented in this paper. The research was performed using 580 data records from traditional ECG exercise test confirmed by coronary arteriography results. Each record of training database included description of the state of a patient providing input data for the neural network. Level and slope of ST segment of a 12 lead ECG signal recorded at rest and after effort (48 floating point values) was the main component of input data for neural network was. Coronary arteriography results (verified the existence or absence of more than 50% stenosis of the particular coronary vessels) were used as a correct neural network training output pattern. More than 96% of cases were correctly recognised by especially optimised and a thoroughly verified neural network. Leave one out method was used for neural network verification so 580 data records could be used for training as well as for verification of neural network.

Predicate calculus for an architecture of multiple neural networks

NASA Astrophysics Data System (ADS)

Consoli, Robert H.

1990-08-01

Future projects with neural networks will require multiple individual network components. Current efforts along these lines are ad hoc. This paper relates the neural network to a classical device and derives a multi-part architecture from that model. Further it provides a Predicate Calculus variant for describing the location and nature of the trainings and suggests Resolution Refutation as a method for determining the performance of the system as well as the location of needed trainings for specific proofs. 2. THE NEURAL NETWORK AND A CLASSICAL DEVICE Recently investigators have been making reports about architectures of multiple neural networksL234. These efforts are appearing at an early stage in neural network investigations they are characterized by architectures suggested directly by the problem space. Touretzky and Hinton suggest an architecture for processing logical statements1 the design of this architecture arises from the syntax of a restricted class of logical expressions and exhibits syntactic limitations. In similar fashion a multiple neural netword arises out of a control problem2 from the sequence learning problem3 and from the domain of machine learning. 4 But a general theory of multiple neural devices is missing. More general attempts to relate single or multiple neural networks to classical computing devices are not common although an attempt is made to relate single neural devices to a Turing machines and Sun et a!. develop a multiple neural architecture that performs pattern classification.

Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

NASA Astrophysics Data System (ADS)

Kuznetsov, A. V.; Makaryants, G. M.

2018-01-01

There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

Altered Synchronizations among Neural Networks in Geriatric Depression

PubMed Central

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G.; Steffens, David C.

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression. PMID:26180795

Altered Synchronizations among Neural Networks in Geriatric Depression.

PubMed

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

A consensual neural network

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Ersoy, O. K.; Swain, P. H.

1991-01-01

A neural network architecture called a consensual neural network (CNN) is proposed for the classification of data from multiple sources. Its relation to hierarchical and ensemble neural networks is discussed. CNN is based on the statistical consensus theory and uses nonlinearly transformed input data. The input data are transformed several times, and the different transformed data are applied as if they were independent inputs. The independent inputs are classified using stage neural networks and outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote-sensing data and geographic data are given.

Neural-Network Simulator

NASA Technical Reports Server (NTRS)

Mitchell, Paul H.

1991-01-01

F77NNS (FORTRAN 77 Neural Network Simulator) computer program simulates popular back-error-propagation neural network. Designed to take advantage of vectorization when used on computers having this capability, also used on any computer equipped with ANSI-77 FORTRAN Compiler. Problems involving matching of patterns or mathematical modeling of systems fit class of problems F77NNS designed to solve. Program has restart capability so neural network solved in stages suitable to user's resources and desires. Enables user to customize patterns of connections between layers of network. Size of neural network F77NNS applied to limited only by amount of random-access memory available to user.

Feedback modulation of neural network synchrony and seizure susceptibility by Mdm2-p53-Nedd4-2 signaling.

PubMed

Jewett, Kathryn A; Christian, Catherine A; Bacos, Jonathan T; Lee, Kwan Young; Zhu, Jiuhe; Tsai, Nien-Pei

2016-03-22

Neural network synchrony is a critical factor in regulating information transmission through the nervous system. Improperly regulated neural network synchrony is implicated in pathophysiological conditions such as epilepsy. Despite the awareness of its importance, the molecular signaling underlying the regulation of neural network synchrony, especially after stimulation, remains largely unknown. In this study, we show that elevation of neuronal activity by the GABA(A) receptor antagonist, Picrotoxin, increases neural network synchrony in primary mouse cortical neuron cultures. The elevation of neuronal activity triggers Mdm2-dependent degradation of the tumor suppressor p53. We show here that blocking the degradation of p53 further enhances Picrotoxin-induced neural network synchrony, while promoting the inhibition of p53 with a p53 inhibitor reduces Picrotoxin-induced neural network synchrony. These data suggest that Mdm2-p53 signaling mediates a feedback mechanism to fine-tune neural network synchrony after activity stimulation. Furthermore, genetically reducing the expression of a direct target gene of p53, Nedd4-2, elevates neural network synchrony basally and occludes the effect of Picrotoxin. Finally, using a kainic acid-induced seizure model in mice, we show that alterations of Mdm2-p53-Nedd4-2 signaling affect seizure susceptibility. Together, our findings elucidate a critical role of Mdm2-p53-Nedd4-2 signaling underlying the regulation of neural network synchrony and seizure susceptibility and reveal potential therapeutic targets for hyperexcitability-associated neurological disorders.

L-type calcium channels refine the neural population code of sound level

PubMed Central

Grimsley, Calum Alex; Green, David Brian

2016-01-01

The coding of sound level by ensembles of neurons improves the accuracy with which listeners identify how loud a sound is. In the auditory system, the rate at which neurons fire in response to changes in sound level is shaped by local networks. Voltage-gated conductances alter local output by regulating neuronal firing, but their role in modulating responses to sound level is unclear. We tested the effects of L-type calcium channels (CaL: CaV1.1–1.4) on sound-level coding in the central nucleus of the inferior colliculus (ICC) in the auditory midbrain. We characterized the contribution of CaL to the total calcium current in brain slices and then examined its effects on rate-level functions (RLFs) in vivo using single-unit recordings in awake mice. CaL is a high-threshold current and comprises ∼50% of the total calcium current in ICC neurons. In vivo, CaL activates at sound levels that evoke high firing rates. In RLFs that increase monotonically with sound level, CaL boosts spike rates at high sound levels and increases the maximum firing rate achieved. In different populations of RLFs that change nonmonotonically with sound level, CaL either suppresses or enhances firing at sound levels that evoke maximum firing. CaL multiplies the gain of monotonic RLFs with dynamic range and divides the gain of nonmonotonic RLFs with the width of the RLF. These results suggest that a single broad class of calcium channels activates enhancing and suppressing local circuits to regulate the sensitivity of neuronal populations to sound level. PMID:27605536

Advances in memory research: single-neuron recordings from the human medial temporal lobe aid our understanding of declarative memory.

PubMed

Viskontas, Indre V

2008-12-01

To gain a complete understanding of how the brain functions, both in illness and good health, data from multiple levels of analysis must be integrated. Technical advances have made direct recordings of neuronal activity deep inside the human brain tractable, providing a rare glimpse into cellular processes during long-term memory formation. Recent findings using intracranial recordings in the medial temporal lobe inform current neural network models of memory, and may lead to a more comprehensive understanding of the neural basis of memory-related processes. These recordings have shown that cells in the hippocampus appear to support declarative learning by distinguishing novel and familiar stimuli via changes in firing patterns. Some cells with highly selective and invariant responses have also been described, and these responses seem to represent abstract concepts such as identity, rather than superficial perceptual features of items. Importantly, however, both selective and globally responsive cells are capable of changing their preferred stimulus depending on the conscious demands of the task. Firing patterns of human medial temporal lobe neurons indicate that cells can be both plastic and stable in terms of the information that they code; although some cells show highly selective and reproducible excitatory responses when presented with a familiar object, other cells change their receptive fields in line with changes in experience and the cognitive environment.

Precision and reliability of periodically and quasiperiodically driven integrate-and-fire neurons.

PubMed

Tiesinga, P H E

2002-04-01

Neurons in the brain communicate via trains of all-or-none electric events known as spikes. How the brain encodes information using spikes-the neural code-remains elusive. Here the robustness against noise of stimulus-induced neural spike trains is studied in terms of attractors and bifurcations. The dynamics of model neurons converges after a transient onto an attractor yielding a reproducible sequence of spike times. At a bifurcation point the spike times on the attractor change discontinuously when a parameter is varied. Reliability, the stability of the attractor against noise, is reduced when the neuron operates close to a bifurcation point. We determined using analytical spike-time maps the attractor and bifurcation structure of an integrate-and-fire model neuron driven by a periodic or a quasiperiodic piecewise constant current and investigated the stability of attractors against noise. The integrate-and-fire model neuron became mode locked to the periodic current with a rational winding number p/q and produced p spikes per q cycles. There were q attractors. p:q mode-locking regions formed Arnold tongues. In the model, reliability was the highest during 1:1 mode locking when there was only one attractor, as was also observed in recent experiments. The quasiperiodically driven neuron mode locked to either one of the two drive periods, or to a linear combination of both of them. Mode-locking regions were organized in Arnold tongues and reliability was again highest when there was only one attractor. These results show that neuronal reliability in response to the rhythmic drive generated by synchronized networks of neurons is profoundly influenced by the location of the Arnold tongues in parameter space.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Space-Time Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James A.; Shelton, Robert O.

1992-01-01

Concept of space-time neural network affords distributed temporal memory enabling such network to model complicated dynamical systems mathematically and to recognize temporally varying spatial patterns. Digital filters replace synaptic-connection weights of conventional back-error-propagation neural network.

Financial Time Series Prediction Using Spiking Neural Networks

PubMed Central

Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

2014-01-01

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. PMID:25170618

Qualitative analysis of Cohen-Grossberg neural networks with multiple delays

NASA Astrophysics Data System (ADS)

Ye, Hui; Michel, Anthony N.; Wang, Kaining

1995-03-01

It is well known that a class of artificial neural networks with symmetric interconnections and without transmission delays, known as Cohen-Grossberg neural networks, possesses global stability (i.e., all trajectories tend to some equilibrium). We demonstrate in the present paper that many of the qualitative properties of Cohen-Grossberg networks will not be affected by the introduction of sufficiently small delays. Specifically, we establish some bound conditions for the time delays under which a given Cohen-Grossberg network with multiple delays is globally stable and possesses the same asymptotically stable equilibria as the corresponding network without delays. An effective method of determining the asymptotic stability of an equilibrium of a Cohen-Grossberg network with multiple delays is also presented. The present results are motivated by some of the authors earlier work [Phys. Rev. E 50, 4206 (1994)] and by some of the work of Marcus and Westervelt [Phys. Rev. A 39, 347 (1989)]. These works address qualitative analyses of Hopfield neural networks with one time delay. The present work generalizes these results to Cohen-Grossberg neural networks with multiple time delays. Hopfield neural networks constitute special cases of Cohen-Grossberg neural networks.

Dynamic Neural Networks Supporting Memory Retrieval

PubMed Central

St. Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

2011-01-01

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) Medial Prefrontal Cortex (PFC) Network, associated with self-referential processes, 2) Medial Temporal Lobe (MTL) Network, associated with memory, 3) Frontoparietal Network, associated with strategic search, and 4) Cingulooperculum Network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior. PMID:21550407

A social network approach to understanding science communication among fire professionals

Treesearch

Vita Wright

2012-01-01

Studies of science communication and use in the fire management community suggest manager's access research via informal information networks and that these networks vary by both agency and position. We used a phone survey followed by traditional statistical analyses to understand the informal social networks of fire professionals in two western regions of the...

A social network approach to understanding science communication among fire professionals (Abstract)

Treesearch

Vita Wright; Andrea Thode; Anne Mottek-Lucas; Jacklynn Fallon; Megan Matonis

2012-01-01

Studies of science communication and use in the fire management community suggest manager's access research via informal information networks and that these networks vary by both agency and position. We used a phone survey followed by traditional statistical analyses to understand the informal social networks of fire professionals in two western regions of the...

Coherence resonance in bursting neural networks

NASA Astrophysics Data System (ADS)

Kim, June Hoan; Lee, Ho Jun; Min, Cheol Hong; Lee, Kyoung J.

2015-10-01

Synchronized neural bursts are one of the most noticeable dynamic features of neural networks, being essential for various phenomena in neuroscience, yet their complex dynamics are not well understood. With extrinsic electrical and optical manipulations on cultured neural networks, we demonstrate that the regularity (or randomness) of burst sequences is in many cases determined by a (few) low-dimensional attractor(s) working under strong neural noise. Moreover, there is an optimal level of noise strength at which the regularity of the interburst interval sequence becomes maximal—a phenomenon of coherence resonance. The experimental observations are successfully reproduced through computer simulations on a well-established neural network model, suggesting that the same phenomena may occur in many in vivo as well as in vitro neural networks.

Enhancing fire science exchange: The Northern Rockies Fire Science Network [poster

Treesearch

Vita Wright

2011-01-01

The Joint Fire Science Program is developing a national network of knowledge exchange consortia comprised of interested management and science stakeholders working together to tailor and actively demonstrate existing fire science information to benefit management.

Classification of Respiratory Sounds by Using An Artificial Neural Network

DTIC Science & Technology

2001-10-28

CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK M.C. Sezgin, Z. Dokur, T. Ölmez, M. Korürek Department of Electronics and...successfully classified by the GAL network. Keywords-Respiratory Sounds, Classification of Biomedical Signals, Artificial Neural Network . I. INTRODUCTION...process, feature extraction, and classification by the artificial neural network . At first, the RS signal obtained from a real-time measurement equipment is

Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

DTIC Science & Technology

1987-10-01

include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

A neural net approach to space vehicle guidance

NASA Technical Reports Server (NTRS)

Caglayan, Alper K.; Allen, Scott M.

1990-01-01

The space vehicle guidance problem is formulated using a neural network approach, and the appropriate neural net architecture for modeling optimum guidance trajectories is investigated. In particular, an investigation is made of the incorporation of prior knowledge about the characteristics of the optimal guidance solution into the neural network architecture. The online classification performance of the developed network is demonstrated using a synthesized network trained with a database of optimum guidance trajectories. Such a neural-network-based guidance approach can readily adapt to environment uncertainties such as those encountered by an AOTV during atmospheric maneuvers.

Neural network and its application to CT imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nikravesh, M.; Kovscek, A.R.; Patzek, T.W.

We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are (I) visualization of the distribution of oil and air saturation by CT, (II) interpretation of CT scans using neural networks, and (III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. Excellent agreement between the actual images and the neural network predictions is found.

Electronic neural networks for global optimization

NASA Technical Reports Server (NTRS)

Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.

1990-01-01

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

NASA Technical Reports Server (NTRS)

Harrington, Peter DEB.; Zheng, Peng

1995-01-01

Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

Newly developed double neural network concept for reliable fast plasma position control

NASA Astrophysics Data System (ADS)

Jeon, Young-Mu; Na, Yong-Su; Kim, Myung-Rak; Hwang, Y. S.

2001-01-01

Neural network is considered as a parameter estimation tool in plasma controls for next generation tokamak such as ITER. The neural network has been reported to be so accurate and fast for plasma equilibrium identification that it may be applied to the control of complex tokamak plasmas. For this application, the reliability of the conventional neural network needs to be improved. In this study, a new idea of double neural network is developed to achieve this. The new idea has been applied to simple plasma position identification of KSTAR tokamak for feasibility test. Characteristics of the concept show higher reliability and fault tolerance even in severe faulty conditions, which may make neural network applicable to plasma control reliably and widely in future tokamaks.

Rule extraction from minimal neural networks for credit card screening.

PubMed

Setiono, Rudy; Baesens, Bart; Mues, Christophe

2011-08-01

While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.

512-Channel and 13-Region Simultaneous Recordings Coupled with Optogenetic Manipulation in Freely Behaving Mice

PubMed Central

Xie, Kun; Fox, Grace E.; Liu, Jun; Tsien, Joe Z.

2016-01-01

The development of technologies capable of recording both single-unit activity and local field potentials (LFPs) over a wide range of brain circuits in freely behaving animals is the key to constructing brain activity maps. Although mice are the most popular mammalian genetic model, in vivo neural recording has been traditionally limited to smaller channel count and fewer brain structures because of the mouse’s small size and thin skull. Here, we describe a 512-channel tetrode system that allows us to record simultaneously over a dozen cortical and subcortical structures in behaving mice. This new technique offers two major advantages – namely, the ultra-low cost and the do-it-yourself flexibility for targeting any combination of many brain areas. We show the successful recordings of both single units and LFPs from 13 distinct neural circuits of the mouse brain, including subregions of the anterior cingulate cortices, retrosplenial cortices, somatosensory cortices, secondary auditory cortex, hippocampal CA1, dentate gyrus, subiculum, lateral entorhinal cortex, perirhinal cortex, and prelimbic cortex. This 512-channel system can also be combined with Cre-lox neurogenetics and optogenetics to further examine interactions between genes, cell types, and circuit dynamics across a wide range of brain structures. Finally, we demonstrate that complex stimuli – such as an earthquake and fear-inducing foot-shock – trigger firing changes in all of the 13 brain regions recorded, supporting the notion that neural code is highly distributed. In addition, we show that localized optogenetic manipulation in any given brain region could disrupt network oscillations and caused changes in single-unit firing patterns in a brain-wide manner, thereby raising the cautionary note of the interpretation of optogenetically manipulated behaviors. PMID:27378865

Understanding the transmission of wildfire risk on a fire prone landscape - A Case study from Central Oregon

NASA Astrophysics Data System (ADS)

Ager, Alan; Barros, Ana; Day, Michelle; Preisler, Haiganoush; Evers, Cody

2015-04-01

We develop the idea of risk transmission from large wildfires and apply network analyses to understand its importance within the 3.2 million ha Fire-People-Forest study area in central Oregon, US. Historic wildfires within the study and elsewhere in the western US frequently burn over long distances (e.g., 20-50 km) through highly fragmented landscapes with respect to ownership, fuels, management intensity, population density, and ecological conditions. The collective arrangement of fuel loadings in concert with weather and suppression efforts ultimately determines containment and the resulting fire perimeter. While spatial interactions among land parcels in terms of fire spread and intensity have been frequently noted by fire managers, quantifying risk and exposure transmission is not well understood. In this paper we used simulation modeling to quantify wildfire transmission and built a transmission network among and within land owners and communities within the study area. The results suggested that 84% of the predicted area burned within the 25 communities in the study area was from simulated fires that ignited on federal lands. The wildland urban interface surrounding the communities was predicted to burn at a rate of 2 % per year, with 57% of the area burned from fires ignited on federal lands. The node degree for communities indicated that simulated fires originated on about 6 different landowners. Network analyses in general revealed independent variation in transmitted fire among landowners in terms of both node degree (diversity of landowners exchanging fire) and transmitted fire, indicating that both the spatial grain of land ownership and wildfire topology contribute to transmission among land parcels. We discuss how network analyses of wildfire transmission can inform fire management goals for creating fire adapted communities, conserving biodiversity, and resolving competing demands for fire-prone ecosystem services. We also discuss how biophysical fire networks can potentially be coupled with social fire networks to improve wildfire mitigation planning.

An improved wavelet neural network medical image segmentation algorithm with combined maximum entropy

NASA Astrophysics Data System (ADS)

Hu, Xiaoqian; Tao, Jinxu; Ye, Zhongfu; Qiu, Bensheng; Xu, Jinzhang

2018-05-01

In order to solve the problem of medical image segmentation, a wavelet neural network medical image segmentation algorithm based on combined maximum entropy criterion is proposed. Firstly, we use bee colony algorithm to optimize the network parameters of wavelet neural network, get the parameters of network structure, initial weights and threshold values, and so on, we can quickly converge to higher precision when training, and avoid to falling into relative extremum; then the optimal number of iterations is obtained by calculating the maximum entropy of the segmented image, so as to achieve the automatic and accurate segmentation effect. Medical image segmentation experiments show that the proposed algorithm can reduce sample training time effectively and improve convergence precision, and segmentation effect is more accurate and effective than traditional BP neural network (back propagation neural network : a multilayer feed forward neural network which trained according to the error backward propagation algorithm.

Knowledge extraction from evolving spiking neural networks with rank order population coding.

PubMed

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Excitation of lateral habenula neurons as a neural mechanism underlying ethanol-induced conditioned taste aversion.

PubMed

Tandon, Shashank; Keefe, Kristen A; Taha, Sharif A

2017-02-15

The lateral habenula (LHb) has been implicated in regulation of drug-seeking behaviours through aversion-mediated learning. In this study, we recorded neuronal activity in the LHb of rats during an operant task before and after ethanol-induced conditioned taste aversion (CTA) to saccharin. Ethanol-induced CTA caused significantly higher baseline firing rates in LHb neurons, as well as elevated firing rates in response to cue presentation, lever press and saccharin taste. In a separate cohort of rats, we found that bilateral LHb lesions blocked ethanol-induced CTA. Our results strongly suggest that excitation of LHb neurons is required for ethanol-induced CTA, and point towards a mechanism through which LHb firing may regulate voluntary ethanol consumption. Ethanol, like other drugs of abuse, has both rewarding and aversive properties. Previous work suggests that sensitivity to ethanol's aversive effects negatively modulates voluntary alcohol intake and thus may be important in vulnerability to developing alcohol use disorders. We previously found that rats with lesions of the lateral habenula (LHb), which is implicated in aversion-mediated learning, show accelerated escalation of voluntary ethanol consumption. To understand neural encoding in the LHb contributing to ethanol-induced aversion, we recorded neural firing in the LHb of freely behaving, water-deprived rats before and after an ethanol-induced (1.5 g kg -1 20% ethanol, i.p.) conditioned taste aversion (CTA) to saccharin taste. Ethanol-induced CTA strongly decreased motivation for saccharin in an operant task to obtain the tastant. Comparison of LHb neural firing before and after CTA induction revealed four main differences in firing properties. First, baseline firing after CTA induction was significantly higher. Second, firing evoked by cues signalling saccharin availability shifted from a pattern of primarily inhibition before CTA to primarily excitation after CTA induction. Third, CTA induction reduced the magnitude of lever press-evoked inhibition. Finally, firing rates were significantly higher during consumption of the devalued saccharin solution after CTA induction. Next, we studied sham- and LHb-lesioned rats in our operant CTA paradigm and found that LHb lesion significantly attenuated CTA effects in the operant task. Our data demonstrate the importance of LHb excitation in regulating expression of ethanol-induced aversion and suggest a mechanism for its role in modulating escalation of voluntary ethanol intake. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Excitation of lateral habenula neurons as a neural mechanism underlying ethanol‐induced conditioned taste aversion

PubMed Central

Keefe, Kristen A.; Taha, Sharif A.

2016-01-01

Key points The lateral habenula (LHb) has been implicated in regulation of drug‐seeking behaviours through aversion‐mediated learning.In this study, we recorded neuronal activity in the LHb of rats during an operant task before and after ethanol‐induced conditioned taste aversion (CTA) to saccharin.Ethanol‐induced CTA caused significantly higher baseline firing rates in LHb neurons, as well as elevated firing rates in response to cue presentation, lever press and saccharin taste.In a separate cohort of rats, we found that bilateral LHb lesions blocked ethanol‐induced CTA.Our results strongly suggest that excitation of LHb neurons is required for ethanol‐induced CTA, and point towards a mechanism through which LHb firing may regulate voluntary ethanol consumption. Abstract Ethanol, like other drugs of abuse, has both rewarding and aversive properties. Previous work suggests that sensitivity to ethanol's aversive effects negatively modulates voluntary alcohol intake and thus may be important in vulnerability to developing alcohol use disorders. We previously found that rats with lesions of the lateral habenula (LHb), which is implicated in aversion‐mediated learning, show accelerated escalation of voluntary ethanol consumption. To understand neural encoding in the LHb contributing to ethanol‐induced aversion, we recorded neural firing in the LHb of freely behaving, water‐deprived rats before and after an ethanol‐induced (1.5 g kg−1 20% ethanol, i.p.) conditioned taste aversion (CTA) to saccharin taste. Ethanol‐induced CTA strongly decreased motivation for saccharin in an operant task to obtain the tastant. Comparison of LHb neural firing before and after CTA induction revealed four main differences in firing properties. First, baseline firing after CTA induction was significantly higher. Second, firing evoked by cues signalling saccharin availability shifted from a pattern of primarily inhibition before CTA to primarily excitation after CTA induction. Third, CTA induction reduced the magnitude of lever press‐evoked inhibition. Finally, firing rates were significantly higher during consumption of the devalued saccharin solution after CTA induction. Next, we studied sham‐ and LHb‐lesioned rats in our operant CTA paradigm and found that LHb lesion significantly attenuated CTA effects in the operant task. Our data demonstrate the importance of LHb excitation in regulating expression of ethanol‐induced aversion and suggest a mechanism for its role in modulating escalation of voluntary ethanol intake. PMID:27682823

Multi-scale controls of historical forest-fire regimes: new insights from fire-scar networks

Treesearch

Donald A. Falk; Emily K. Heyerdahl; Peter M. Brown; Calvin Farris; Peter Z. Fule; Donald McKenzie; Thomas W. Swetnam; Alan H. Taylor; Megan L. Van Horne

2011-01-01

Anticipating future forest-fire regimes under changing climate requires that scientists and natural resource managers understand the factors that control fire across space and time. Fire scars—proxy records of fires, formed in the growth rings of long-lived trees—provide an annually accurate window into past low-severity fire regimes. In western North America, networks...

An algorithm of the wildfire classification by its acoustic emission spectrum using Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Khamukhin, A. A.; Demin, A. Y.; Sonkin, D. M.; Bertoldo, S.; Perona, G.; Kretova, V.

2017-01-01

Crown fires are extremely dangerous as the speed of their distribution is dozen times higher compared to surface fires. Therefore, it is important to classify the fire type as early as possible. A method for forest fires classification exploits their computed acoustic emission spectrum compared with a set of samples of the typical fire acoustic emission spectrum stored in the database. This method implies acquisition acoustic data using Wireless Sensors Networks (WSNs) and their analysis in a central processing and a control center. The paper deals with an algorithm which can be directly implemented on a sensor network node that will allow reducing considerably the network traffic and increasing its efficiency. It is hereby suggested to use the sum of the squares ratio, with regard to amplitudes of low and high frequencies of the wildfire acoustic emission spectrum, as the indicator of a forest fire type. It is shown that the value of the crown fires indicator is several times higher than that of the surface ones. This allows classifying the fire types (crown, surface) in a short time interval and transmitting a fire type indicator code alongside with an alarm signal through the network.

Continuous attractor network models of grid cell firing based on excitatory–inhibitory interactions

PubMed Central

Shipston‐Sharman, Oliver; Solanka, Lukas

2016-01-01

Abstract Neurons in the medial entorhinal cortex encode location through spatial firing fields that have a grid‐like organisation. The challenge of identifying mechanisms for grid firing has been addressed through experimental and theoretical investigations of medial entorhinal circuits. Here, we discuss evidence for continuous attractor network models that account for grid firing by synaptic interactions between excitatory and inhibitory cells. These models assume that grid‐like firing patterns are the result of computation of location from velocity inputs, with additional spatial input required to oppose drift in the attractor state. We focus on properties of continuous attractor networks that are revealed by explicitly considering excitatory and inhibitory neurons, their connectivity and their membrane potential dynamics. Models at this level of detail can account for theta‐nested gamma oscillations as well as grid firing, predict spatial firing of interneurons as well as excitatory cells, show how gamma oscillations can be modulated independently from spatial computations, reveal critical roles for neuronal noise, and demonstrate that only a subset of excitatory cells in a network need have grid‐like firing fields. Evaluating experimental data against predictions from detailed network models will be important for establishing the mechanisms mediating grid firing. PMID:27870120

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location

PubMed Central

Crago, Patrick E; Makowski, Nathan S

2014-01-01

Objective Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. Approach We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Main Results Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases.. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. Significance This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation. PMID:25161163

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location

NASA Astrophysics Data System (ADS)

Crago, Patrick E.; Makowski, Nathaniel S.

2014-10-01

Objective. Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. Approach. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Main results. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. Significance. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

Midbrain adaptation may set the stage for the perception of musical beat

PubMed Central

2017-01-01

The ability to spontaneously feel a beat in music is a phenomenon widely believed to be unique to humans. Though beat perception involves the coordinated engagement of sensory, motor and cognitive processes in humans, the contribution of low-level auditory processing to the activation of these networks in a beat-specific manner is poorly understood. Here, we present evidence from a rodent model that midbrain preprocessing of sounds may already be shaping where the beat is ultimately felt. For the tested set of musical rhythms, on-beat sounds on average evoked higher firing rates than off-beat sounds, and this difference was a defining feature of the set of beat interpretations most commonly perceived by human listeners over others. Basic firing rate adaptation provided a sufficient explanation for these results. Our findings suggest that midbrain adaptation, by encoding the temporal context of sounds, creates points of neural emphasis that may influence the perceptual emergence of a beat. PMID:29118141

Using Grid Cells for Navigation.

PubMed

Bush, Daniel; Barry, Caswell; Manson, Daniel; Burgess, Neil

2015-08-05

Mammals are able to navigate to hidden goal locations by direct routes that may traverse previously unvisited terrain. Empirical evidence suggests that this "vector navigation" relies on an internal representation of space provided by the hippocampal formation. The periodic spatial firing patterns of grid cells in the hippocampal formation offer a compact combinatorial code for location within large-scale space. Here, we consider the computational problem of how to determine the vector between start and goal locations encoded by the firing of grid cells when this vector may be much longer than the largest grid scale. First, we present an algorithmic solution to the problem, inspired by the Fourier shift theorem. Second, we describe several potential neural network implementations of this solution that combine efficiency of search and biological plausibility. Finally, we discuss the empirical predictions of these implementations and their relationship to the anatomy and electrophysiology of the hippocampal formation. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Midbrain adaptation may set the stage for the perception of musical beat.

PubMed

Rajendran, Vani G; Harper, Nicol S; Garcia-Lazaro, Jose A; Lesica, Nicholas A; Schnupp, Jan W H

2017-11-15

The ability to spontaneously feel a beat in music is a phenomenon widely believed to be unique to humans. Though beat perception involves the coordinated engagement of sensory, motor and cognitive processes in humans, the contribution of low-level auditory processing to the activation of these networks in a beat-specific manner is poorly understood. Here, we present evidence from a rodent model that midbrain preprocessing of sounds may already be shaping where the beat is ultimately felt. For the tested set of musical rhythms, on-beat sounds on average evoked higher firing rates than off-beat sounds, and this difference was a defining feature of the set of beat interpretations most commonly perceived by human listeners over others. Basic firing rate adaptation provided a sufficient explanation for these results. Our findings suggest that midbrain adaptation, by encoding the temporal context of sounds, creates points of neural emphasis that may influence the perceptual emergence of a beat. © 2017 The Authors.

Adaptive neural network motion control of manipulators with experimental evaluations.

PubMed

Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

PubMed Central

Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910