Normalization is a general neural mechanism for context-dependent decision making

PubMed Central

Louie, Kenway; Khaw, Mel W.; Glimcher, Paul W.

2013-01-01

Understanding the neural code is critical to linking brain and behavior. In sensory systems, divisive normalization seems to be a canonical neural computation, observed in areas ranging from retina to cortex and mediating processes including contrast adaptation, surround suppression, visual attention, and multisensory integration. Recent electrophysiological studies have extended these insights beyond the sensory domain, demonstrating an analogous algorithm for the value signals that guide decision making, but the effects of normalization on choice behavior are unknown. Here, we show that choice models using normalization generate significant (and classically irrational) choice phenomena driven by either the value or number of alternative options. In value-guided choice experiments, both monkey and human choosers show novel context-dependent behavior consistent with normalization. These findings suggest that the neural mechanism of value coding critically influences stochastic choice behavior and provide a generalizable quantitative framework for examining context effects in decision making. PMID:23530203

Semantic representations in the temporal pole predict false memories

PubMed Central

Chadwick, Martin J.; Anjum, Raeesa S.; Kumaran, Dharshan; Schacter, Daniel L.; Spiers, Hugo J.; Hassabis, Demis

2016-01-01

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the “semantic hub” of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories. PMID:27551087

Semantic representations in the temporal pole predict false memories.

PubMed

Chadwick, Martin J; Anjum, Raeesa S; Kumaran, Dharshan; Schacter, Daniel L; Spiers, Hugo J; Hassabis, Demis

2016-09-06

Recent advances in neuroscience have given us unprecedented insight into the neural mechanisms of false memory, showing that artificial memories can be inserted into the memory cells of the hippocampus in a way that is indistinguishable from true memories. However, this alone is not enough to explain how false memories can arise naturally in the course of our daily lives. Cognitive psychology has demonstrated that many instances of false memory, both in the laboratory and the real world, can be attributed to semantic interference. Whereas previous studies have found that a diverse set of regions show some involvement in semantic false memory, none have revealed the nature of the semantic representations underpinning the phenomenon. Here we use fMRI with representational similarity analysis to search for a neural code consistent with semantic false memory. We find clear evidence that false memories emerge from a similarity-based neural code in the temporal pole, a region that has been called the "semantic hub" of the brain. We further show that each individual has a partially unique semantic code within the temporal pole, and this unique code can predict idiosyncratic patterns of memory errors. Finally, we show that the same neural code can also predict variation in true-memory performance, consistent with an adaptive perspective on false memory. Taken together, our findings reveal the underlying structure of neural representations of semantic knowledge, and how this semantic structure can both enhance and distort our memories.

Neural Codes for One's Own Position and Direction in a Real-World "Vista" Environment.

PubMed

Sulpizio, Valentina; Boccia, Maddalena; Guariglia, Cecilia; Galati, Gaspare

2018-01-01

Humans, like animals, rely on an accurate knowledge of one's spatial position and facing direction to keep orientated in the surrounding space. Although previous neuroimaging studies demonstrated that scene-selective regions (the parahippocampal place area or PPA, the occipital place area or OPA and the retrosplenial complex or RSC), and the hippocampus (HC) are implicated in coding position and facing direction within small-(room-sized) and large-scale navigational environments, little is known about how these regions represent these spatial quantities in a large open-field environment. Here, we used functional magnetic resonance imaging (fMRI) in humans to explore the neural codes of these navigationally-relevant information while participants viewed images which varied for position and facing direction within a familiar, real-world circular square. We observed neural adaptation for repeated directions in the HC, even if no navigational task was required. Further, we found that the amount of knowledge of the environment interacts with the PPA selectivity in encoding positions: individuals who needed more time to memorize positions in the square during a preliminary training task showed less neural attenuation in this scene-selective region. We also observed adaptation effects, which reflect the real distances between consecutive positions, in scene-selective regions but not in the HC. When examining the multi-voxel patterns of activity we observed that scene-responsive regions and the HC encoded both spatial information and that the RSC classification accuracy for positions was higher in individuals scoring higher to a self-reported questionnaire of spatial abilities. Our findings provide new insight into how the human brain represents a real, large-scale "vista" space, demonstrating the presence of neural codes for position and direction in both scene-selective and hippocampal regions, and revealing the existence, in the former regions, of a map-like spatial representation reflecting real-world distance between consecutive positions.

Video data compression using artificial neural network differential vector quantization

NASA Technical Reports Server (NTRS)

Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

1991-01-01

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

Neural network system for purposeful behavior based on foveal visual preprocessor

NASA Astrophysics Data System (ADS)

Golovan, Alexander V.; Shevtsova, Natalia A.; Klepatch, Arkadi A.

1996-10-01

Biologically plausible model of the system with an adaptive behavior in a priori environment and resistant to impairment has been developed. The system consists of input, learning, and output subsystems. The first subsystems classifies input patterns presented as n-dimensional vectors in accordance with some associative rule. The second one being a neural network determines adaptive responses of the system to input patterns. Arranged neural groups coding possible input patterns and appropriate output responses are formed during learning by means of negative reinforcement. Output subsystem maps a neural network activity into the system behavior in the environment. The system developed has been studied by computer simulation imitating a collision-free motion of a mobile robot. After some learning period the system 'moves' along a road without collisions. It is shown that in spite of impairment of some neural network elements the system functions reliably after relearning. Foveal visual preprocessor model developed earlier has been tested to form a kind of visual input to the system.

Space-time adaptive decision feedback neural receivers with data selection for high-data-rate users in DS-CDMA systems.

PubMed

de Lamare, Rodrigo C; Sampaio-Neto, Raimundo

2008-11-01

A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes.

Optical Processing.

DTIC Science & Technology

1985-12-31

34 Optics Letters, 2 (1), 1-3 (1978). 7. Grossberg, S., "Adaptive Resonance in Development, Perception and Cognition ," SIAM-AMS Proc., 13, 107-156...Illusions," Biol. Cybernetics, 23, 187-202, (1976b). 11. Grossberg, S., "How Does A Brain Build a Cognitive Code?", Psychol. Review, 87 (1), 1-51 (1980...34perceptron" (F. Rosenblatt, Principles of Neurodynamics ), workers in the neural network field have been seeking to understand how neural networks can perform

Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load.

PubMed

Holper, L; Van Brussel, L D; Schmidt, L; Schulthess, S; Burke, C J; Louie, K; Seifritz, E; Tobler, P N

2017-01-01

Adaptation facilitates neural representation of a wide range of diverse inputs, including reward values. Adaptive value coding typically relies on contextual information either obtained from the environment or retrieved from and maintained in memory. However, it is unknown whether having to retrieve and maintain context information modulates the brain's capacity for value adaptation. To address this issue, we measured hemodynamic responses of the prefrontal cortex (PFC) in two studies on risky decision-making. In each trial, healthy human subjects chose between a risky and a safe alternative; half of the participants had to remember the risky alternatives, whereas for the other half they were presented visually. The value of safe alternatives varied across trials. PFC responses adapted to contextual risk information, with steeper coding of safe alternative value in lower-risk contexts. Importantly, this adaptation depended on working memory load, such that response functions relating PFC activity to safe values were steeper with presented versus remembered risk. An independent second study replicated the findings of the first study and showed that similar slope reductions also arose when memory maintenance demands were increased with a secondary working memory task. Formal model comparison showed that a divisive normalization model fitted effects of both risk context and working memory demands on PFC activity better than alternative models of value adaptation, and revealed that reduced suppression of background activity was the critical parameter impairing normalization with increased memory maintenance demand. Our findings suggest that mnemonic processes can constrain normalization of neural value representations.

Adaptation to stimulus statistics in the perception and neural representation of auditory space.

PubMed

Dahmen, Johannes C; Keating, Peter; Nodal, Fernando R; Schulz, Andreas L; King, Andrew J

2010-06-24

Sensory systems are known to adapt their coding strategies to the statistics of their environment, but little is still known about the perceptual implications of such adjustments. We investigated how auditory spatial processing adapts to stimulus statistics by presenting human listeners and anesthetized ferrets with noise sequences in which interaural level differences (ILD) rapidly fluctuated according to a Gaussian distribution. The mean of the distribution biased the perceived laterality of a subsequent stimulus, whereas the distribution's variance changed the listeners' spatial sensitivity. The responses of neurons in the inferior colliculus changed in line with these perceptual phenomena. Their ILD preference adjusted to match the stimulus distribution mean, resulting in large shifts in rate-ILD functions, while their gain adapted to the stimulus variance, producing pronounced changes in neural sensitivity. Our findings suggest that processing of auditory space is geared toward emphasizing relative spatial differences rather than the accurate representation of absolute position.

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

PubMed

Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

2018-01-01

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Holistic neural coding of Chinese character forms in bilateral ventral visual system.

PubMed

Mo, Ce; Yu, Mengxia; Seger, Carol; Mo, Lei

2015-02-01

How are Chinese characters recognized and represented in the brain of skilled readers? Functional MRI fast adaptation technique was used to address this question. We found that neural adaptation effects were limited to identical characters in bilateral ventral visual system while no activation reduction was observed for partially overlapping characters regardless of the spatial location of the shared sub-character components, suggesting highly selective neuronal tuning to whole characters. The consistent neural profile across the entire ventral visual cortex indicates that Chinese characters are represented as mutually distinctive wholes rather than combinations of sub-character components, which presents a salient contrast to the left-lateralized, simple-to-complex neural representations of alphabetic words. Our findings thus revealed the cultural modulation effect on both local neuronal activity patterns and functional anatomical regions associated with written symbol recognition. Moreover, the cross-language discrepancy in written symbol recognition mechanism might stem from the language-specific early-stage learning experience. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Feature-selective Attention in Frontoparietal Cortex: Multivoxel Codes Adjust to Prioritize Task-relevant Information.

PubMed

Jackson, Jade; Rich, Anina N; Williams, Mark A; Woolgar, Alexandra

2017-02-01

Human cognition is characterized by astounding flexibility, enabling us to select appropriate information according to the objectives of our current task. A circuit of frontal and parietal brain regions, often referred to as the frontoparietal attention network or multiple-demand (MD) regions, are believed to play a fundamental role in this flexibility. There is evidence that these regions dynamically adjust their responses to selectively process information that is currently relevant for behavior, as proposed by the "adaptive coding hypothesis" [Duncan, J. An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2, 820-829, 2001]. Could this provide a neural mechanism for feature-selective attention, the process by which we preferentially process one feature of a stimulus over another? We used multivariate pattern analysis of fMRI data during a perceptually challenging categorization task to investigate whether the representation of visual object features in the MD regions flexibly adjusts according to task relevance. Participants were trained to categorize visually similar novel objects along two orthogonal stimulus dimensions (length/orientation) and performed short alternating blocks in which only one of these dimensions was relevant. We found that multivoxel patterns of activation in the MD regions encoded the task-relevant distinctions more strongly than the task-irrelevant distinctions: The MD regions discriminated between stimuli of different lengths when length was relevant and between the same objects according to orientation when orientation was relevant. The data suggest a flexible neural system that adjusts its representation of visual objects to preferentially encode stimulus features that are currently relevant for behavior, providing a neural mechanism for feature-selective attention.

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: I dynamics.

PubMed

Zheng, Lei; Nikolaev, Anton; Wardill, Trevor J; O'Kane, Cahir J; de Polavieja, Gonzalo G; Juusola, Mikko

2009-01-01

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1-R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II).

Network Adaptation Improves Temporal Representation of Naturalistic Stimuli in Drosophila Eye: I Dynamics

PubMed Central

Wardill, Trevor J.; O'Kane, Cahir J.; de Polavieja, Gonzalo G.; Juusola, Mikko

2009-01-01

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1–R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II). PMID:19180196

Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load

PubMed Central

Burke, C. J.; Seifritz, E.; Tobler, P. N.

2017-01-01

Abstract Adaptation facilitates neural representation of a wide range of diverse inputs, including reward values. Adaptive value coding typically relies on contextual information either obtained from the environment or retrieved from and maintained in memory. However, it is unknown whether having to retrieve and maintain context information modulates the brain’s capacity for value adaptation. To address this issue, we measured hemodynamic responses of the prefrontal cortex (PFC) in two studies on risky decision-making. In each trial, healthy human subjects chose between a risky and a safe alternative; half of the participants had to remember the risky alternatives, whereas for the other half they were presented visually. The value of safe alternatives varied across trials. PFC responses adapted to contextual risk information, with steeper coding of safe alternative value in lower-risk contexts. Importantly, this adaptation depended on working memory load, such that response functions relating PFC activity to safe values were steeper with presented versus remembered risk. An independent second study replicated the findings of the first study and showed that similar slope reductions also arose when memory maintenance demands were increased with a secondary working memory task. Formal model comparison showed that a divisive normalization model fitted effects of both risk context and working memory demands on PFC activity better than alternative models of value adaptation, and revealed that reduced suppression of background activity was the critical parameter impairing normalization with increased memory maintenance demand. Our findings suggest that mnemonic processes can constrain normalization of neural value representations. PMID:28462394

Neural coding of time-varying interaural time differences and time-varying amplitude in the inferior colliculus

PubMed Central

2017-01-01

Binaural cues occurring in natural environments are frequently time varying, either from the motion of a sound source or through interactions between the cues produced by multiple sources. Yet, a broad understanding of how the auditory system processes dynamic binaural cues is still lacking. In the current study, we directly compared neural responses in the inferior colliculus (IC) of unanesthetized rabbits to broadband noise with time-varying interaural time differences (ITD) with responses to noise with sinusoidal amplitude modulation (SAM) over a wide range of modulation frequencies. On the basis of prior research, we hypothesized that the IC, one of the first stages to exhibit tuning of firing rate to modulation frequency, might use a common mechanism to encode time-varying information in general. Instead, we found weaker temporal coding for dynamic ITD compared with amplitude modulation and stronger effects of adaptation for amplitude modulation. The differences in temporal coding of dynamic ITD compared with SAM at the single-neuron level could be a neural correlate of “binaural sluggishness,” the inability to perceive fluctuations in time-varying binaural cues at high modulation frequencies, for which a physiological explanation has so far remained elusive. At ITD-variation frequencies of 64 Hz and above, where a temporal code was less effective, noise with a dynamic ITD could still be distinguished from noise with a constant ITD through differences in average firing rate in many neurons, suggesting a frequency-dependent tradeoff between rate and temporal coding of time-varying binaural information. NEW & NOTEWORTHY Humans use time-varying binaural cues to parse auditory scenes comprising multiple sound sources and reverberation. However, the neural mechanisms for doing so are poorly understood. Our results demonstrate a potential neural correlate for the reduced detectability of fluctuations in time-varying binaural information at high speeds, as occurs in reverberation. The results also suggest that the neural mechanisms for processing time-varying binaural and monaural cues are largely distinct. PMID:28381487

Attention enhances multi-voxel representation of novel objects in frontal, parietal and visual cortices.

PubMed

Woolgar, Alexandra; Williams, Mark A; Rich, Anina N

2015-04-01

Selective attention is fundamental for human activity, but the details of its neural implementation remain elusive. One influential theory, the adaptive coding hypothesis (Duncan, 2001, An adaptive coding model of neural function in prefrontal cortex, Nature Reviews Neuroscience 2:820-829), proposes that single neurons in certain frontal and parietal regions dynamically adjust their responses to selectively encode relevant information. This selective representation may in turn support selective processing in more specialized brain regions such as the visual cortices. Here, we use multi-voxel decoding of functional magnetic resonance images to demonstrate selective representation of attended--and not distractor--objects in frontal, parietal, and visual cortices. In addition, we highlight a critical role for task demands in determining which brain regions exhibit selective coding. Strikingly, representation of attended objects in frontoparietal cortex was highest under conditions of high perceptual demand, when stimuli were hard to perceive and coding in early visual cortex was weak. Coding in early visual cortex varied as a function of attention and perceptual demand, while coding in higher visual areas was sensitive to the allocation of attention but robust to changes in perceptual difficulty. Consistent with high-profile reports, peripherally presented objects could also be decoded from activity at the occipital pole, a region which corresponds to the fovea. Our results emphasize the flexibility of frontoparietal and visual systems. They support the hypothesis that attention enhances the multi-voxel representation of information in the brain, and suggest that the engagement of this attentional mechanism depends critically on current task demands. Copyright © 2015 Elsevier Inc. All rights reserved.

Understanding the Implications of Neural Population Activity on Behavior

NASA Astrophysics Data System (ADS)

Briguglio, John

Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests a variety of hypotheses that will be useful in helping to understand the relationship between neural activity and behavior as recorded neural populations continue to grow.

A stimulus-dependent spike threshold is an optimal neural coder

PubMed Central

Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama

2015-01-01

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710

Statistical coding and decoding of heartbeat intervals.

PubMed

Lucena, Fausto; Barros, Allan Kardec; Príncipe, José C; Ohnishi, Noboru

2011-01-01

The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

Flexible categorization of relative stimulus strength by the optic tectum

PubMed Central

Mysore, Shreesh P.; Knudsen, Eric I.

2011-01-01

Categorization is the process by which the brain segregates continuously variable stimuli into discrete groups. We report that patterns of neural population activity in the owl optic tectum (OT) categorize stimuli based on their relative strengths into “strongest” versus “other”. The category boundary shifts adaptively to track changes in the absolute strength of the strongest stimulus. This population-wide categorization is mediated by the responses of a small subset of neurons. Our data constitute the first direct demonstration of an explicit categorization of stimuli by a neural network based on relative stimulus strength or salience. The finding of categorization by the population code relaxes constraints on the properties of downstream decoders that might read out the location of the strongest stimulus. These results indicate that the ensemble neural code in the OT could mediate bottom-up stimulus selection for gaze and attention, a form of stimulus categorization in which the category boundary often shifts within hundreds of milliseconds. PMID:21613487

An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources.

PubMed

Doulamis, A D; Doulamis, N D; Kollias, S D

2003-01-01

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

PubMed Central

Bharioke, Arjun; Chklovskii, Dmitri B.

2015-01-01

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs. PMID:26247884

Visual adaptation and face perception

PubMed Central

Webster, Michael A.; MacLeod, Donald I. A.

2011-01-01

The appearance of faces can be strongly affected by the characteristics of faces viewed previously. These perceptual after-effects reflect processes of sensory adaptation that are found throughout the visual system, but which have been considered only relatively recently in the context of higher level perceptual judgements. In this review, we explore the consequences of adaptation for human face perception, and the implications of adaptation for understanding the neural-coding schemes underlying the visual representation of faces. The properties of face after-effects suggest that they, in part, reflect response changes at high and possibly face-specific levels of visual processing. Yet, the form of the after-effects and the norm-based codes that they point to show many parallels with the adaptations and functional organization that are thought to underlie the encoding of perceptual attributes like colour. The nature and basis for human colour vision have been studied extensively, and we draw on ideas and principles that have been developed to account for norms and normalization in colour vision to consider potential similarities and differences in the representation and adaptation of faces. PMID:21536555

Visual adaptation and face perception.

PubMed

Webster, Michael A; MacLeod, Donald I A

2011-06-12

The appearance of faces can be strongly affected by the characteristics of faces viewed previously. These perceptual after-effects reflect processes of sensory adaptation that are found throughout the visual system, but which have been considered only relatively recently in the context of higher level perceptual judgements. In this review, we explore the consequences of adaptation for human face perception, and the implications of adaptation for understanding the neural-coding schemes underlying the visual representation of faces. The properties of face after-effects suggest that they, in part, reflect response changes at high and possibly face-specific levels of visual processing. Yet, the form of the after-effects and the norm-based codes that they point to show many parallels with the adaptations and functional organization that are thought to underlie the encoding of perceptual attributes like colour. The nature and basis for human colour vision have been studied extensively, and we draw on ideas and principles that have been developed to account for norms and normalization in colour vision to consider potential similarities and differences in the representation and adaptation of faces.

Fast converging minimum probability of error neural network receivers for DS-CDMA communications.

PubMed

Matyjas, John D; Psaromiligkos, Ioannis N; Batalama, Stella N; Medley, Michael J

2004-03-01

We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

Aeroelasticity of morphing wings using neural networks

NASA Astrophysics Data System (ADS)

Natarajan, Anand

In this dissertation, neural networks are designed to effectively model static non-linear aeroelastic problems in adaptive structures and linear dynamic aeroelastic systems with time varying stiffness. The use of adaptive materials in aircraft wings allows for the change of the contour or the configuration of a wing (morphing) in flight. The use of smart materials, to accomplish these deformations, can imply that the stiffness of the wing with a morphing contour changes as the contour changes. For a rapidly oscillating body in a fluid field, continuously adapting structural parameters may render the wing to behave as a time variant system. Even the internal spars/ribs of the aircraft wing which define the wing stiffness can be made adaptive, that is, their stiffness can be made to vary with time. The immediate effect on the structural dynamics of the wing, is that, the wing motion is governed by a differential equation with time varying coefficients. The study of this concept of a time varying torsional stiffness, made possible by the use of active materials and adaptive spars, in the dynamic aeroelastic behavior of an adaptable airfoil is performed here. Another type of aeroelastic problem of an adaptive structure that is investigated here, is the shape control of an adaptive bump situated on the leading edge of an airfoil. Such a bump is useful in achieving flow separation control for lateral directional maneuverability of the aircraft. Since actuators are being used to create this bump on the wing surface, the energy required to do so needs to be minimized. The adverse pressure drag as a result of this bump needs to be controlled so that the loss in lift over the wing is made minimal. The design of such a "spoiler bump" on the surface of the airfoil is an optimization problem of maximizing pressure drag due to flow separation while minimizing the loss in lift and energy required to deform the bump. One neural network is trained using the CFD code FLUENT to represent the aerodynamic loading over the bump. A second neural network is trained for calculating the actuator loads, bump displacement and lift, drag forces over the airfoil using the finite element solver, ANSYS and the previously trained neural network. This non-linear aeroelastic model of the deforming bump on an airfoil surface using neural networks can serve as a fore-runner for other non-linear aeroelastic problems.

Parallel Coding of First- and Second-Order Stimulus Attributes by Midbrain Electrosensory Neurons

PubMed Central

McGillivray, Patrick; Vonderschen, Katrin; Fortune, Eric S.; Chacron, Maurice J.

2015-01-01

Natural stimuli often have time-varying first-order (i.e., mean) and second-order (i.e., variance) attributes that each carry critical information for perception and can vary independently over orders of magnitude. Experiments have shown that sensory systems continuously adapt their responses based on changes in each of these attributes. This adaptation creates ambiguity in the neural code as multiple stimuli may elicit the same neural response. While parallel processing of first- and second-order attributes by separate neural pathways is sufficient to remove this ambiguity, the existence of such pathways and the neural circuits that mediate their emergence have not been uncovered to date. We recorded the responses of midbrain electrosensory neurons in the weakly electric fish Apteronotus leptorhynchus to stimuli with first- and second-order attributes that varied independently in time. We found three distinct groups of midbrain neurons: the first group responded to both first- and second-order attributes, the second group responded selectively to first-order attributes, and the last group responded selectively to second-order attributes. In contrast, all afferent hindbrain neurons responded to both first- and second-order attributes. Using computational analyses, we show how inputs from a heterogeneous population of ON- and OFF-type afferent neurons are combined to give rise to response selectivity to either first- or second-order stimulus attributes in midbrain neurons. Our study thus uncovers, for the first time, generic and widely applicable mechanisms by which parallel processing of first- and second-order stimulus attributes emerges in the brain. PMID:22514313

Evolutionary Construction of Block-Based Neural Networks in Consideration of Failure

NASA Astrophysics Data System (ADS)

Takamori, Masahito; Koakutsu, Seiichi; Hamagami, Tomoki; Hirata, Hironori

In this paper we propose a modified gene coding and an evolutionary construction in consideration of failure in evolutionary construction of Block-Based Neural Networks. In the modified gene coding, we arrange the genes of weights on a chromosome in consideration of the position relation of the genes of weight and structure. By the modified gene coding, the efficiency of search by crossover is increased. Thereby, it is thought that improvement of the convergence rate of construction and shortening of construction time can be performed. In the evolutionary construction in consideration of failure, the structure which is adapted for failure is built in the state where failure occured. Thereby, it is thought that BBNN can be reconstructed in a short time at the time of failure. To evaluate the proposed method, we apply it to pattern classification and autonomous mobile robot control problems. The computational experiments indicate that the proposed method can improve convergence rate of construction and shorten of construction and reconstruction time.

The tactile speed aftereffect depends on the speed of adapting motion across the skin rather than other spatiotemporal features

PubMed Central

Seizova-Cajic, Tatjana; Holcombe, Alex O.

2015-01-01

After prolonged exposure to a surface moving across the skin, this felt movement appears slower, a phenomenon known as the tactile speed aftereffect (tSAE). We asked which feature of the adapting motion drives the tSAE: speed, the spacing between texture elements, or the frequency with which they cross the skin. After adapting to a ridged moving surface with one hand, participants compared the speed of test stimuli on adapted and unadapted hands. We used surfaces with different spatial periods (SPs; 3, 6, 12 mm) that produced adapting motion with different combinations of adapting speed (20, 40, 80 mm/s) and temporal frequency (TF; 3.4, 6.7, 13.4 ridges/s). The primary determinant of tSAE magnitude was speed of the adapting motion, not SP or TF. This suggests that adaptation occurs centrally, after speed has been computed from SP and TF, and/or that it reflects a speed cue independent of those features in the first place (e.g., indentation force). In a second experiment, we investigated the properties of the neural code for speed. Speed tuning predicts that adaptation should be greatest for speeds at or near the adapting speed. However, the tSAE was always stronger when the adapting stimulus was faster (242 mm/s) than the test (30–143 mm/s) compared with when the adapting and test speeds were matched. These results give no indication of speed tuning and instead suggest that adaptation occurs at a level where an intensive code dominates. In an intensive code, the faster the stimulus, the more the neurons fire. PMID:26631149

Circuit Motifs for Contrast-Adaptive Differentiation in Early Sensory Systems: The Role of Presynaptic Inhibition and Short-Term Plasticity

PubMed Central

Zhang, Danke; Wu, Si; Rasch, Malte J.

2015-01-01

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems. PMID:25723493

Circuit motifs for contrast-adaptive differentiation in early sensory systems: the role of presynaptic inhibition and short-term plasticity.

PubMed

Zhang, Danke; Wu, Si; Rasch, Malte J

2015-01-01

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.

What the success of brain imaging implies about the neural code.

PubMed

Guest, Olivia; Love, Bradley C

2017-01-19

The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of the neural code and ventral stream, as well as what can be successfully investigated with fMRI.

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.

Neural Signatures of Phonetic Learning in Adulthood: A Magnetoencephalography Study

PubMed Central

Zhang, Yang; Kuhl, Patricia K.; Imada, Toshiaki; Iverson, Paul; Pruitt, John; Stevens, Erica B.; Kawakatsu, Masaki; Tohkura, Yoh'ichi; Nemoto, Iku

2010-01-01

The present study used magnetoencephalography (MEG) to examine perceptual learning of American English /r/ and /l/ categories by Japanese adults who had limited English exposure. A training software program was developed based on the principles of infant phonetic learning, featuring systematic acoustic exaggeration, multi-talker variability, visible articulation, and adaptive listening. The program was designed to help Japanese listeners utilize an acoustic dimension relevant for phonemic categorization of /r-l/ in English. Although training did not produce native-like phonetic boundary along the /r-l/ synthetic continuum in the second language learners, success was seen in highly significant identification improvement over twelve training sessions and transfer of learning to novel stimuli. Consistent with behavioral results, pre-post MEG measures showed not only enhanced neural sensitivity to the /r-l/ distinction in the left-hemisphere mismatch field (MMF) response but also bilateral decreases in equivalent current dipole (ECD) cluster and duration measures for stimulus coding in the inferior parietal region. The learning-induced increases in neural sensitivity and efficiency were also found in distributed source analysis using Minimum Current Estimates (MCE). Furthermore, the pre-post changes exhibited significant brain-behavior correlations between speech discrimination scores and MMF amplitudes as well as between the behavioral scores and ECD measures of neural efficiency. Together, the data provide corroborating evidence that substantial neural plasticity for second-language learning in adulthood can be induced with adaptive and enriched linguistic exposure. Like the MMF, the ECD cluster and duration measures are sensitive neural markers of phonetic learning. PMID:19457395

Value is in the eye of the beholder: early visual cortex codes monetary value of objects during a diverted attention task.

PubMed

Persichetti, Andrew S; Aguirre, Geoffrey K; Thompson-Schill, Sharon L

2015-05-01

A central concern in the study of learning and decision-making is the identification of neural signals associated with the values of choice alternatives. An important factor in understanding the neural correlates of value is the representation of the object itself, separate from the act of choosing. Is it the case that the representation of an object within visual areas will change if it is associated with a particular value? We used fMRI adaptation to measure the neural similarity of a set of novel objects before and after participants learned to associate monetary values with the objects. We used a range of both positive and negative values to allow us to distinguish effects of behavioral salience (i.e., large vs. small values) from effects of valence (i.e., positive vs. negative values). During the scanning session, participants made a perceptual judgment unrelated to value. Crucially, the similarity of the visual features of any pair of objects did not predict the similarity of their value, so we could distinguish adaptation effects due to each dimension of similarity. Within early visual areas, we found that value similarity modulated the neural response to the objects after training. These results show that an abstract dimension, in this case, monetary value, modulates neural response to an object in visual areas of the brain even when attention is diverted.

What the success of brain imaging implies about the neural code

PubMed Central

Guest, Olivia; Love, Bradley C

2017-01-01

The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI’s limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI’s successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of the neural code and ventral stream, as well as what can be successfully investigated with fMRI. DOI: http://dx.doi.org/10.7554/eLife.21397.001 PMID:28103186

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

FUEL-FLEXIBLE GASIFICATION-COMBUSTION TECHNOLOGY FOR PRODUCTION OF H2 AND SEQUESTRATION-READY CO2

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

George Rizeq; Janice West; Arnaldo Frydman

Further development of a combustion Large Eddy Simulation (LES) code for the design of advanced gaseous combustion systems is described in this sixth quarterly report. CFD Research Corporation (CFDRC) is developing the LES module within the parallel, unstructured solver included in the commercial CFD-ACE+ software. In this quarter, in-situ adaptive tabulation (ISAT) for efficient chemical rate storage and retrieval was implemented and tested within the Linear Eddy Model (LEM). ISAT type 3 is being tested so that extrapolation can be performed and further improve the retrieval rate. Further testing of the LEM for subgrid chemistry was performed for parallel applicationsmore » and for multi-step chemistry. Validation of the software on backstep and bluff-body reacting cases were performed. Initial calculations of the SimVal experiment at Georgia Tech using their LES code were performed. Georgia Tech continues the effort to parameterize the LEM over composition space so that a neural net can be used efficiently in the combustion LES code. A new and improved Artificial Neural Network (ANN), with log-transformed output, for the 1-step chemistry was implemented in CFDRC's LES code and gave reasonable results. This quarter, the 2nd consortium meeting was held at CFDRC. Next quarter, LES software development and testing will continue. Alpha testing of the code will continue to be performed on cases of interest to the industrial consortium. Optimization of subgrid models will be pursued, particularly with the ISAT approach. Also next quarter, the demonstration of the neural net approach, for multi-step chemical kinetics speed-up in CFD-ACE+, will be accomplished.« less

Auditory spatial processing in the human cortex.

PubMed

Salminen, Nelli H; Tiitinen, Hannu; May, Patrick J C

2012-12-01

The auditory system codes spatial locations in a way that deviates from the spatial representations found in other modalities. This difference is especially striking in the cortex, where neurons form topographical maps of visual and tactile space but where auditory space is represented through a population rate code. In this hemifield code, sound source location is represented in the activity of two widely tuned opponent populations, one tuned to the right and the other to the left side of auditory space. Scientists are only beginning to uncover how this coding strategy adapts to various spatial processing demands. This review presents the current understanding of auditory spatial processing in the cortex. To this end, the authors consider how various implementations of the hemifield code may exist within the auditory cortex and how these may be modulated by the stimulation and task context. As a result, a coherent set of neural strategies for auditory spatial processing emerges.

The Dynamic Range Paradox: A Central Auditory Model of Intensity Change Detection

PubMed Central

Simpson, Andrew J.R.; Reiss, Joshua D.

2013-01-01

In this paper we use empirical loudness modeling to explore a perceptual sub-category of the dynamic range problem of auditory neuroscience. Humans are able to reliably report perceived intensity (loudness), and discriminate fine intensity differences, over a very large dynamic range. It is usually assumed that loudness and intensity change detection operate upon the same neural signal, and that intensity change detection may be predicted from loudness data and vice versa. However, while loudness grows as intensity is increased, improvement in intensity discrimination performance does not follow the same trend and so dynamic range estimations of the underlying neural signal from loudness data contradict estimations based on intensity just-noticeable difference (JND) data. In order to account for this apparent paradox we draw on recent advances in auditory neuroscience. We test the hypothesis that a central model, featuring central adaptation to the mean loudness level and operating on the detection of maximum central-loudness rate of change, can account for the paradoxical data. We use numerical optimization to find adaptation parameters that fit data for continuous-pedestal intensity change detection over a wide dynamic range. The optimized model is tested on a selection of equivalent pseudo-continuous intensity change detection data. We also report a supplementary experiment which confirms the modeling assumption that the detection process may be modeled as rate-of-change. Data are obtained from a listening test (N = 10) using linearly ramped increment-decrement envelopes applied to pseudo-continuous noise with an overall level of 33 dB SPL. Increments with half-ramp durations between 5 and 50,000 ms are used. The intensity JND is shown to increase towards long duration ramps (p<10−6). From the modeling, the following central adaptation parameters are derived; central dynamic range of 0.215 sones, 95% central normalization, and a central loudness JND constant of 5.5×10−5 sones per ms. Through our findings, we argue that loudness reflects peripheral neural coding, and the intensity JND reflects central neural coding. PMID:23536749

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

PubMed Central

Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

2018-01-01

The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research. PMID:29599739

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.

PubMed

Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

2018-01-01

The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior.

PubMed

Panzeri, Stefano; Harvey, Christopher D; Piasini, Eugenio; Latham, Peter E; Fellin, Tommaso

2017-02-08

The two basic processes underlying perceptual decisions-how neural responses encode stimuli, and how they inform behavioral choices-have mainly been studied separately. Thus, although many spatiotemporal features of neural population activity, or "neural codes," have been shown to carry sensory information, it is often unknown whether the brain uses these features for perception. To address this issue, we propose a new framework centered on redefining the neural code as the neural features that carry sensory information used by the animal to drive appropriate behavior; that is, the features that have an intersection between sensory and choice information. We show how this framework leads to a new statistical analysis of neural activity recorded during behavior that can identify such neural codes, and we discuss how to combine intersection-based analysis of neural recordings with intervention on neural activity to determine definitively whether specific neural activity features are involved in a task. Copyright © 2017 Elsevier Inc. All rights reserved.

Time course of dynamic range adaptation in the auditory nerve

PubMed Central

Wang, Grace I.; Dean, Isabel; Delgutte, Bertrand

2012-01-01

Auditory adaptation to sound-level statistics occurs as early as in the auditory nerve (AN), the first stage of neural auditory processing. In addition to firing rate adaptation characterized by a rate decrement dependent on previous spike activity, AN fibers show dynamic range adaptation, which is characterized by a shift of the rate-level function or dynamic range toward the most frequently occurring levels in a dynamic stimulus, thereby improving the precision of coding of the most common sound levels (Wen B, Wang GI, Dean I, Delgutte B. J Neurosci 29: 13797–13808, 2009). We investigated the time course of dynamic range adaptation by recording from AN fibers with a stimulus in which the sound levels periodically switch from one nonuniform level distribution to another (Dean I, Robinson BL, Harper NS, McAlpine D. J Neurosci 28: 6430–6438, 2008). Dynamic range adaptation occurred rapidly, but its exact time course was difficult to determine directly from the data because of the concomitant firing rate adaptation. To characterize the time course of dynamic range adaptation without the confound of firing rate adaptation, we developed a phenomenological “dual adaptation” model that accounts for both forms of AN adaptation. When fitted to the data, the model predicts that dynamic range adaptation occurs as rapidly as firing rate adaptation, over 100–400 ms, and the time constants of the two forms of adaptation are correlated. These findings suggest that adaptive processing in the auditory periphery in response to changes in mean sound level occurs rapidly enough to have significant impact on the coding of natural sounds. PMID:22457465

Tactile detection of slip: surface microgeometry and peripheral neural codes.

PubMed

Srinivasan, M A; Whitehouse, J M; LaMotte, R H

1990-06-01

1. The role of the microgeometry of planar surfaces in the detection of sliding of the surfaces on human and monkey fingerpads was investigated. By the use of a servo-controlled tactile stimulator to press and stroke glass plates on passive fingerpads of human subjects, the ability of humans to discriminate the direction of skin stretch caused by friction and to detect the sliding motion (slip) of the plates with or without micrometer-sized surface features was determined. To identify the associated peripheral neural codes, evoked responses to the same stimuli were recorded from single, low-threshold mechanoreceptive afferent fibers innervating the fingerpads of anesthetized macaque monkeys. 2. Humans could not detect the slip of a smooth glass plate on the fingerpad. However, the direction of skin stretch was perceived based on the information conveyed by the slowly adapting afferents that respond differentially to the stretch directions. Whereas the direction of skin stretch signaled the direction of impending slip, the perception of relative motion between the plate and the finger required the existence of detectable surface features. 3. Barely detectable micrometer-sized protrusions on smooth surfaces led to the detection of slip of these surfaces, because of the exclusive activation of rapidly adapting fibers of either the Meissner (RA) or the Pacinian (PC) type to specific geometries of the microfeatures. The motion of a smooth plate with a very small single raised dot (4 microns high, 550 microns diam) caused the sequential activation of neighboring RAs along the dot path, thus providing a reliable spatiotemporal code. The stroking of the plate with a fine homogeneous texture composed of a matrix of dots (1 microns high, 50 microns diam, and spaced at 100 microns center-to-center) induced vibrations in the fingerpad that activated only the PCs and resulted in an intensive code. 4. The results show that surprisingly small features on smooth surfaces are detected by humans and lead to the detection of slip of these surfaces, with the geometry of the microfeatures governing the associated neural codes. When the surface features are of sizes greater than the response thresholds of all the receptors, redundant spatiotemporal and intensive information is available for the detection of slip.

Adaptation in Coding by Large Populations of Neurons in the Retina

NASA Astrophysics Data System (ADS)

Ioffe, Mark L.

A comprehensive theory of neural computation requires an understanding of the statistical properties of the neural population code. The focus of this work is the experimental study and theoretical analysis of the statistical properties of neural activity in the tiger salamander retina. This is an accessible yet complex system, for which we control the visual input and record from a substantial portion--greater than a half--of the ganglion cell population generating the spiking output. Our experiments probe adaptation of the retina to visual statistics: a central feature of sensory systems which have to adjust their limited dynamic range to a far larger space of possible inputs. In Chapter 1 we place our work in context with a brief overview of the relevant background. In Chapter 2 we describe the experimental methodology of recording from 100+ ganglion cells in the tiger salamander retina. In Chapter 3 we first present the measurements of adaptation of individual cells to changes in stimulation statistics and then investigate whether pairwise correlations in fluctuations of ganglion cell activity change across different stimulation conditions. We then transition to a study of the population-level probability distribution of the retinal response captured with maximum-entropy models. Convergence of the model inference is presented in Chapter 4. In Chapter 5 we first test the empirical presence of a phase transition in such models fitting the retinal response to different experimental conditions, and then proceed to develop other characterizations which are sensitive to complexity in the interaction matrix. This includes an analysis of the dynamics of sampling at finite temperature, which demonstrates a range of subtle attractor-like properties in the energy landscape. These are largely conserved when ambient illumination is varied 1000-fold, a result not necessarily apparent from the measured low-order statistics of the distribution. Our results form a consistent picture which is discussed at the end of Chapter 5. We conclude with a few future directions related to this thesis.

Emergence of Adaptive Computation by Single Neurons in the Developing Cortex

PubMed Central

Famulare, Michael; Gjorgjieva, Julijana; Moody, William J.

2013-01-01

Adaptation is a fundamental computational motif in neural processing. To maintain stable perception in the face of rapidly shifting input, neural systems must extract relevant information from background fluctuations under many different contexts. Many neural systems are able to adjust their input–output properties such that an input's ability to trigger a response depends on the size of that input relative to its local statistical context. This “gain-scaling” strategy has been shown to be an efficient coding strategy. We report here that this property emerges during early development as an intrinsic property of single neurons in mouse sensorimotor cortex, coinciding with the disappearance of spontaneous waves of network activity, and can be modulated by changing the balance of spike-generating currents. Simultaneously, developing neurons move toward a common intrinsic operating point and a stable ratio of spike-generating currents. This developmental trajectory occurs in the absence of sensory input or spontaneous network activity. Through a combination of electrophysiology and modeling, we demonstrate that developing cortical neurons develop the ability to perform nearly perfect gain scaling by virtue of the maturing spike-generating currents alone. We use reduced single neuron models to identify the conditions for this property to hold. PMID:23884925

The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

A Nonsynonymous Mutation in the Transcriptional Regulator lbh Is Associated with Cichlid Craniofacial Adaptation and Neural Crest Cell Development

PubMed Central

Powder, Kara E.; Cousin, Hélène; McLinden, Gretchen P.; Craig Albertson, R.

2014-01-01

Since the time of Darwin, biologists have sought to understand the origins and maintenance of life’s diversity of form. However, the nature of the exact DNA mutations and molecular mechanisms that result in morphological differences between species remains unclear. Here, we characterize a nonsynonymous mutation in a transcriptional coactivator, limb bud and heart homolog (lbh), which is associated with adaptive variation in the lower jaw of cichlid fishes. Using both zebrafish and Xenopus, we demonstrate that lbh mediates migration of cranial neural crest cells, the cellular source of the craniofacial skeleton. A single amino acid change that is alternatively fixed in cichlids with differing facial morphologies results in discrete shifts in migration patterns of this multipotent cell type that are consistent with both embryological and adult craniofacial phenotypes. Among animals, this polymorphism in lbh represents a rare example of a coding change that is associated with continuous morphological variation. This work offers novel insights into the development and evolution of the craniofacial skeleton, underscores the evolutionary potential of neural crest cells, and extends our understanding of the genetic nature of mutations that underlie divergence in complex phenotypes. PMID:25234704

Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

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

Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.

In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetrymore » with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural net approach it is possible to reduce the rate counts used to unfold the neutron spectrum. To evaluate these codes a computer tool called Neutron Spectrometry and dosimetry computer tool was designed. The results obtained with this package are showed. The codes here mentioned are freely available upon request to the authors.« less

Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

NASA Astrophysics Data System (ADS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2013-07-01

In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural net approach it is possible to reduce the rate counts used to unfold the neutron spectrum. To evaluate these codes a computer tool called Neutron Spectrometry and dosimetry computer tool was designed. The results obtained with this package are showed. The codes here mentioned are freely available upon request to the authors.

Adaptive Control of Visually Guided Grasping in Neural Networks

DTIC Science & Technology

1990-03-12

D.P. Shankweiler, M. Studdert-Kennedy (1967) Perception of the speech code, Psychol. Rev. 74, 43 1. J. Piaget ( 1952 ), The Origins of Intelligence in...Coordination, IEEE Control Systems Magazine.V9:3 p.25-30 Piaget , J. ( 1952 ), The Origins of Intelligence in Children, translated by M.Cook, (International...University Press, New York. Piaget , J. (1954) The Construction of Reality in the Child, Translated by M. Cook , Ballentine Books, New York - 24-

Visual information processing II; Proceedings of the Meeting, Orlando, FL, Apr. 14-16, 1993

NASA Technical Reports Server (NTRS)

Huck, Friedrich O. (Editor); Juday, Richard D. (Editor)

1993-01-01

Various papers on visual information processing are presented. Individual topics addressed include: aliasing as noise, satellite image processing using a hammering neural network, edge-detetion method using visual perception, adaptive vector median filters, design of a reading test for low-vision image warping, spatial transformation architectures, automatic image-enhancement method, redundancy reduction in image coding, lossless gray-scale image compression by predictive GDF, information efficiency in visual communication, optimizing JPEG quantization matrices for different applications, use of forward error correction to maintain image fidelity, effect of peanoscanning on image compression. Also discussed are: computer vision for autonomous robotics in space, optical processor for zero-crossing edge detection, fractal-based image edge detection, simulation of the neon spreading effect by bandpass filtering, wavelet transform (WT) on parallel SIMD architectures, nonseparable 2D wavelet image representation, adaptive image halftoning based on WT, wavelet analysis of global warming, use of the WT for signal detection, perfect reconstruction two-channel rational filter banks, N-wavelet coding for pattern classification, simulation of image of natural objects, number-theoretic coding for iconic systems.

A Computational Model of a Descending Mechanosensory Pathway Involved in Active Tactile Sensing

PubMed Central

Ache, Jan M.; Dürr, Volker

2015-01-01

Many animals, including humans, rely on active tactile sensing to explore the environment and negotiate obstacles, especially in the dark. Here, we model a descending neural pathway that mediates short-latency proprioceptive information from a tactile sensor on the head to thoracic neural networks. We studied the nocturnal stick insect Carausius morosus, a model organism for the study of adaptive locomotion, including tactually mediated reaching movements. Like mammals, insects need to move their tactile sensors for probing the environment. Cues about sensor position and motion are therefore crucial for the spatial localization of tactile contacts and the coordination of fast, adaptive motor responses. Our model explains how proprioceptive information about motion and position of the antennae, the main tactile sensors in insects, can be encoded by a single type of mechanosensory afferents. Moreover, it explains how this information is integrated and mediated to thoracic neural networks by a diverse population of descending interneurons (DINs). First, we quantified responses of a DIN population to changes in antennal position, motion and direction of movement. Using principal component (PC) analysis, we find that only two PCs account for a large fraction of the variance in the DIN response properties. We call the two-dimensional space spanned by these PCs ‘coding-space’ because it captures essential features of the entire DIN population. Second, we model the mechanoreceptive input elements of this descending pathway, a population of proprioceptive mechanosensory hairs monitoring deflection of the antennal joints. Finally, we propose a computational framework that can model the response properties of all important DIN types, using the hair field model as its only input. This DIN model is validated by comparison of tuning characteristics, and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population. This reveals the versatility of the framework for modelling a complete descending neural pathway. PMID:26158851

Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

NASA Technical Reports Server (NTRS)

Gupta, Pramod; Loparo, Kenneth; Mackall, Dale; Schumann, Johann; Soares, Fola

2004-01-01

Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

Synaptic E-I Balance Underlies Efficient Neural Coding.

PubMed

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Synaptic E-I Balance Underlies Efficient Neural Coding

PubMed Central

Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding. PMID:29456491

Dynamics and Adaptive Control for Stability Recovery of Damaged Aircraft

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Krishnakumar, Kalmanje; Kaneshige, John; Nespeca, Pascal

2006-01-01

This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in off-nominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid direct-indirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive least-square method for parameter identification. A hardware-in-the-loop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and high-fidelity simulations of the proposed hybrid adaptive Bight control approach.

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

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

Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored atmore » synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.« less

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

NASA Astrophysics Data System (ADS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2013-07-01

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called "Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres", (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the "Robust design of artificial neural networks methodology" and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of 252Cf, 241AmBe and 239PuBe neutron sources measured with a Bonner spheres system.

An introduction to deep learning on biological sequence data: examples and solutions.

PubMed

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

2017-11-15

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Neural network decoder for quantum error correcting codes

NASA Astrophysics Data System (ADS)

Krastanov, Stefan; Jiang, Liang

Artificial neural networks form a family of extremely powerful - albeit still poorly understood - tools used in anything from image and sound recognition through text generation to, in our case, decoding. We present a straightforward Recurrent Neural Network architecture capable of deducing the correcting procedure for a quantum error-correcting code from a set of repeated stabilizer measurements. We discuss the fault-tolerance of our scheme and the cost of training the neural network for a system of a realistic size. Such decoders are especially interesting when applied to codes, like the quantum LDPC codes, that lack known efficient decoding schemes.

ANNarchy: a code generation approach to neural simulations on parallel hardware

PubMed Central

Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

2015-01-01

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units.

PubMed

Bukovsky, Ivo; Homma, Noriyasu

2017-09-01

Stability evaluation of a weight-update system of higher order neural units (HONUs) with polynomial aggregation of neural inputs (also known as classes of polynomial neural networks) for adaptation of both feedforward and recurrent HONUs by a gradient descent method is introduced. An essential core of the approach is based on the spectral radius of a weight-update system, and it allows stability monitoring and its maintenance at every adaptation step individually. Assuring the stability of the weight-update system (at every single adaptation step) naturally results in the adaptation stability of the whole neural architecture that adapts to the target data. As an aside, the used approach highlights the fact that the weight optimization of HONU is a linear problem, so the proposed approach can be generally extended to any neural architecture that is linear in its adaptable parameters.

The Enigmatic Marine Reptile Nanchangosaurus from the Lower Triassic of Hubei, China and the Phylogenetic Affinities of Hupehsuchia

PubMed Central

Chen, Xiao-hong; Motani, Ryosuke; Cheng, Long; Jiang, Da-yong; Rieppel, Olivier

2014-01-01

The study of the holotype and of a new specimen of Nanchangosaurus suni (Reptilia; Diapsida; Hupehsuchia) revealed a suite of hitherto unrecognized characters. For example, Nanchangosaurus has bipartite neural spines and its vertebral count is nearly identical to that of Hupehsuchus. It differs from the latter in having poorly developed forelimbs despite the advanced ossification in the rest of the skeleton. Other differences all pertain to hupehsuchian plesiomorphies retained in Nanchangosaurus, such as low neural spines. The relationship of Hupehsuchia within Diapsida was analyzed based on a data matrix containing 41 taxa coded for 213 characters, of which 18 were identified as aquatic adaptations from functional inferences. These aquatic adaptations may be vulnerable to the argumentation of character homology because expectation for homoplasy is high. There is an apparent incongruence between phylogenetic signals from aquatic adaptations and the rest of the data, with aquatic adaptations favoring all marine reptiles but Helveticosaurus to form a super-clade. However, this super-clade does not obtain when aquatic adaptations were deleted, whereas individual marine reptile clades are all derived without them. We examined all possible combinations of the 18 aquatic adaptations (n = 262143) and found that four lineages of marine reptiles are recognized almost regardless of which of these features were included in the analysis: Hupehsuchia-Ichthyopterygia clade, Sauropterygia-Saurosphargidae clade, Thalattosauria, and Helveticosaurus. The interrelationships among these four depended on the combination of aquatic adaptations to be included, i.e., assumed to be homologous a priori by bypassing character argumentation. Hupehsuchia always appeared as the sister taxon of Ichthyopterygia. PMID:25014493

The enigmatic marine reptile nanchangosaurus from the lower triassic of Hubei, China and the phylogenetic affinities of Hupehsuchia.

PubMed

Chen, Xiao-hong; Motani, Ryosuke; Cheng, Long; Jiang, Da-yong; Rieppel, Olivier

2014-01-01

The study of the holotype and of a new specimen of Nanchangosaurus suni (Reptilia; Diapsida; Hupehsuchia) revealed a suite of hitherto unrecognized characters. For example, Nanchangosaurus has bipartite neural spines and its vertebral count is nearly identical to that of Hupehsuchus. It differs from the latter in having poorly developed forelimbs despite the advanced ossification in the rest of the skeleton. Other differences all pertain to hupehsuchian plesiomorphies retained in Nanchangosaurus, such as low neural spines. The relationship of Hupehsuchia within Diapsida was analyzed based on a data matrix containing 41 taxa coded for 213 characters, of which 18 were identified as aquatic adaptations from functional inferences. These aquatic adaptations may be vulnerable to the argumentation of character homology because expectation for homoplasy is high. There is an apparent incongruence between phylogenetic signals from aquatic adaptations and the rest of the data, with aquatic adaptations favoring all marine reptiles but Helveticosaurus to form a super-clade. However, this super-clade does not obtain when aquatic adaptations were deleted, whereas individual marine reptile clades are all derived without them. We examined all possible combinations of the 18 aquatic adaptations (n = 262143) and found that four lineages of marine reptiles are recognized almost regardless of which of these features were included in the analysis: Hupehsuchia-Ichthyopterygia clade, Sauropterygia-Saurosphargidae clade, Thalattosauria, and Helveticosaurus. The interrelationships among these four depended on the combination of aquatic adaptations to be included, i.e., assumed to be homologous a priori by bypassing character argumentation. Hupehsuchia always appeared as the sister taxon of Ichthyopterygia.

Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS).

PubMed

Hosseini, Seyed Abolfazl; Esmaili Paeen Afrakoti, Iman

2018-01-17

The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Taking into account the normalization condition of each energy spectrum, 4300 neutron energy spectra were generated randomly. (The value in each bin was generated randomly, and finally a normalization of each generated energy spectrum was performed). The randomly generated neutron energy spectra were considered as output data of the developed ANFIS computational code in the training step. To calculate the neutron energy spectrum using conventional methods, an inverse problem with an approximately singular response matrix (with the determinant of the matrix close to zero) should be solved. The solution of the inverse problem using the conventional methods unfold neutron energy spectrum with low accuracy. Application of the iterative algorithms in the solution of such a problem, or utilizing the intelligent algorithms (in which there is no need to solve the problem), is usually preferred for unfolding of the energy spectrum. Therefore, the main reason for development of intelligent algorithms like ANFIS for unfolding of neutron energy spectra is to avoid solving the inverse problem. In the present study, the unfolded neutron energy spectra of 252Cf and 241Am-9Be neutron sources using the developed computational code were found to have excellent agreement with the reference data. Also, the unfolded energy spectra of the neutron sources as obtained using ANFIS were more accurate than the results reported from calculations performed using artificial neural networks in previously published papers. © The Author(s) 2018. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

Evidence for highly selective neuronal tuning to whole words in the "visual word form area".

PubMed

Glezer, Laurie S; Jiang, Xiong; Riesenhuber, Maximilian

2009-04-30

Theories of reading have posited the existence of a neural representation coding for whole real words (i.e., an orthographic lexicon), but experimental support for such a representation has proved elusive. Using fMRI rapid adaptation techniques, we provide evidence that the human left ventral occipitotemporal cortex (specifically the "visual word form area," VWFA) contains a representation based on neurons highly selective for individual real words, in contrast to current theories that posit a sublexical representation in the VWFA.

Power prediction in mobile communication systems using an optimal neural-network structure.

PubMed

Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J

1997-01-01

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.

Method and system for determining induction motor speed

DOEpatents

Parlos, Alexander G.; Bharadwaj, Raj M.

2004-03-30

A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

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).

Theory of Mind: A Neural Prediction Problem

PubMed Central

Koster-Hale, Jorie; Saxe, Rebecca

2014-01-01

Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others’ goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind. PMID:24012000

An application of neural network for Structural Health Monitoring of an adaptive wing with an array of FBG sensors

NASA Astrophysics Data System (ADS)

Mieloszyk, Magdalena; Krawczuk, Marek; Skarbek, Lukasz; Ostachowicz, Wieslaw

2011-07-01

This paper presents an application of neural networks to determinate the level of activation of shape memory alloy actuators of an adaptive wing. In this concept the shape of the wing can be controlled and altered thanks to the wing design and the use of integrated shape memory alloy actuators. The wing is assumed as assembled from a number of wing sections that relative positions can be controlled independently by thermal activation of shape memory actuators. The investigated wing is employed with an array of Fibre Bragg Grating sensors. The Fibre Bragg Grating sensors with combination of a neural network have been used to Structural Health Monitoring of the wing condition. The FBG sensors are a great tool to control the condition of composite structures due to their immunity to electromagnetic fields as well as their small size and weight. They can be mounted onto the surface or embedded into the wing composite material without any significant influence on the wing strength. The paper concentrates on analysis of the determination of the twisting moment produced by an activated shape memory alloy actuator. This has been analysed both numerically using the finite element method by a commercial code ABAQUS® and experimentally using Fibre Bragg Grating sensor measurements. The results of the analysis have been then used by a neural network to determine twisting moments produced by each shape memory alloy actuator.

Neural Decoder for Topological Codes

NASA Astrophysics Data System (ADS)

Torlai, Giacomo; Melko, Roger G.

2017-07-01

We present an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network called a Boltzmann machine, of the type extensively used in deep learning. We provide a general prescription for the training of the network and a decoding strategy that is applicable to a wide variety of stabilizer codes with very little specialization. We demonstrate the neural decoder numerically on the well-known two-dimensional toric code with phase-flip errors.

Adaptation effects to attractiveness of face photographs and art portraits are domain-specific

PubMed Central

Hayn-Leichsenring, Gregor U.; Kloth, Nadine; Schweinberger, Stefan R.; Redies, Christoph

2013-01-01

We studied the neural coding of facial attractiveness by investigating effects of adaptation to attractive and unattractive human faces on the perceived attractiveness of veridical human face pictures (Experiment 1) and art portraits (Experiment 2). Experiment 1 revealed a clear pattern of contrastive aftereffects. Relative to a pre-adaptation baseline, the perceived attractiveness of faces was increased after adaptation to unattractive faces, and was decreased after adaptation to attractive faces. Experiment 2 revealed similar aftereffects when art portraits rather than face photographs were used as adaptors and test stimuli, suggesting that effects of adaptation to attractiveness are not restricted to facial photographs. Additionally, we found similar aftereffects in art portraits for beauty, another aesthetic feature that, unlike attractiveness, relates to the properties of the image (rather than to the face displayed). Importantly, Experiment 3 showed that aftereffects were abolished when adaptors were art portraits and face photographs were test stimuli. These results suggest that adaptation to facial attractiveness elicits aftereffects in the perception of subsequently presented faces, for both face photographs and art portraits, and that these effects do not cross image domains. PMID:24349690

Decoding small surface codes with feedforward neural networks

NASA Astrophysics Data System (ADS)

Varsamopoulos, Savvas; Criger, Ben; Bertels, Koen

2018-01-01

Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implementations. To decrease the decoding time, we reduce the decoding problem to a classification problem that a feedforward neural network can solve. We investigate quantum error correction and fault tolerance at small code distances using neural network-based decoders, demonstrating that the neural network can generalize to inputs that were not provided during training and that they can reach similar or better decoding performance compared to previous algorithms. We conclude by discussing the time required by a feedforward neural network decoder in hardware.

Combinatorial neural codes from a mathematical coding theory perspective.

PubMed

Curto, Carina; Itskov, Vladimir; Morrison, Katherine; Roth, Zachary; Walker, Judy L

2013-07-01

Shannon's seminal 1948 work gave rise to two distinct areas of research: information theory and mathematical coding theory. While information theory has had a strong influence on theoretical neuroscience, ideas from mathematical coding theory have received considerably less attention. Here we take a new look at combinatorial neural codes from a mathematical coding theory perspective, examining the error correction capabilities of familiar receptive field codes (RF codes). We find, perhaps surprisingly, that the high levels of redundancy present in these codes do not support accurate error correction, although the error-correcting performance of receptive field codes catches up to that of random comparison codes when a small tolerance to error is introduced. However, receptive field codes are good at reflecting distances between represented stimuli, while the random comparison codes are not. We suggest that a compromise in error-correcting capability may be a necessary price to pay for a neural code whose structure serves not only error correction, but must also reflect relationships between stimuli.

Adaptation to Vocal Expressions Reveals Multistep Perception of Auditory Emotion

PubMed Central

Maurage, Pierre; Rouger, Julien; Latinus, Marianne; Belin, Pascal

2014-01-01

The human voice carries speech as well as important nonlinguistic signals that influence our social interactions. Among these cues that impact our behavior and communication with other people is the perceived emotional state of the speaker. A theoretical framework for the neural processing stages of emotional prosody has suggested that auditory emotion is perceived in multiple steps (Schirmer and Kotz, 2006) involving low-level auditory analysis and integration of the acoustic information followed by higher-level cognition. Empirical evidence for this multistep processing chain, however, is still sparse. We examined this question using functional magnetic resonance imaging and a continuous carry-over design (Aguirre, 2007) to measure brain activity while volunteers listened to non-speech-affective vocalizations morphed on a continuum between anger and fear. Analyses dissociated neuronal adaptation effects induced by similarity in perceived emotional content between consecutive stimuli from those induced by their acoustic similarity. We found that bilateral voice-sensitive auditory regions as well as right amygdala coded the physical difference between consecutive stimuli. In contrast, activity in bilateral anterior insulae, medial superior frontal cortex, precuneus, and subcortical regions such as bilateral hippocampi depended predominantly on the perceptual difference between morphs. Our results suggest that the processing of vocal affect recognition is a multistep process involving largely distinct neural networks. Amygdala and auditory areas predominantly code emotion-related acoustic information while more anterior insular and prefrontal regions respond to the abstract, cognitive representation of vocal affect. PMID:24920615

Adaptation to vocal expressions reveals multistep perception of auditory emotion.

PubMed

Bestelmeyer, Patricia E G; Maurage, Pierre; Rouger, Julien; Latinus, Marianne; Belin, Pascal

2014-06-11

The human voice carries speech as well as important nonlinguistic signals that influence our social interactions. Among these cues that impact our behavior and communication with other people is the perceived emotional state of the speaker. A theoretical framework for the neural processing stages of emotional prosody has suggested that auditory emotion is perceived in multiple steps (Schirmer and Kotz, 2006) involving low-level auditory analysis and integration of the acoustic information followed by higher-level cognition. Empirical evidence for this multistep processing chain, however, is still sparse. We examined this question using functional magnetic resonance imaging and a continuous carry-over design (Aguirre, 2007) to measure brain activity while volunteers listened to non-speech-affective vocalizations morphed on a continuum between anger and fear. Analyses dissociated neuronal adaptation effects induced by similarity in perceived emotional content between consecutive stimuli from those induced by their acoustic similarity. We found that bilateral voice-sensitive auditory regions as well as right amygdala coded the physical difference between consecutive stimuli. In contrast, activity in bilateral anterior insulae, medial superior frontal cortex, precuneus, and subcortical regions such as bilateral hippocampi depended predominantly on the perceptual difference between morphs. Our results suggest that the processing of vocal affect recognition is a multistep process involving largely distinct neural networks. Amygdala and auditory areas predominantly code emotion-related acoustic information while more anterior insular and prefrontal regions respond to the abstract, cognitive representation of vocal affect. Copyright © 2014 Bestelmeyer et al.

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

Recent advances in coding theory for near error-free communications

NASA Technical Reports Server (NTRS)

Cheung, K.-M.; Deutsch, L. J.; Dolinar, S. J.; Mceliece, R. J.; Pollara, F.; Shahshahani, M.; Swanson, L.

1991-01-01

Channel and source coding theories are discussed. The following subject areas are covered: large constraint length convolutional codes (the Galileo code); decoder design (the big Viterbi decoder); Voyager's and Galileo's data compression scheme; current research in data compression for images; neural networks for soft decoding; neural networks for source decoding; finite-state codes; and fractals for data compression.

Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure.

PubMed

Sun, Yumei; Chen, Bing; Lin, Chong; Wang, Honghong

2017-09-18

This paper focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded. At last, two examples are used to test the validity of our results.

A closed-loop neurobotic system for fine touch sensing

NASA Astrophysics Data System (ADS)

Bologna, L. L.; Pinoteau, J.; Passot, J.-B.; Garrido, J. A.; Vogel, J.; Ros Vidal, E.; Arleo, A.

2013-08-01

Objective. Fine touch sensing relies on peripheral-to-central neurotransmission of somesthetic percepts, as well as on active motion policies shaping tactile exploration. This paper presents a novel neuroengineering framework for robotic applications based on the multistage processing of fine tactile information in the closed action-perception loop. Approach. The integrated system modules focus on (i) neural coding principles of spatiotemporal spiking patterns at the periphery of the somatosensory pathway, (ii) probabilistic decoding mechanisms mediating cortical-like tactile recognition and (iii) decision-making and low-level motor adaptation underlying active touch sensing. We probed the resulting neural architecture through a Braille reading task. Main results. Our results on the peripheral encoding of primary contact features are consistent with experimental data on human slow-adapting type I mechanoreceptors. They also suggest second-order processing by cuneate neurons may resolve perceptual ambiguities, contributing to a fast and highly performing online discrimination of Braille inputs by a downstream probabilistic decoder. The implemented multilevel adaptive control provides robustness to motion inaccuracy, while making the number of finger accelerations covariate with Braille character complexity. The resulting modulation of fingertip kinematics is coherent with that observed in human Braille readers. Significance. This work provides a basis for the design and implementation of modular neuromimetic systems for fine touch discrimination in robotics.

Failing to learn from negative prediction errors: Obesity is associated with alterations in a fundamental neural learning mechanism.

PubMed

Mathar, David; Neumann, Jane; Villringer, Arno; Horstmann, Annette

2017-10-01

Prediction errors (PEs) encode the difference between expected and actual action outcomes in the brain via dopaminergic modulation. Integration of these learning signals ensures efficient behavioral adaptation. Obesity has recently been linked to altered dopaminergic fronto-striatal circuits, thus implying impairments in cognitive domains that rely on its integrity. 28 obese and 30 lean human participants performed an implicit stimulus-response learning paradigm inside an fMRI scanner. Computational modeling and psycho-physiological interaction (PPI) analysis was utilized for assessing PE-related learning and associated functional connectivity. We show that human obesity is associated with insufficient incorporation of negative PEs into behavioral adaptation even in a non-food context, suggesting differences in a fundamental neural learning mechanism. Obese subjects were less efficient in using negative PEs to improve implicit learning performance, despite proper coding of PEs in striatum. We further observed lower functional coupling between ventral striatum and supplementary motor area in obese subjects subsequent to negative PEs. Importantly, strength of functional coupling predicted task performance and negative PE utilization. These findings show that obesity is linked to insufficient behavioral adaptation specifically in response to negative PEs, and to associated alterations in function and connectivity within the fronto-striatal system. Recognition of neural differences as a central characteristic of obesity hopefully paves the way to rethink established intervention strategies: Differential behavioral sensitivity to negative and positive PEs should be considered when designing intervention programs. Measures relying on penalization of unwanted behavior may prove less effective in obese subjects than alternative approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

PubMed

Schmitt, Michael

2004-09-01

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

Toward a Unified Sub-symbolic Computational Theory of Cognition

PubMed Central

Butz, Martin V.

2016-01-01

This paper proposes how various disciplinary theories of cognition may be combined into a unifying, sub-symbolic, computational theory of cognition. The following theories are considered for integration: psychological theories, including the theory of event coding, event segmentation theory, the theory of anticipatory behavioral control, and concept development; artificial intelligence and machine learning theories, including reinforcement learning and generative artificial neural networks; and theories from theoretical and computational neuroscience, including predictive coding and free energy-based inference. In the light of such a potential unification, it is discussed how abstract cognitive, conceptualized knowledge and understanding may be learned from actively gathered sensorimotor experiences. The unification rests on the free energy-based inference principle, which essentially implies that the brain builds a predictive, generative model of its environment. Neural activity-oriented inference causes the continuous adaptation of the currently active predictive encodings. Neural structure-oriented inference causes the longer term adaptation of the developing generative model as a whole. Finally, active inference strives for maintaining internal homeostasis, causing goal-directed motor behavior. To learn abstract, hierarchical encodings, however, it is proposed that free energy-based inference needs to be enhanced with structural priors, which bias cognitive development toward the formation of particular, behaviorally suitable encoding structures. As a result, it is hypothesized how abstract concepts can develop from, and thus how they are structured by and grounded in, sensorimotor experiences. Moreover, it is sketched-out how symbol-like thought can be generated by a temporarily active set of predictive encodings, which constitute a distributed neural attractor in the form of an interactive free-energy minimum. The activated, interactive network attractor essentially characterizes the semantics of a concept or a concept composition, such as an actual or imagined situation in our environment. Temporal successions of attractors then encode unfolding semantics, which may be generated by a behavioral or mental interaction with an actual or imagined situation in our environment. Implications, further predictions, possible verification, and falsifications, as well as potential enhancements into a fully spelled-out unified theory of cognition are discussed at the end of the paper. PMID:27445895

Research In Nonlinear Flight Control for Tiltrotor Aircraft Operating in the Terminal Area

NASA Technical Reports Server (NTRS)

Calise, A. J.; Rysdyk, R.

1996-01-01

The research during the first year of the effort focused on the implementation of the recently developed combination of neural net work adaptive control and feedback linearization. At the core of this research is the comprehensive simulation code Generic Tiltrotor Simulator (GTRS) of the XV-15 tilt rotor aircraft. For this research the GTRS code has been ported to a Fortran environment for use on PC. The emphasis of the research is on terminal area approach procedures, including conversion from aircraft to helicopter configuration. This report focuses on the longitudinal control which is the more challenging case for augmentation. Therefore, an attitude command attitude hold (ACAH) control augmentation is considered which is typically used for the pitch channel during approach procedures. To evaluate the performance of the neural network adaptive control architecture it was necessary to develop a set of low order pilot models capable of performing such tasks as, follow desired altitude profiles, follow desired speed profiles, operate on both sides of powercurve, convert, including flaps as well as mastangle changes, operate with different stability and control augmentation system (SCAS) modes. The pilot models are divided in two sets, one for the backside of the powercurve and one for the frontside. These two sets are linearly blended with speed. The mastangle is also scheduled with speed. Different aspects of the proposed architecture for the neural network (NNW) augmented model inversion were also demonstrated. The demonstration involved implementation of a NNW architecture using linearized models from GTRS, including rotor states, to represent the XV-15 at various operating points. The dynamics used for the model inversion were based on the XV-15 operating at 30 Kts, with residualized rotor dynamics, and not including cross coupling between translational and rotational states. The neural network demonstrated ACAH control under various circumstances. Future efforts will include the implementation into the Fortran environment of GTRS, including pilot modeling and NNW augmentation for the lateral channels. These efforts should lead to the development of architectures that will provide for fully automated approach, using similar strategies.

Connecting Neural Coding to Number Cognition: A Computational Account

ERIC Educational Resources Information Center

Prather, Richard W.

2012-01-01

The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

The statistics of the vestibular input experienced during natural self-motion differ between rodents and primates.

PubMed

Carriot, Jérome; Jamali, Mohsen; Chacron, Maurice J; Cullen, Kathleen E

2017-04-15

In order to understand how the brain's coding strategies are adapted to the statistics of the sensory stimuli experienced during everyday life, the use of animal models is essential. Mice and non-human primates have become common models for furthering our knowledge of the neuronal coding of natural stimuli, but differences in their natural environments and behavioural repertoire may impact optimal coding strategies. Here we investigated the structure and statistics of the vestibular input experienced by mice versus non-human primates during natural behaviours, and found important differences. Our data establish that the structure and statistics of natural signals in non-human primates more closely resemble those observed previously in humans, suggesting similar coding strategies for incoming vestibular input. These results help us understand how the effects of active sensing and biomechanics will differentially shape the statistics of vestibular stimuli across species, and have important implications for sensory coding in other systems. It is widely believed that sensory systems are adapted to the statistical structure of natural stimuli, thereby optimizing coding. Recent evidence suggests that this is also the case for the vestibular system, which senses self-motion and in turn contributes to essential brain functions ranging from the most automatic reflexes to spatial perception and motor coordination. However, little is known about the statistics of self-motion stimuli actually experienced by freely moving animals in their natural environments. Accordingly, here we examined the natural self-motion signals experienced by mice and monkeys: two species commonly used to study vestibular neural coding. First, we found that probability distributions for all six dimensions of motion (three rotations, three translations) in both species deviated from normality due to long tails. Interestingly, the power spectra of natural rotational stimuli displayed similar structure for both species and were not well fitted by power laws. This result contrasts with reports that the natural spectra of other sensory modalities (i.e. vision, auditory and tactile) instead show a power-law relationship with frequency, which indicates scale invariance. Analysis of natural translational stimuli revealed important species differences as power spectra deviated from scale invariance for monkeys but not for mice. By comparing our results to previously published data for humans, we found the statistical structure of natural self-motion stimuli in monkeys and humans more closely resemble one another. Our results thus predict that, overall, neural coding strategies used by vestibular pathways to encode natural self-motion stimuli are fundamentally different in rodents and primates. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

The statistics of the vestibular input experienced during natural self‐motion differ between rodents and primates

PubMed Central

Carriot, Jérome; Jamali, Mohsen; Chacron, Maurice J.

2017-01-01

Key points In order to understand how the brain's coding strategies are adapted to the statistics of the sensory stimuli experienced during everyday life, the use of animal models is essential.Mice and non‐human primates have become common models for furthering our knowledge of the neuronal coding of natural stimuli, but differences in their natural environments and behavioural repertoire may impact optimal coding strategies.Here we investigated the structure and statistics of the vestibular input experienced by mice versus non‐human primates during natural behaviours, and found important differences.Our data establish that the structure and statistics of natural signals in non‐human primates more closely resemble those observed previously in humans, suggesting similar coding strategies for incoming vestibular input.These results help us understand how the effects of active sensing and biomechanics will differentially shape the statistics of vestibular stimuli across species, and have important implications for sensory coding in other systems. Abstract It is widely believed that sensory systems are adapted to the statistical structure of natural stimuli, thereby optimizing coding. Recent evidence suggests that this is also the case for the vestibular system, which senses self‐motion and in turn contributes to essential brain functions ranging from the most automatic reflexes to spatial perception and motor coordination. However, little is known about the statistics of self‐motion stimuli actually experienced by freely moving animals in their natural environments. Accordingly, here we examined the natural self‐motion signals experienced by mice and monkeys: two species commonly used to study vestibular neural coding. First, we found that probability distributions for all six dimensions of motion (three rotations, three translations) in both species deviated from normality due to long tails. Interestingly, the power spectra of natural rotational stimuli displayed similar structure for both species and were not well fitted by power laws. This result contrasts with reports that the natural spectra of other sensory modalities (i.e. vision, auditory and tactile) instead show a power‐law relationship with frequency, which indicates scale invariance. Analysis of natural translational stimuli revealed important species differences as power spectra deviated from scale invariance for monkeys but not for mice. By comparing our results to previously published data for humans, we found the statistical structure of natural self‐motion stimuli in monkeys and humans more closely resemble one another. Our results thus predict that, overall, neural coding strategies used by vestibular pathways to encode natural self‐motion stimuli are fundamentally different in rodents and primates. PMID:28083981

A neutron spectrum unfolding computer code based on artificial neural networks

NASA Astrophysics Data System (ADS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2014-02-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in the HTML format. NSDann unfolding code is freely available, upon request to the authors.

Dynamical information encoding in neural adaptation.

PubMed

Luozheng Li; Wenhao Zhang; Yuanyuan Mi; Dahui Wang; Xiaohan Lin; Si Wu

2016-08-01

Adaptation refers to the general phenomenon that a neural system dynamically adjusts its response property according to the statistics of external inputs. In response to a prolonged constant stimulation, neuronal firing rates always first increase dramatically at the onset of the stimulation; and afterwards, they decrease rapidly to a low level close to background activity. This attenuation of neural activity seems to be contradictory to our experience that we can still sense the stimulus after the neural system is adapted. Thus, it prompts a question: where is the stimulus information encoded during the adaptation? Here, we investigate a computational model in which the neural system employs a dynamical encoding strategy during the neural adaptation: at the early stage of the adaptation, the stimulus information is mainly encoded in the strong independent firings; and as time goes on, the information is shifted into the weak but concerted responses of neurons. We find that short-term plasticity, a general feature of synapses, provides a natural mechanism to achieve this goal. Furthermore, we demonstrate that with balanced excitatory and inhibitory inputs, this correlation-based information can be read out efficiently. The implications of this study on our understanding of neural information encoding are discussed.

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

NASA Technical Reports Server (NTRS)

Zhang, Yuhan; Lu, Dr. Thomas

2010-01-01

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

PubMed

Hu, Jin; Zeng, Chunna

2017-02-01

The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genetic learning in rule-based and neural systems

NASA Technical Reports Server (NTRS)

Smith, Robert E.

1993-01-01

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

Intelligent neural network and fuzzy logic control of industrial and power systems

NASA Astrophysics Data System (ADS)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

NASA Astrophysics Data System (ADS)

Sun, Ran; Wang, Jihe; Zhang, Dexin; Shao, Xiaowei

2018-02-01

This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.

Dysfunction of Rapid Neural Adaptation in Dyslexia.

PubMed

Perrachione, Tyler K; Del Tufo, Stephanie N; Winter, Rebecca; Murtagh, Jack; Cyr, Abigail; Chang, Patricia; Halverson, Kelly; Ghosh, Satrajit S; Christodoulou, Joanna A; Gabrieli, John D E

2016-12-21

Identification of specific neurophysiological dysfunctions resulting in selective reading difficulty (dyslexia) has remained elusive. In addition to impaired reading development, individuals with dyslexia frequently exhibit behavioral deficits in perceptual adaptation. Here, we assessed neurophysiological adaptation to stimulus repetition in adults and children with dyslexia for a wide variety of stimuli, spoken words, written words, visual objects, and faces. For every stimulus type, individuals with dyslexia exhibited significantly diminished neural adaptation compared to controls in stimulus-specific cortical areas. Better reading skills in adults and children with dyslexia were associated with greater repetition-induced neural adaptation. These results highlight a dysfunction of rapid neural adaptation as a core neurophysiological difference in dyslexia that may underlie impaired reading development. Reduced neurophysiological adaptation may relate to prior reports of reduced behavioral adaptation in dyslexia and may reveal a difference in brain functions that ultimately results in a specific reading impairment. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of artificial neural networks to the design optimization of aerospace structural components

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.

1993-01-01

The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

Optimum Design of Aerospace Structural Components Using Neural Networks

NASA Technical Reports Server (NTRS)

Berke, L.; Patnaik, S. N.; Murthy, P. L. N.

1993-01-01

The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires a trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network using the code NETS. Optimum designs for new design conditions were predicted using the trained network. Neural net prediction of optimum designs was found to be satisfactory for the majority of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

Neural Mechanisms of Information Storage in Visual Short-Term Memory

PubMed Central

Serences, John T.

2016-01-01

The capacity to briefly memorize fleeting sensory information supports visual search and behavioral interactions with relevant stimuli in the environment. Traditionally, studies investigating the neural basis of visual short term memory (STM) have focused on the role of prefrontal cortex (PFC) in exerting executive control over what information is stored and how it is adaptively used to guide behavior. However, the neural substrates that support the actual storage of content-specific information in STM are more controversial, with some attributing this function to PFC and others to the specialized areas of early visual cortex that initially encode incoming sensory stimuli. In contrast to these traditional views, I will review evidence suggesting that content-specific information can be flexibly maintained in areas across the cortical hierarchy ranging from early visual cortex to PFC. While the factors that determine exactly where content-specific information is represented are not yet entirely clear, recognizing the importance of task-demands and better understanding the operation of non-spiking neural codes may help to constrain new theories about how memories are maintained at different resolutions, across different timescales, and in the presence of distracting information. PMID:27668990

Neural mechanisms of information storage in visual short-term memory.

PubMed

Serences, John T

2016-11-01

The capacity to briefly memorize fleeting sensory information supports visual search and behavioral interactions with relevant stimuli in the environment. Traditionally, studies investigating the neural basis of visual short term memory (STM) have focused on the role of prefrontal cortex (PFC) in exerting executive control over what information is stored and how it is adaptively used to guide behavior. However, the neural substrates that support the actual storage of content-specific information in STM are more controversial, with some attributing this function to PFC and others to the specialized areas of early visual cortex that initially encode incoming sensory stimuli. In contrast to these traditional views, I will review evidence suggesting that content-specific information can be flexibly maintained in areas across the cortical hierarchy ranging from early visual cortex to PFC. While the factors that determine exactly where content-specific information is represented are not yet entirely clear, recognizing the importance of task-demands and better understanding the operation of non-spiking neural codes may help to constrain new theories about how memories are maintained at different resolutions, across different timescales, and in the presence of distracting information. Copyright © 2016 Elsevier Ltd. All rights reserved.

Survey of adaptive image coding techniques

NASA Technical Reports Server (NTRS)

Habibi, A.

1977-01-01

The general problem of image data compression is discussed briefly with attention given to the use of Karhunen-Loeve transforms, suboptimal systems, and block quantization. A survey is then conducted encompassing the four categories of adaptive systems: (1) adaptive transform coding (adaptive sampling, adaptive quantization, etc.), (2) adaptive predictive coding (adaptive delta modulation, adaptive DPCM encoding, etc.), (3) adaptive cluster coding (blob algorithms and the multispectral cluster coding technique), and (4) adaptive entropy coding.

Neural Coding Mechanisms in Gustation.

DTIC Science & Technology

1980-09-15

world is composed of four primary tastes ( sweet , sour, salty , and bitter), and that each of these is carried by a separate and private neural line, thus...ted sweet -sour- salty -bitter types. The mathematical method of analysis was hierarchical cluster analysis based on the responses of many neurons (20 to...block number) Taste Neural coding Neural organization Stimulus organization Olfaction AB TRACT M~ea -i .rvm~ .1* N necffas and idmatity by block mmnbwc

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.

Energy coding in biological neural networks

PubMed Central

Zhang, Zhikang

2007-01-01

According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, we present a brand new scientific theory that offers an unique mechanism for brain information processing. We demonstrate that the neural coding produced by the activity of the brain is well described by our theory of energy coding. Due to the energy coding model’s ability to reveal mechanisms of brain information processing based upon known biophysical properties, we can not only reproduce various experimental results of neuro-electrophysiology, but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, we estimate that the theory has very important consequences for quantitative research of cognitive function. PMID:19003513

Learning and adaptation: neural and behavioural mechanisms behind behaviour change

NASA Astrophysics Data System (ADS)

Lowe, Robert; Sandamirskaya, Yulia

2018-01-01

This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

Adaptive Neurotechnology for Making Neural Circuits Functional .

NASA Astrophysics Data System (ADS)

Jung, Ranu

2008-03-01

Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2017-01-01

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches. PMID:28771497

Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

PubMed

Chen, Ziting; Li, Zhijun; Chen, C L Philip

2017-06-01

An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

Clique-Based Neural Associative Memories with Local Coding and Precoding.

PubMed

Mofrad, Asieh Abolpour; Parker, Matthew G; Ferdosi, Zahra; Tadayon, Mohammad H

2016-08-01

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

Mechanisms of Long Non-Coding RNAs in the Assembly and Plasticity of Neural Circuitry.

PubMed

Wang, Andi; Wang, Junbao; Liu, Ying; Zhou, Yan

2017-01-01

The mechanisms underlying development processes and functional dynamics of neural circuits are far from understood. Long non-coding RNAs (lncRNAs) have emerged as essential players in defining identities of neural cells, and in modulating neural activities. In this review, we summarized latest advances concerning roles and mechanisms of lncRNAs in assembly, maintenance and plasticity of neural circuitry, as well as lncRNAs' implications in neurological disorders. We also discussed technical advances and challenges in studying functions and mechanisms of lncRNAs in neural circuitry. Finally, we proposed that lncRNA studies would advance our understanding on how neural circuits develop and function in physiology and disease conditions.

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.

Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

PubMed

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

2015-03-01

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: II mechanisms.

PubMed

Nikolaev, Anton; Zheng, Lei; Wardill, Trevor J; O'Kane, Cahir J; de Polavieja, Gonzalo G; Juusola, Mikko

2009-01-01

Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1-R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information.

Nonlinear adaptive inverse control via the unified model neural network

NASA Astrophysics Data System (ADS)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

The specificity of stimulus-specific adaptation in human auditory cortex increases with repeated exposure to the adapting stimulus.

PubMed

Briley, Paul M; Krumbholz, Katrin

2013-12-01

The neural response to a sensory stimulus tends to be more strongly reduced when the stimulus is preceded by the same, rather than a different, stimulus. This stimulus-specific adaptation (SSA) is ubiquitous across the senses. In hearing, SSA has been suggested to play a role in change detection as indexed by the mismatch negativity. This study sought to test whether SSA, measured in human auditory cortex, is caused by neural fatigue (reduction in neural responsiveness) or by sharpening of neural tuning to the adapting stimulus. For that, we measured event-related cortical potentials to pairs of pure tones with varying frequency separation and stimulus onset asynchrony (SOA). This enabled us to examine the relationship between the degree of specificity of adaptation as a function of frequency separation and the rate of decay of adaptation with increasing SOA. Using simulations of tonotopic neuron populations, we demonstrate that the fatigue model predicts independence of adaptation specificity and decay rate, whereas the sharpening model predicts interdependence. The data showed independence and thus supported the fatigue model. In a second experiment, we measured adaptation specificity after multiple presentations of the adapting stimulus. The multiple adapters produced more adaptation overall, but the effect was more specific to the adapting frequency. Within the context of the fatigue model, the observed increase in adaptation specificity could be explained by assuming a 2.5-fold increase in neural frequency selectivity. We discuss possible bottom-up and top-down mechanisms of this effect.

Beauty is in the efficient coding of the beholder.

PubMed

Renoult, Julien P; Bovet, Jeanne; Raymond, Michel

2016-03-01

Sexual ornaments are often assumed to be indicators of mate quality. Yet it remains poorly known how certain ornaments are chosen before any coevolutionary race makes them indicative. Perceptual biases have been proposed to play this role, but known biases are mostly restricted to a specific taxon, which precludes evaluating their general importance in sexual selection. Here we identify a potentially universal perceptual bias in mate choice. We used an algorithm that models the sparseness of the activity of simple cells in the primary visual cortex (or V1) of humans when coding images of female faces. Sparseness was found positively correlated with attractiveness as rated by men and explained up to 17% of variance in attractiveness. Because V1 is adapted to process signals from natural scenes, in general, not faces specifically, our results indicate that attractiveness for female faces is influenced by a visual bias. Sparseness and more generally efficient neural coding are ubiquitous, occurring in various animals and sensory modalities, suggesting that the influence of efficient coding on mate choice can be widespread in animals.

Biologically driven neural platform invoking parallel electrophoretic separation and urinary metabolite screening.

PubMed

Page, Tessa; Nguyen, Huong Thi Huynh; Hilts, Lindsey; Ramos, Lorena; Hanrahan, Grady

2012-06-01

This work reveals a computational framework for parallel electrophoretic separation of complex biological macromolecules and model urinary metabolites. More specifically, the implementation of a particle swarm optimization (PSO) algorithm on a neural network platform for multiparameter optimization of multiplexed 24-capillary electrophoresis technology with UV detection is highlighted. Two experimental systems were examined: (1) separation of purified rabbit metallothioneins and (2) separation of model toluene urinary metabolites and selected organic acids. Results proved superior to the use of neural networks employing standard back propagation when examining training error, fitting response, and predictive abilities. Simulation runs were obtained as a result of metaheuristic examination of the global search space with experimental responses in good agreement with predicted values. Full separation of selected analytes was realized after employing optimal model conditions. This framework provides guidance for the application of metaheuristic computational tools to aid in future studies involving parallel chemical separation and screening. Adaptable pseudo-code is provided to enable users of varied software packages and modeling framework to implement the PSO algorithm for their desired use.

Rats in Virtual Space: The development and implementation of a multimodal virtual reality system for small animals

NASA Astrophysics Data System (ADS)

Aharoni, Daniel Benjamin

The integration of multimodal sensory information into a common neural code is a critical function of all complex nervous systems. This process is required for adaptive responding to incoming stimuli as well as the formation of a cognitive map of the external sensory environment. The underlying neural mechanisms of multimodal integration are poorly understood due, in part, to the technical difficulties of manipulating multimodal sensory information in combination with simultaneous in-vivo electrophysiological recording in awake behaving animals. We therefore developed a non-invasive multimodal virtual reality system that is conducive to wired electrophysiological recording techniques. This system allows for the dynamic presentation of highly immersive audiovisual virtual environments to rats maintained in a body fixed position on top of a quiet spherical treadmill. Notably, this allows the rats to remain at the same spatial location in the real world without the need for head fixation. This method opens the door for a wide array of future studies aimed at elucidating the underlying neural mechanisms of multimodal integration.

Reward and Aversion.

PubMed

Hu, Hailan

2016-07-08

To benefit from opportunities and cope with challenges in the environment, animals must adapt their behavior to acquire rewards and to avoid punishments. Maladaptive changes in the neuromodulatory systems and neural circuits for reward and aversion can lead to manifestation of several prominent psychiatric disorders including addiction and depression. Recent progress is pushing the boundaries of knowledge on two major fronts in research on reward and aversion: First, new layers of complexity have been reported on the functions of dopamine (DA) and serotonin (5-HT) neuromodulatory systems in reward and aversion. Second, specific circuit components in the neural pathways that encode reward and aversion have begun to be identified. This review aims to outline historic perspectives and new insights into the functions of DA and 5-HT systems in coding the distinct components of rewards. It also highlights recent advances in neural circuit studies enabled by new technologies, such as cell-type-specific electrophysiology and tracing, and optogenetics-based behavioral manipulation. This knowledge may provide guidance for developing novel treatment strategies for neuropsychiatric diseases related to the malfunction of the reward system.

View-Independent Working Memory Representations of Artificial Shapes in Prefrontal and Posterior Regions of the Human Brain.

PubMed

Christophel, Thomas B; Allefeld, Carsten; Endisch, Christian; Haynes, John-Dylan

2018-06-01

Traditional views of visual working memory postulate that memorized contents are stored in dorsolateral prefrontal cortex using an adaptive and flexible code. In contrast, recent studies proposed that contents are maintained by posterior brain areas using codes akin to perceptual representations. An important question is whether this reflects a difference in the level of abstraction between posterior and prefrontal representations. Here, we investigated whether neural representations of visual working memory contents are view-independent, as indicated by rotation-invariance. Using functional magnetic resonance imaging and multivariate pattern analyses, we show that when subjects memorize complex shapes, both posterior and frontal brain regions maintain the memorized contents using a rotation-invariant code. Importantly, we found the representations in frontal cortex to be localized to the frontal eye fields rather than dorsolateral prefrontal cortices. Thus, our results give evidence for the view-independent storage of complex shapes in distributed representations across posterior and frontal brain regions.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

PubMed

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

PubMed Central

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world. PMID:26528176

Electrophysiological models of neural processing.

PubMed

Nelson, Mark E

2011-01-01

The brain is an amazing information processing system that allows organisms to adaptively monitor and control complex dynamic interactions with their environment across multiple spatial and temporal scales. Mathematical modeling and computer simulation techniques have become essential tools in understanding diverse aspects of neural processing ranging from sub-millisecond temporal coding in the sound localization circuity of barn owls to long-term memory storage and retrieval in humans that can span decades. The processing capabilities of individual neurons lie at the core of these models, with the emphasis shifting upward and downward across different levels of biological organization depending on the nature of the questions being addressed. This review provides an introduction to the techniques for constructing biophysically based models of individual neurons and local networks. Topics include Hodgkin-Huxley-type models of macroscopic membrane currents, Markov models of individual ion-channel currents, compartmental models of neuronal morphology, and network models involving synaptic interactions among multiple neurons.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex.

PubMed

Downer, Joshua D; Rapone, Brittany; Verhein, Jessica; O'Connor, Kevin N; Sutter, Mitchell L

2017-05-24

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations ( r noise ) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on r noise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in r noise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations ( r noise ) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on r noise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. Copyright © 2017 the authors 0270-6474/17/375378-15$15.00/0.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex

PubMed Central

2017-01-01

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations (rnoise) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on rnoise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in rnoise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations (rnoise) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on rnoise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. PMID:28432139

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.

A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

PubMed

Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

2011-01-01

To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

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.

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks.

PubMed

Sharghi Ido, A; Bonyadi, M R; Etaati, G R; Shahriari, M

2009-10-01

Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both (241)Am-Be and (252)Cf neutron sources. The results of neural network are in good agreement with FORIST code.

Deciphering Neural Codes of Memory during Sleep

PubMed Central

Chen, Zhe; Wilson, Matthew A.

2017-01-01

Memories of experiences are stored in the cerebral cortex. Sleep is critical for consolidating hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms on memory consolidation, and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here, we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches for analyzing sleep-associated neural codes (SANC). We focus on two analysis paradigms for sleep-associated memory, and propose a new unsupervised learning framework (“memory first, meaning later”) for unbiased assessment of SANC. PMID:28390699

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.

Adaptive Optimization of Aircraft Engine Performance Using Neural Networks

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Long, Theresa W.

1995-01-01

Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.

Flight control with adaptive critic neural network

NASA Astrophysics Data System (ADS)

Han, Dongchen

2001-10-01

In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

Neural Predictors of Visuomotor Adaptation Rate and Multi-Day Savings

NASA Technical Reports Server (NTRS)

Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob;

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

PubMed Central

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-01-01

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators. PMID:26266537

Musicians change their tune: how hearing loss alters the neural code.

PubMed

Parbery-Clark, Alexandra; Anderson, Samira; Kraus, Nina

2013-08-01

Individuals with sensorineural hearing loss have difficulty understanding speech, especially in background noise. This deficit remains even when audibility is restored through amplification, suggesting that mechanisms beyond a reduction in peripheral sensitivity contribute to the perceptual difficulties associated with hearing loss. Given that normal-hearing musicians have enhanced auditory perceptual skills, including speech-in-noise perception, coupled with heightened subcortical responses to speech, we aimed to determine whether similar advantages could be observed in middle-aged adults with hearing loss. Results indicate that musicians with hearing loss, despite self-perceptions of average performance for understanding speech in noise, have a greater ability to hear in noise relative to nonmusicians. This is accompanied by more robust subcortical encoding of sound (e.g., stimulus-to-response correlations and response consistency) as well as more resilient neural responses to speech in the presence of background noise (e.g., neural timing). Musicians with hearing loss also demonstrate unique neural signatures of spectral encoding relative to nonmusicians: enhanced neural encoding of the speech-sound's fundamental frequency but not of its upper harmonics. This stands in contrast to previous outcomes in normal-hearing musicians, who have enhanced encoding of the harmonics but not the fundamental frequency. Taken together, our data suggest that although hearing loss modifies a musician's spectral encoding of speech, the musician advantage for perceiving speech in noise persists in a hearing-impaired population by adaptively strengthening underlying neural mechanisms for speech-in-noise perception. Copyright © 2013 Elsevier B.V. All rights reserved.

Beyond Molecular Codes: Simple Rules to Wire Complex Brains

PubMed Central

Hassan, Bassem A.; Hiesinger, P. Robin

2015-01-01

Summary Molecular codes, like postal zip codes, are generally considered a robust way to ensure the specificity of neuronal target selection. However, a code capable of unambiguously generating complex neural circuits is difficult to conceive. Here, we re-examine the notion of molecular codes in the light of developmental algorithms. We explore how molecules and mechanisms that have been considered part of a code may alternatively implement simple pattern formation rules sufficient to ensure wiring specificity in neural circuits. This analysis delineates a pattern-based framework for circuit construction that may contribute to our understanding of brain wiring. PMID:26451480

Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

PubMed

Zhang, Yanjun; Tao, Gang; Chen, Mou

2016-09-01

This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

Dynamical principles in neuroscience

NASA Astrophysics Data System (ADS)

Rabinovich, Mikhail I.; Varona, Pablo; Selverston, Allen I.; Abarbanel, Henry D. I.

2006-10-01

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?

Dynamical principles in neuroscience

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

Rabinovich, Mikhail I.; Varona, Pablo; Selverston, Allen I.

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only amore » few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?.« less

Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

PubMed

Long, Lijun; Zhao, Jun

2015-07-01

This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

Spatial features of synaptic adaptation affecting learning performance.

PubMed

Berger, Damian L; de Arcangelis, Lucilla; Herrmann, Hans J

2017-09-08

Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation and the spatial range of synaptic connections.

Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

NASA Astrophysics Data System (ADS)

Kmet', Tibor; Kmet'ová, Mária

2009-09-01

A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

Visual attention mitigates information loss in small- and large-scale neural codes

PubMed Central

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-01-01

Summary The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. PMID:25769502

Short-term synaptic plasticity and heterogeneity in neural systems

NASA Astrophysics Data System (ADS)

Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

2013-01-01

We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

Stable and Dynamic Coding for Working Memory in Primate Prefrontal Cortex

PubMed Central

Watanabe, Kei; Funahashi, Shintaro; Stokes, Mark G.

2017-01-01

Working memory (WM) provides the stability necessary for high-level cognition. Influential theories typically assume that WM depends on the persistence of stable neural representations, yet increasing evidence suggests that neural states are highly dynamic. Here we apply multivariate pattern analysis to explore the population dynamics in primate lateral prefrontal cortex (PFC) during three variants of the classic memory-guided saccade task (recorded in four animals). We observed the hallmark of dynamic population coding across key phases of a working memory task: sensory processing, memory encoding, and response execution. Throughout both these dynamic epochs and the memory delay period, however, the neural representational geometry remained stable. We identified two characteristics that jointly explain these dynamics: (1) time-varying changes in the subpopulation of neurons coding for task variables (i.e., dynamic subpopulations); and (2) time-varying selectivity within neurons (i.e., dynamic selectivity). These results indicate that even in a very simple memory-guided saccade task, PFC neurons display complex dynamics to support stable representations for WM. SIGNIFICANCE STATEMENT Flexible, intelligent behavior requires the maintenance and manipulation of incoming information over various time spans. For short time spans, this faculty is labeled “working memory” (WM). Dominant models propose that WM is maintained by stable, persistent patterns of neural activity in prefrontal cortex (PFC). However, recent evidence suggests that neural activity in PFC is dynamic, even while the contents of WM remain stably represented. Here, we explored the neural dynamics in PFC during a memory-guided saccade task. We found evidence for dynamic population coding in various task epochs, despite striking stability in the neural representational geometry of WM. Furthermore, we identified two distinct cellular mechanisms that contribute to dynamic population coding. PMID:28559375

On the Effectiveness of a Neural Network for Adaptive External Pacing.

ERIC Educational Resources Information Center

Montazemi, Ali R.; Wang, Feng

1995-01-01

Proposes a neural network model for an intelligent tutoring system featuring adaptive external control of student pacing. An experiment was conducted, and students using adaptive external pacing experienced improved mastery learning and increased motivation for time management. Contains 66 references. (JKP)

Behavioral and Neural Adaptation in Approach Behavior.

PubMed

Wang, Shuo; Falvello, Virginia; Porter, Jenny; Said, Christopher P; Todorov, Alexander

2018-06-01

People often make approachability decisions based on perceived facial trustworthiness. However, it remains unclear how people learn trustworthiness from a population of faces and whether this learning influences their approachability decisions. Here we investigated the neural underpinning of approach behavior and tested two important hypotheses: whether the amygdala adapts to different trustworthiness ranges and whether the amygdala is modulated by task instructions and evaluative goals. We showed that participants adapted to the stimulus range of perceived trustworthiness when making approach decisions and that these decisions were further modulated by the social context. The right amygdala showed both linear response and quadratic response to trustworthiness level, as observed in prior studies. Notably, the amygdala's response to trustworthiness was not modulated by stimulus range or social context, a possible neural dynamic adaptation. Together, our data have revealed a robust behavioral adaptation to different trustworthiness ranges as well as a neural substrate underlying approach behavior based on perceived facial trustworthiness.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

PubMed

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

Neural coding using telegraphic switching of magnetic tunnel junction

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

Suh, Dong Ik; Bae, Gi Yoon; Oh, Heong Sik

2015-05-07

In this work, we present a synaptic transmission representing neural coding with spike trains by using a magnetic tunnel junction (MTJ). Telegraphic switching generates an artificial neural signal with both the applied magnetic field and the spin-transfer torque that act as conflicting inputs for modulating the number of spikes in spike trains. The spiking probability is observed to be weighted with modulation between 27.6% and 99.8% by varying the amplitude of the voltage input or the external magnetic field. With a combination of the reverse coding scheme and the synaptic characteristic of MTJ, an artificial function for the synaptic transmissionmore » is achieved.« less

Relationship between neural response and adaptation selectivity to form and color: an ERP study.

PubMed

Rentzeperis, Ilias; Nikolaev, Andrey R; Kiper, Daniel C; van Leeuwen, Cees

2012-01-01

Adaptation is widely used as a tool for studying selectivity to visual features. In these studies it is usually assumed that the loci of feature selective neural responses and adaptation coincide. We used an adaptation paradigm to investigate the relationship between response and adaptation selectivity in event-related potentials (ERPs). ERPs were evoked by the presentation of colored Glass patterns in a form discrimination task. Response selectivities to form and, to some extent, color of the patterns were reflected in the C1 and N1 ERP components. Adaptation selectivity to color was reflected in N1 and was followed by a late (300-500 ms after stimulus onset) effect of form adaptation. Thus for form, response and adaptation selectivity were manifested in non-overlapping intervals. These results indicate that adaptation and response selectivity can be associated with different processes. Therefore, inferring selectivity from an adaptation paradigm requires analysis of both adaptation and neural response data.

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.

Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

PubMed

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

2015-09-01

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

USGS Publications Warehouse

Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

2007-01-01

To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

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.

Clustering of neural code words revealed by a first-order phase transition

NASA Astrophysics Data System (ADS)

Huang, Haiping; Toyoizumi, Taro

2016-06-01

A network of neurons in the central nervous system collectively represents information by its spiking activity states. Typically observed states, i.e., code words, occupy only a limited portion of the state space due to constraints imposed by network interactions. Geometrical organization of code words in the state space, critical for neural information processing, is poorly understood due to its high dimensionality. Here, we explore the organization of neural code words using retinal data by computing the entropy of code words as a function of Hamming distance from a particular reference codeword. Specifically, we report that the retinal code words in the state space are divided into multiple distinct clusters separated by entropy-gaps, and that this structure is shared with well-known associative memory networks in a recallable phase. Our analysis also elucidates a special nature of the all-silent state. The all-silent state is surrounded by the densest cluster of code words and located within a reachable distance from most code words. This code-word space structure quantitatively predicts typical deviation of a state-trajectory from its initial state. Altogether, our findings reveal a non-trivial heterogeneous structure of the code-word space that shapes information representation in a biological network.

Visual attention mitigates information loss in small- and large-scale neural codes.

PubMed

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-04-01

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. Copyright © 2015 Elsevier Ltd. All rights reserved.

Deciphering Neural Codes of Memory during Sleep.

PubMed

Chen, Zhe; Wilson, Matthew A

2017-05-01

Memories of experiences are stored in the cerebral cortex. Sleep is critical for the consolidation of hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms in memory consolidation and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches to the analysis of sleep-associated neural codes (SANCs). We focus on two analysis paradigms for sleep-associated memory and propose a new unsupervised learning framework ('memory first, meaning later') for unbiased assessment of SANCs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inference in the brain: Statistics flowing in redundant population codes

PubMed Central

Pitkow, Xaq; Angelaki, Dora E

2017-01-01

It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors. PMID:28595050

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

Neural readaptation to earth s gravity following exposure to microgravity

NASA Astrophysics Data System (ADS)

Boyle, R.; Highstein, S.; Mensinger, A.

Vertebrates possess hair cell otolith organs of the inner ear, the utricule and saccule, that transduce inertial force due to head translation and head tilt relative to gravitational vertical, and transform the vector sum of the imposing accelerations into a neural code carried by the afferent nerve fibers. This code is combined in the central vestibular pathways with motion signals obtained from the semicircular canals and other sensory modalities to compute a cent ral representation of the body in space called the gravitoinertial vector. Thus the central nervous system resolves the ambiguity of gravity and self-motion and thereby maintains balance and equilibrium under varying conditions. Exposure to microgravity imposes an extreme condition to which the organism must adapt. Space travelers often experience disorientation during the first few days in microgravity, called Space Adaptation Syndrome. From the earliest manned missions it was evident that adjustments to the microgravity environment in-flight and upon return to Earth's 1g occur. We studied the neural readaptation to Earth's 1g using electrophysiological techniques to measure the response characteristics of utricular nerve afferents in fish upon return from an exposure to microgravity. Following a 9 (STS-95) and 15 (STS-90) day exposure to microgravity aboard two NASA shuttle orbital flights, single afferent recording experiments were conducted in four toadfish, Opsanus tau, to characterize the afferent response properties to gravito inertial accelerations and compare them to- afferent responses of control animals similarly tested. Six recording sessions were made sequentially 10-117 hrs postflight. Afferent responses to translational accelerations and head tilts were detected in the earliest sessions. The most striking result is the occurrence of hypersensitive afferents, having extremely high response sensitivity to minor displacements such as < 0.5 mm displacement at 0.006g, within the first day postflight. After about 30 hrs the afferent response properties of flight and control fish were similar. The reduced gravitational acceleration in orbit apparently resulted in a temporary up-regulation of the sensitivity of utricular afferents. The time course of return to normal afferent sensitivity parallels the decrease in vestibular disorientation in astronauts following return from space. (Supported by NASA, NIH and NASDA)

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.

Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis.

PubMed

Liu, Zhi; Lai, Guanyu; Zhang, Yun; Chen, Xin; Chen, Chun Lung Philip

2014-12-01

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

An ECG signals compression method and its validation using NNs.

PubMed

Fira, Catalina Monica; Goras, Liviu

2008-04-01

This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called "quality score," which takes into account both the reconstruction errors and the compression ratio, is proposed.

Cortical oscillations and sensory predictions.

PubMed

Arnal, Luc H; Giraud, Anne-Lise

2012-07-01

Many theories of perception are anchored in the central notion that the brain continuously updates an internal model of the world to infer the probable causes of sensory events. In this framework, the brain needs not only to predict the causes of sensory input, but also when they are most likely to happen. In this article, we review the neurophysiological bases of sensory predictions of "what' (predictive coding) and 'when' (predictive timing), with an emphasis on low-level oscillatory mechanisms. We argue that neural rhythms offer distinct and adapted computational solutions to predicting 'what' is going to happen in the sensory environment and 'when'. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dynamic Trial-by-Trial Recoding of Task-Set Representations in the Frontoparietal Cortex Mediates Behavioral Flexibility

PubMed Central

Qiao, Lei; Zhang, Lijie

2017-01-01

Cognitive flexibility forms the core of the extraordinary ability of humans to adapt, but the precise neural mechanisms underlying our ability to nimbly shift between task sets remain poorly understood. Recent functional magnetic resonance imaging (fMRI) studies employing multivoxel pattern analysis (MVPA) have shown that a currently relevant task set can be decoded from activity patterns in the frontoparietal cortex, but whether these regions support the dynamic transformation of task sets from trial to trial is not clear. Here, we combined a cued task-switching protocol with human (both sexes) fMRI, and harnessed representational similarity analysis (RSA) to facilitate a novel assessment of trial-by-trial changes in neural task-set representations. We first used MVPA to define task-sensitive frontoparietal and visual regions and found that neural task-set representations on switch trials are less stably encoded than on repeat trials. We then exploited RSA to show that the neural representational pattern dissimilarity across consecutive trials is greater for switch trials than for repeat trials, and that the degree of this pattern dissimilarity predicts behavior. Moreover, the overall neural pattern of representational dissimilarities followed from the assumption that repeating sets, compared with switching sets, results in stronger neural task representations. Finally, when moving from cue to target phase within a trial, pattern dissimilarities tracked the transformation from previous-trial task representations to the currently relevant set. These results provide neural evidence for the longstanding assumptions of an effortful task-set reconfiguration process hampered by task-set inertia, and they demonstrate that frontoparietal and stimulus processing regions support “dynamic adaptive coding,” flexibly representing changing task sets in a trial-by-trial fashion. SIGNIFICANCE STATEMENT Humans can fluently switch between different tasks, reflecting an ability to dynamically configure “task sets,” rule representations that link stimuli to appropriate responses. Recent studies show that neural signals in frontal and parietal brain regions can tell us which of two tasks a person is currently performing. However, it is not known whether these regions are also involved in dynamically reconfiguring task-set representations when switching between tasks. Here we measured human brain activity during task switching and tracked the similarity of neural task-set representations from trial to trial. We show that frontal and parietal brain regions flexibly recode changing task sets in a trial-by-trial fashion, and that task-set similarity over consecutive trials predicts behavior. PMID:28972126

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

Adaptive EAGLE dynamic solution adaptation and grid quality enhancement

NASA Technical Reports Server (NTRS)

Luong, Phu Vinh; Thompson, J. F.; Gatlin, B.; Mastin, C. W.; Kim, H. J.

1992-01-01

In the effort described here, the elliptic grid generation procedure in the EAGLE grid code was separated from the main code into a subroutine, and a new subroutine which evaluates several grid quality measures at each grid point was added. The elliptic grid routine can now be called, either by a computational fluid dynamics (CFD) code to generate a new adaptive grid based on flow variables and quality measures through multiple adaptation, or by the EAGLE main code to generate a grid based on quality measure variables through static adaptation. Arrays of flow variables can be read into the EAGLE grid code for use in static adaptation as well. These major changes in the EAGLE adaptive grid system make it easier to convert any CFD code that operates on a block-structured grid (or single-block grid) into a multiple adaptive code.

The Brain Adapts to Dishonesty

PubMed Central

Garrett, Neil; Lazzaro, Stephanie C.; Ariely, Dan; Sharot, Tali

2016-01-01

Dishonesty is an integral part of our social world, influencing domains ranging from finance and politics to personal relationships. Anecdotally, digressions from a moral code are often described as a series of small breaches that grow over time. Here, we provide empirical evidence for a gradual escalation of self-serving dishonesty and reveal a neural mechanism supporting it. Behaviorally, we show that the extent to which participants engage in self-serving dishonesty increases with repetition. Using fMRI we show that signal reduction in the amygdala is sensitive to the history of dishonest behavior, consistent with adaptation. Critically, the extent of amygdala BOLD reduction to dishonesty on a present decision relative to the last, predicts the magnitude of escalation of self-serving dishonesty on the next decision. The findings uncover a biological mechanism that supports a “slippery slope”: what begins as small acts of dishonesty can escalate into larger instances. PMID:27775721

Architecture of enteric neural circuits involved in intestinal motility.

PubMed

Costa, M; Brookes, S H

2008-08-01

This short review describes the conceptual development in the search for the enteric neural circuits with the initial identifications of the classes of enteric neurons on the bases of their morphology, neurochemistry, biophysical properties, projections and connectivity. The discovery of the presence of multiple neurochemicals in the same nerve cells in specific combinations led to the concept of "chemical coding" and of "plurichemical transmission". The proposal that enteric reflexes are largely responsible for the propulsion of contents led to investigations of polarised reflex pathways and how these may be activated to generate the coordinated propulsive behaviour of the intestine. The research over the past decades attempted to integrate information of chemical neuroanatomy with functional studies, with the development of methods combining anatomical, functional and pharmacological techniques. This multidisciplinary strategy led to a full accounting of all functional classes of enteric neurons in the guinea-pig, and advanced wiring diagrams of the enteric neural circuits have been proposed. In parallel, investigations of the actual behaviour of the intestine during physiological motor activity have advanced with the development of spatio-temporal analysis from video recordings. The relation between neural pathways, their activities and the generation of patterns of motor activity remain largely unexplained. The enteric neural circuits appear not set in rigid programs but respond to different physico-chemical contents in an adaptable way (neuromechanical hypothesis). The generation of the complex repertoire of motor patterns results from the interplay of myogenic and neuromechanical mechanisms with spontaneous generation of migratory motor activity by enteric circuits.

Neural network with dynamically adaptable neurons

NASA Technical Reports Server (NTRS)

Tawel, Raoul (Inventor)

1994-01-01

This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

Reduced adaptability, but no fundamental disruption, of norm-based face coding following early visual deprivation from congenital cataracts.

PubMed

Rhodes, Gillian; Nishimura, Mayu; de Heering, Adelaide; Jeffery, Linda; Maurer, Daphne

2017-05-01

Faces are adaptively coded relative to visual norms that are updated by experience, and this adaptive coding is linked to face recognition ability. Here we investigated whether adaptive coding of faces is disrupted in individuals (adolescents and adults) who experience face recognition difficulties following visual deprivation from congenital cataracts in infancy. We measured adaptive coding using face identity aftereffects, where smaller aftereffects indicate less adaptive updating of face-coding mechanisms by experience. We also examined whether the aftereffects increase with adaptor identity strength, consistent with norm-based coding of identity, as in typical populations, or whether they show a different pattern indicating some more fundamental disruption of face-coding mechanisms. Cataract-reversal patients showed significantly smaller face identity aftereffects than did controls (Experiments 1 and 2). However, their aftereffects increased significantly with adaptor strength, consistent with norm-based coding (Experiment 2). Thus we found reduced adaptability but no fundamental disruption of norm-based face-coding mechanisms in cataract-reversal patients. Our results suggest that early visual experience is important for the normal development of adaptive face-coding mechanisms. © 2016 John Wiley & Sons Ltd.

Emotional facial expressions reduce neural adaptation to face identity.

PubMed

Gerlicher, Anna M V; van Loon, Anouk M; Scholte, H Steven; Lamme, Victor A F; van der Leij, Andries R

2014-05-01

In human social interactions, facial emotional expressions are a crucial source of information. Repeatedly presented information typically leads to an adaptation of neural responses. However, processing seems sustained with emotional facial expressions. Therefore, we tested whether sustained processing of emotional expressions, especially threat-related expressions, would attenuate neural adaptation. Neutral and emotional expressions (happy, mixed and fearful) of same and different identity were presented at 3 Hz. We used electroencephalography to record the evoked steady-state visual potentials (ssVEP) and tested to what extent the ssVEP amplitude adapts to the same when compared with different face identities. We found adaptation to the identity of a neutral face. However, for emotional faces, adaptation was reduced, decreasing linearly with negative valence, with the least adaptation to fearful expressions. This short and straightforward method may prove to be a valuable new tool in the study of emotional processing.

Auditory-neurophysiological responses to speech during early childhood: Effects of background noise

PubMed Central

White-Schwoch, Travis; Davies, Evan C.; Thompson, Elaine C.; Carr, Kali Woodruff; Nicol, Trent; Bradlow, Ann R.; Kraus, Nina

2015-01-01

Early childhood is a critical period of auditory learning, during which children are constantly mapping sounds to meaning. But learning rarely occurs under ideal listening conditions—children are forced to listen against a relentless din. This background noise degrades the neural coding of these critical sounds, in turn interfering with auditory learning. Despite the importance of robust and reliable auditory processing during early childhood, little is known about the neurophysiology underlying speech processing in children so young. To better understand the physiological constraints these adverse listening scenarios impose on speech sound coding during early childhood, auditory-neurophysiological responses were elicited to a consonant-vowel syllable in quiet and background noise in a cohort of typically-developing preschoolers (ages 3–5 yr). Overall, responses were degraded in noise: they were smaller, less stable across trials, slower, and there was poorer coding of spectral content and the temporal envelope. These effects were exacerbated in response to the consonant transition relative to the vowel, suggesting that the neural coding of spectrotemporally-dynamic speech features is more tenuous in noise than the coding of static features—even in children this young. Neural coding of speech temporal fine structure, however, was more resilient to the addition of background noise than coding of temporal envelope information. Taken together, these results demonstrate that noise places a neurophysiological constraint on speech processing during early childhood by causing a breakdown in neural processing of speech acoustics. These results may explain why some listeners have inordinate difficulties understanding speech in noise. Speech-elicited auditory-neurophysiological responses offer objective insight into listening skills during early childhood by reflecting the integrity of neural coding in quiet and noise; this paper documents typical response properties in this age group. These normative metrics may be useful clinically to evaluate auditory processing difficulties during early childhood. PMID:26113025

How distinct is the coding of face identity and expression? Evidence for some common dimensions in face space.

PubMed

Rhodes, Gillian; Pond, Stephen; Burton, Nichola; Kloth, Nadine; Jeffery, Linda; Bell, Jason; Ewing, Louise; Calder, Andrew J; Palermo, Romina

2015-09-01

Traditional models of face perception emphasize distinct routes for processing face identity and expression. These models have been highly influential in guiding neural and behavioural research on the mechanisms of face perception. However, it is becoming clear that specialised brain areas for coding identity and expression may respond to both attributes and that identity and expression perception can interact. Here we use perceptual aftereffects to demonstrate the existence of dimensions in perceptual face space that code both identity and expression, further challenging the traditional view. Specifically, we find a significant positive association between face identity aftereffects and expression aftereffects, which dissociates from other face (gaze) and non-face (tilt) aftereffects. Importantly, individual variation in the adaptive calibration of these common dimensions significantly predicts ability to recognize both identity and expression. These results highlight the role of common dimensions in our ability to recognize identity and expression, and show why the high-level visual processing of these attributes is not entirely distinct. Copyright © 2015 Elsevier B.V. All rights reserved.

Neural Coding of Formant-Exaggerated Speech in the Infant Brain

ERIC Educational Resources Information Center

Zhang, Yang; Koerner, Tess; Miller, Sharon; Grice-Patil, Zach; Svec, Adam; Akbari, David; Tusler, Liz; Carney, Edward

2011-01-01

Speech scientists have long proposed that formant exaggeration in infant-directed speech plays an important role in language acquisition. This event-related potential (ERP) study investigated neural coding of formant-exaggerated speech in 6-12-month-old infants. Two synthetic /i/ vowels were presented in alternating blocks to test the effects of…

Adaptation to conflict via context-driven anticipatory signals in the dorsomedial prefrontal cortex.

PubMed

Horga, Guillermo; Maia, Tiago V; Wang, Pengwei; Wang, Zhishun; Marsh, Rachel; Peterson, Bradley S

2011-11-09

Behavioral interference elicited by competing response tendencies adapts to contextual changes. Recent nonhuman primate research suggests a key mnemonic role of distinct prefrontal cells in supporting such context-driven behavioral adjustments by maintaining conflict information across trials, but corresponding prefrontal functions have yet to be probed in humans. Using event-related functional magnetic resonance imaging, we investigated the human neural substrates of contextual adaptations to conflict. We found that a neural system comprising the rostral dorsomedial prefrontal cortex and portions of the dorsolateral prefrontal cortex specifically encodes the history of previously experienced conflict and influences subsequent adaptation to conflict on a trial-by-trial basis. This neural system became active in anticipation of stimulus onsets during preparatory periods and interacted with a second neural system engaged during the processing of conflict. Our findings suggest that a dynamic interaction between a system that represents conflict history and a system that resolves conflict underlies the contextual adaptation to conflict.

Adaptation to Conflict via Context-Driven Anticipatory Signals in the Dorsomedial Prefrontal Cortex

PubMed Central

Horga, Guillermo; Maia, Tiago V.; Wang, Pengwei; Wang, Zhishun; Marsh, Rachel; Peterson, Bradley S.

2011-01-01

Behavioral interference elicited by competing response tendencies adapts to contextual changes. Recent nonhuman primate research suggests a key mnemonic role of distinct prefrontal cells in supporting such context-driven behavioral adjustments by maintaining conflict information across trials, but corresponding prefrontal functions have yet to be probed in humans. Using event-related functional magnetic resonance imaging (fMRI), we investigated the human neural substrates of contextual adaptations to conflict. We found that a neural system comprising the rostral dorsomedial prefrontal cortex and portions of the dorsolateral prefrontal cortex specifically encodes the history of previously experienced conflict and influences subsequent adaptation to conflict on a trial-by-trial basis. This neural system became active in anticipation of stimulus onsets during preparatory periods and interacted with a second neural system engaged during the processing of conflict. Our findings suggest that a dynamic interaction between a system that represents conflict history and a system that resolves conflict underlies the contextual adaptation to conflict. PMID:22072672

Evolving RBF neural networks for adaptive soft-sensor design.

PubMed

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

PubMed

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

PubMed Central

Rossi-Pool, Román; Salinas, Emilio; Zainos, Antonio; Alvarez, Manuel; Vergara, José; Parga, Néstor; Romo, Ranulfo

2016-01-01

The problem of neural coding in perceptual decision making revolves around two fundamental questions: (i) How are the neural representations of sensory stimuli related to perception, and (ii) what attributes of these neural responses are relevant for downstream networks, and how do they influence decision making? We studied these two questions by recording neurons in primary somatosensory (S1) and dorsal premotor (DPC) cortex while trained monkeys reported whether the temporal pattern structure of two sequential vibrotactile stimuli (of equal mean frequency) was the same or different. We found that S1 neurons coded the temporal patterns in a literal way and only during the stimulation periods and did not reflect the monkeys’ decisions. In contrast, DPC neurons coded the stimulus patterns as broader categories and signaled them during the working memory, comparison, and decision periods. These results show that the initial sensory representation is transformed into an intermediate, more abstract categorical code that combines past and present information to ultimately generate a perceptually informed choice. PMID:27872293

Using Neural Net Technology To Enhance the Efficiency of a Computer Adaptive Testing Application.

ERIC Educational Resources Information Center

Van Nelson, C.; Henriksen, Larry W.

The potential for computer adaptive testing (CAT) has been well documented. In order to improve the efficiency of this process, it may be possible to utilize a neural network, or more specifically, a back propagation neural network. The paper asserts that in order to accomplish this end, it must be shown that grouping examinees by ability as…

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

Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression

NASA Astrophysics Data System (ADS)

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

2016-04-01

Microsaccades are very small eye movements during fixation. Experimentally, they have been found to play an important role in visual information processing. However, neural responses induced by microsaccades are not yet well understood and are rarely studied theoretically. Here we propose a network model with a cascading adaptation including both retinal adaptation and short-term depression (STD) at thalamocortical synapses. In the neural network model, we compare the microsaccade-induced neural responses in the presence of STD and those without STD. It is found that the cascading with STD can give rise to faster and sharper responses to microsaccades. Moreover, STD can enhance response effectiveness and sensitivity to microsaccadic spatiotemporal changes, suggesting improved detection of small eye movements (or moving visual objects). We also explore the mechanism of the response properties in the model. Our studies strongly indicate that STD plays an important role in neural responses to microsaccades. Our model considers simultaneously retinal adaptation and STD at thalamocortical synapses in the study of microsaccade-induced neural activity, and may be useful for further investigation of the functional roles of microsaccades in visual information processing.

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

NASA Technical Reports Server (NTRS)

Momoh, James A.; Wang, Yanchun; Dolce, James L.

1997-01-01

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

DATA-MEAns: an open source tool for the classification and management of neural ensemble recordings.

PubMed

Bonomini, María P; Ferrandez, José M; Bolea, Jose Angel; Fernandez, Eduardo

2005-10-30

The number of laboratories using techniques that allow to acquire simultaneous recordings of as many units as possible is considerably increasing. However, the development of tools used to analyse this multi-neuronal activity is generally lagging behind the development of the tools used to acquire these data. Moreover, the data exchange between research groups using different multielectrode acquisition systems is hindered by commercial constraints such as exclusive file structures, high priced licenses and hard policies on intellectual rights. This paper presents a free open-source software for the classification and management of neural ensemble data. The main goal is to provide a graphical user interface that links the experimental data to a basic set of routines for analysis, visualization and classification in a consistent framework. To facilitate the adaptation and extension as well as the addition of new routines, tools and algorithms for data analysis, the source code and documentation are freely available.

The basolateral amygdala in reward learning and addiction

PubMed Central

Wassum, Kate M.; Izquierdo, Alicia

2015-01-01

Sophisticated behavioral paradigms partnered with the emergence of increasingly selective techniques to target the basolateral amygdala (BLA) have resulted in an enhanced understanding of the role of this nucleus in learning and using reward information. Due to the wide variety of behavioral approaches many questions remain on the circumscribed role of BLA in appetitive behavior. In this review, we integrate conclusions of BLA function in reward-related behavior using traditional interference techniques (lesion, pharmacological inactivation) with those using newer methodological approaches in experimental animals that allow in vivo manipulation of cell type-specific populations and neural recordings. Secondly, from a review of appetitive behavioral tasks in rodents and monkeys and recent computational models of reward procurement, we derive evidence for BLA as a neural integrator of reward value, history, and cost parameters. Taken together, BLA codes specific and temporally dynamic outcome representations in a distributed network to orchestrate adaptive responses. We provide evidence that experiences with opiates and psychostimulants alter these outcome representations in BLA, resulting in long-term modified action. PMID:26341938

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model

PubMed Central

Ryu, Stephen I.

2017-01-01

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain–machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses. SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis. PMID:28087767

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

PubMed Central

Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

2013-01-01

Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

PubMed

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-07-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

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

Luo, Shaohua; Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021; Wu, Songli

2015-07-15

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in themore » closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.« less

Deconvolution using a neural network

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

Lehman, S.K.

1990-11-15

Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

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.

Single neural code for blur in subjects with different interocular optical blur orientation

PubMed Central

Radhakrishnan, Aiswaryah; Sawides, Lucie; Dorronsoro, Carlos; Peli, Eli; Marcos, Susana

2015-01-01

The ability of the visual system to compensate for differences in blur orientation between eyes is not well understood. We measured the orientation of the internal blur code in both eyes of the same subject monocularly by presenting pairs of images blurred with real ocular point spread functions (PSFs) of similar blur magnitude but varying in orientations. Subjects assigned a level of confidence to their selection of the best perceived image in each pair. Using a classification-images–inspired paradigm and applying a reverse correlation technique, a classification map was obtained from the weighted averages of the PSFs, representing the internal blur code. Positive and negative neural PSFs were obtained from the classification map, representing the neural blur for best and worse perceived blur, respectively. The neural PSF was found to be highly correlated in both eyes, even for eyes with different ocular PSF orientations (rPos = 0.95; rNeg = 0.99; p < 0.001). We found that in subjects with similar and with different ocular PSF orientations between eyes, the orientation of the positive neural PSF was closer to the orientation of the ocular PSF of the eye with the better optical quality (average difference was ∼10°), while the orientation of the positive and negative neural PSFs tended to be orthogonal. These results suggest a single internal code for blur with orientation driven by the orientation of the optical blur of the eye with better optical quality. PMID:26114678

Deep Learning Methods for Improved Decoding of Linear Codes

NASA Astrophysics Data System (ADS)

Nachmani, Eliya; Marciano, Elad; Lugosch, Loren; Gross, Warren J.; Burshtein, David; Be'ery, Yair

2018-02-01

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.

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

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text.

PubMed

Duarte, Francisco; Martins, Bruno; Pinto, Cátia Sousa; Silva, Mário J

2018-04-01

We address the assignment of ICD-10 codes for causes of death by analyzing free-text descriptions in death certificates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. We leverage a deep neural network that combines word embeddings, recurrent units, and neural attention, for the generation of intermediate representations of the textual contents. The neural network also explores the hierarchical nature of the input data, by building representations from the sequences of words within individual fields, which are then combined according to the sequences of fields that compose the inputs. Moreover, we explore innovative mechanisms for initializing the weights of the final nodes of the network, leveraging co-occurrences between classes together with the hierarchical structure of ICD-10. Experimental results attest to the contribution of the different neural network components. Our best model achieves accuracy scores over 89%, 81%, and 76%, respectively for ICD-10 chapters, blocks, and full-codes. Through examples, we also show that our method can produce interpretable results, useful for public health surveillance. Copyright © 2018 Elsevier Inc. All rights reserved.

Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

PubMed

Gao, Hui; Song, Yongduan; Wen, Changyun

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

Vector Adaptive/Predictive Encoding Of Speech

NASA Technical Reports Server (NTRS)

Chen, Juin-Hwey; Gersho, Allen

1989-01-01

Vector adaptive/predictive technique for digital encoding of speech signals yields decoded speech of very good quality after transmission at coding rate of 9.6 kb/s and of reasonably good quality at 4.8 kb/s. Requires 3 to 4 million multiplications and additions per second. Combines advantages of adaptive/predictive coding, and code-excited linear prediction, yielding speech of high quality but requires 600 million multiplications and additions per second at encoding rate of 4.8 kb/s. Vector adaptive/predictive coding technique bridges gaps in performance and complexity between adaptive/predictive coding and code-excited linear prediction.

Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

Performance optimization of PM-16QAM transmission system enabled by real-time self-adaptive coding.

PubMed

Qu, Zhen; Li, Yao; Mo, Weiyang; Yang, Mingwei; Zhu, Shengxiang; Kilper, Daniel C; Djordjevic, Ivan B

2017-10-15

We experimentally demonstrate self-adaptive coded 5×100  Gb/s WDM polarization multiplexed 16 quadrature amplitude modulation transmission over a 100 km fiber link, which is enabled by a real-time control plane. The real-time optical signal-to-noise ratio (OSNR) is measured using an optical performance monitoring device. The OSNR measurement is processed and fed back using control plane logic and messaging to the transmitter side for code adaptation, where the binary data are adaptively encoded with three types of low-density parity-check (LDPC) codes with code rates of 0.8, 0.75, and 0.7 of large girth. The total code-adaptation latency is measured to be 2273 ms. Compared with transmission without adaptation, average net capacity improvements of 102%, 36%, and 7.5% are obtained, respectively, by adaptive LDPC coding.

Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

PubMed Central

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots. PMID:26441629

Method and system for pattern analysis using a coarse-coded neural network

NASA Technical Reports Server (NTRS)

Spirkovska, Liljana (Inventor); Reid, Max B. (Inventor)

1994-01-01

A method and system for performing pattern analysis with a neural network coarse-coding a pattern to be analyzed so as to form a plurality of sub-patterns collectively defined by data. Each of the sub-patterns comprises sets of pattern data. The neural network includes a plurality fields, each field being associated with one of the sub-patterns so as to receive the sub-pattern data therefrom. Training and testing by the neural network then proceeds in the usual way, with one modification: the transfer function thresholds the value obtained from summing the weighted products of each field over all sub-patterns associated with each pattern being analyzed by the system.

Decoding of finger trajectory from ECoG using deep learning.

PubMed

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Decoding of finger trajectory from ECoG using deep learning

NASA Astrophysics Data System (ADS)

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Objective. Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. Approach. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. Main results. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. Significance. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

What is adapted in face adaptation? The neural representations of expression in the human visual system.

PubMed

Fox, Christopher J; Barton, Jason J S

2007-01-05

The neural representation of facial expression within the human visual system is not well defined. Using an adaptation paradigm, we examined aftereffects on expression perception produced by various stimuli. Adapting to a face, which was used to create morphs between two expressions, substantially biased expression perception within the morphed faces away from the adapting expression. This adaptation was not based on low-level image properties, as a different image of the same person displaying that expression produced equally robust aftereffects. Smaller but significant aftereffects were generated by images of different individuals, irrespective of gender. Non-face visual, auditory, or verbal representations of emotion did not generate significant aftereffects. These results suggest that adaptation affects at least two neural representations of expression: one specific to the individual (not the image), and one that represents expression across different facial identities. The identity-independent aftereffect suggests the existence of a 'visual semantic' for facial expression in the human visual system.

Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

PubMed

Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

2008-06-01

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone.

PubMed

Chen, Mou; Tao, Gang

2016-08-01

In this paper, an adaptive neural fault-tolerant control scheme is proposed and analyzed for a class of uncertain nonlinear large-scale systems with unknown dead zone and external disturbances. To tackle the unknown nonlinear interaction functions in the large-scale system, the radial basis function neural network (RBFNN) is employed to approximate them. To further handle the unknown approximation errors and the effects of the unknown dead zone and external disturbances, integrated as the compounded disturbances, the corresponding disturbance observers are developed for their estimations. Based on the outputs of the RBFNN and the disturbance observer, the adaptive neural fault-tolerant control scheme is designed for uncertain nonlinear large-scale systems by using a decentralized backstepping technique. The closed-loop stability of the adaptive control system is rigorously proved via Lyapunov analysis and the satisfactory tracking performance is achieved under the integrated effects of unknown dead zone, actuator fault, and unknown external disturbances. Simulation results of a mass-spring-damper system are given to illustrate the effectiveness of the proposed adaptive neural fault-tolerant control scheme for uncertain nonlinear large-scale systems.

Neural adaptation to thin and fat bodies in the fusiform body area and middle occipital gyrus: an fMRI adaptation study.

PubMed

Hummel, Dennis; Rudolf, Anne K; Brandi, Marie-Luise; Untch, Karl-Heinz; Grabhorn, Ralph; Hampel, Harald; Mohr, Harald M

2013-12-01

Visual perception can be strongly biased due to exposure to specific stimuli in the environment, often causing neural adaptation and visual aftereffects. In this study, we investigated whether adaptation to certain body shapes biases the perception of the own body shape. Furthermore, we aimed to evoke neural adaptation to certain body shapes. Participants completed a behavioral experiment (n = 14) to rate manipulated pictures of their own bodies after adaptation to demonstratively thin or fat pictures of their own bodies. The same stimuli were used in a second experiment (n = 16) using functional magnetic resonance imaging (fMRI) adaptation. In the behavioral experiment, after adapting to a thin picture of the own body participants also judged a thinner than actual body picture to be the most realistic and vice versa, resembling a typical aftereffect. The fusiform body area (FBA) and the right middle occipital gyrus (rMOG) show neural adaptation to specific body shapes while the extrastriate body area (EBA) bilaterally does not. The rMOG cluster is highly selective for bodies and perhaps body parts. The findings of the behavioral experiment support the existence of a perceptual body shape aftereffect, resulting from a specific adaptation to thin and fat pictures of one's own body. The fMRI results imply that body shape adaptation occurs in the FBA and the rMOG. The role of the EBA in body shape processing remains unclear. The results are also discussed in the light of clinical body image disturbances. Copyright © 2012 Wiley Periodicals, Inc.

Neural Coding of Reward Magnitude in the Orbitofrontal Cortex of the Rat during a Five-Odor Olfactory Discrimination Task

ERIC Educational Resources Information Center

van Duuren, Esther; Nieto Escamez, Francisco A.; Joosten, Ruud N. J. M. A.; Visser, Rein; Mulder, Antonius B.; Pennartz, Cyriel M. A.

2007-01-01

The orbitofrontal cortex (OBFc) has been suggested to code the motivational value of environmental stimuli and to use this information for the flexible guidance of goal-directed behavior. To examine whether information regarding reward prediction is quantitatively represented in the rat OBFc, neural activity was recorded during an olfactory…

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.

Neural coding in barrel cortex during whisker-guided locomotion

PubMed Central

Sofroniew, Nicholas James; Vlasov, Yurii A; Hires, Samuel Andrew; Freeman, Jeremy; Svoboda, Karel

2015-01-01

Animals seek out relevant information by moving through a dynamic world, but sensory systems are usually studied under highly constrained and passive conditions that may not probe important dimensions of the neural code. Here, we explored neural coding in the barrel cortex of head-fixed mice that tracked walls with their whiskers in tactile virtual reality. Optogenetic manipulations revealed that barrel cortex plays a role in wall-tracking. Closed-loop optogenetic control of layer 4 neurons can substitute for whisker-object contact to guide behavior resembling wall tracking. We measured neural activity using two-photon calcium imaging and extracellular recordings. Neurons were tuned to the distance between the animal snout and the contralateral wall, with monotonic, unimodal, and multimodal tuning curves. This rich representation of object location in the barrel cortex could not be predicted based on simple stimulus-response relationships involving individual whiskers and likely emerges within cortical circuits. DOI: http://dx.doi.org/10.7554/eLife.12559.001 PMID:26701910

Neural networks for data compression and invariant image recognition

NASA Technical Reports Server (NTRS)

Gardner, Sheldon

1989-01-01

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

Facial expression coding in children and adolescents with autism: Reduced adaptability but intact norm-based coding.

PubMed

Rhodes, Gillian; Burton, Nichola; Jeffery, Linda; Read, Ainsley; Taylor, Libby; Ewing, Louise

2018-05-01

Individuals with autism spectrum disorder (ASD) can have difficulty recognizing emotional expressions. Here, we asked whether the underlying perceptual coding of expression is disrupted. Typical individuals code expression relative to a perceptual (average) norm that is continuously updated by experience. This adaptability of face-coding mechanisms has been linked to performance on various face tasks. We used an adaptation aftereffect paradigm to characterize expression coding in children and adolescents with autism. We asked whether face expression coding is less adaptable in autism and whether there is any fundamental disruption of norm-based coding. If expression coding is norm-based, then the face aftereffects should increase with adaptor expression strength (distance from the average expression). We observed this pattern in both autistic and typically developing participants, suggesting that norm-based coding is fundamentally intact in autism. Critically, however, expression aftereffects were reduced in the autism group, indicating that expression-coding mechanisms are less readily tuned by experience. Reduced adaptability has also been reported for coding of face identity and gaze direction. Thus, there appears to be a pervasive lack of adaptability in face-coding mechanisms in autism, which could contribute to face processing and broader social difficulties in the disorder. © 2017 The British Psychological Society.

Improved methods in neural network-based adaptive output feedback control, with applications to flight control

NASA Astrophysics Data System (ADS)

Kim, Nakwan

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

PubMed

Wang, Leimin; Shen, Yi; Zhang, Guodong

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

Adaptive coding of the value of social cues with oxytocin, an fMRI study in autism spectrum disorder.

PubMed

Andari, Elissar; Richard, Nathalie; Leboyer, Marion; Sirigu, Angela

2016-03-01

The neuropeptide oxytocin (OT) is one of the major targets of research in neuroscience, with respect to social functioning. Oxytocin promotes social skills and improves the quality of face processing in individuals with social dysfunctions such as autism spectrum disorder (ASD). Although one of OT's key functions is to promote social behavior during dynamic social interactions, the neural correlates of this function remain unknown. Here, we combined acute intranasal OT (IN-OT) administration (24 IU) and fMRI with an interactive ball game and a face-matching task in individuals with ASD (N = 20). We found that IN-OT selectively enhanced the brain activity of early visual areas in response to faces as compared to non-social stimuli. OT inhalation modulated the BOLD activity of amygdala and hippocampus in a context-dependent manner. Interestingly, IN-OT intake enhanced the activity of mid-orbitofrontal cortex in response to a fair partner, and insula region in response to an unfair partner. These OT-induced neural responses were accompanied by behavioral improvements in terms of allocating appropriate feelings of trust toward different partners' profiles. Our findings suggest that OT impacts the brain activity of key areas implicated in attention and emotion regulation in an adaptive manner, based on the value of social cues. Copyright © 2016 Elsevier Ltd. All rights reserved.

The predictive roles of neural oscillations in speech motor adaptability.

PubMed

Sengupta, Ranit; Nasir, Sazzad M

2016-06-01

The human speech system exhibits a remarkable flexibility by adapting to alterations in speaking environments. While it is believed that speech motor adaptation under altered sensory feedback involves rapid reorganization of speech motor networks, the mechanisms by which different brain regions communicate and coordinate their activity to mediate adaptation remain unknown, and explanations of outcome differences in adaption remain largely elusive. In this study, under the paradigm of altered auditory feedback with continuous EEG recordings, the differential roles of oscillatory neural processes in motor speech adaptability were investigated. The predictive capacities of different EEG frequency bands were assessed, and it was found that theta-, beta-, and gamma-band activities during speech planning and production contained significant and reliable information about motor speech adaptability. It was further observed that these bands do not work independently but interact with each other suggesting an underlying brain network operating across hierarchically organized frequency bands to support motor speech adaptation. These results provide novel insights into both learning and disorders of speech using time frequency analysis of neural oscillations. Copyright © 2016 the American Physiological Society.

Robust information propagation through noisy neural circuits

PubMed Central

Pouget, Alexandre

2017-01-01

Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina’s performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with “differential correlations”, which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can—in some cases—optimize robustness against noise. PMID:28419098

Adaptive neural network/expert system that learns fault diagnosis for different structures

NASA Astrophysics Data System (ADS)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model.

PubMed

Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V

2017-02-15

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses. SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis. Copyright © 2017 the authors 0270-6474/17/371721-12$15.00/0.

Reduced adaptability, but no fundamental disruption, of norm-based face-coding mechanisms in cognitively able children and adolescents with autism.

PubMed

Rhodes, Gillian; Ewing, Louise; Jeffery, Linda; Avard, Eleni; Taylor, Libby

2014-09-01

Faces are adaptively coded relative to visual norms that are updated by experience. This coding is compromised in autism and the broader autism phenotype, suggesting that atypical adaptive coding of faces may be an endophenotype for autism. Here we investigate the nature of this atypicality, asking whether adaptive face-coding mechanisms are fundamentally altered, or simply less responsive to experience, in autism. We measured adaptive coding, using face identity aftereffects, in cognitively able children and adolescents with autism and neurotypical age- and ability-matched participants. We asked whether these aftereffects increase with adaptor identity strength as in neurotypical populations, or whether they show a different pattern indicating a more fundamental alteration in face-coding mechanisms. As expected, face identity aftereffects were reduced in the autism group, but they nevertheless increased with adaptor strength, like those of our neurotypical participants, consistent with norm-based coding of face identity. Moreover, their aftereffects correlated positively with face recognition ability, consistent with an intact functional role for adaptive coding in face recognition ability. We conclude that adaptive norm-based face-coding mechanisms are basically intact in autism, but are less readily calibrated by experience. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mechanism on brain information processing: Energy coding

NASA Astrophysics Data System (ADS)

Wang, Rubin; Zhang, Zhikang; Jiao, Xianfa

2006-09-01

According to the experimental result of signal transmission and neuronal energetic demands being tightly coupled to information coding in the cerebral cortex, the authors present a brand new scientific theory that offers a unique mechanism for brain information processing. They demonstrate that the neural coding produced by the activity of the brain is well described by the theory of energy coding. Due to the energy coding model's ability to reveal mechanisms of brain information processing based upon known biophysical properties, they cannot only reproduce various experimental results of neuroelectrophysiology but also quantitatively explain the recent experimental results from neuroscientists at Yale University by means of the principle of energy coding. Due to the theory of energy coding to bridge the gap between functional connections within a biological neural network and energetic consumption, they estimate that the theory has very important consequences for quantitative research of cognitive function.

Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model.

PubMed

Westendorff, Stephanie; Kuang, Shenbing; Taghizadeh, Bahareh; Donchin, Opher; Gail, Alexander

2015-04-01

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement ("jump") consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation. Copyright © 2015 the American Physiological Society.

Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model

PubMed Central

Westendorff, Stephanie; Kuang, Shenbing; Taghizadeh, Bahareh; Donchin, Opher

2015-01-01

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement (“jump”) consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation. PMID:25609106

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

NASA Technical Reports Server (NTRS)

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

2003-01-01

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Suppressive and enhancing effects in early visual cortex during illusory shape perception: A comment on.

PubMed

Moors, Pieter

2015-01-01

In a recent functional magnetic resonance imaging study, Kok and de Lange (2014) observed that BOLD activity for a Kanizsa illusory shape stimulus, in which pacmen-like inducers elicit an illusory shape percept, was either enhanced or suppressed relative to a nonillusory control configuration depending on whether the spatial profile of BOLD activity in early visual cortex was related to the illusory shape or the inducers, respectively. The authors argued that these findings fit well with the predictive coding framework, because top-down predictions related to the illusory shape are not met with bottom-up sensory input and hence the feedforward error signal is enhanced. Conversely, for the inducing elements, there is a match between top-down predictions and input, leading to a decrease in error. Rather than invoking predictive coding as the explanatory framework, the suppressive effect related to the inducers might be caused by neural adaptation to perceptually stable input due to the trial sequence used in the experiment.

Theta phase precession and phase selectivity: a cognitive device description of neural coding

NASA Astrophysics Data System (ADS)

Zalay, Osbert C.; Bardakjian, Berj L.

2009-06-01

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to guide downstream processes such as phase precession, because of their demonstrated frequency-selective properties.

Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

PubMed

Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

2014-01-01

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

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

Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

NASA Astrophysics Data System (ADS)

Aoyagi, Toshio; Nomura, Masaki

1999-08-01

Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

ANNA: A Convolutional Neural Network Code for Spectroscopic Analysis

NASA Astrophysics Data System (ADS)

Lee-Brown, Donald; Anthony-Twarog, Barbara J.; Twarog, Bruce A.

2018-01-01

We present ANNA, a Python-based convolutional neural network code for the automated analysis of stellar spectra. ANNA provides a flexible framework that allows atmospheric parameters such as temperature and metallicity to be determined with accuracies comparable to those of established but less efficient techniques. ANNA performs its parameterization extremely quickly; typically several thousand spectra can be analyzed in less than a second. Additionally, the code incorporates features which greatly speed up the training process necessary for the neural network to measure spectra accurately, resulting in a tool that can easily be run on a single desktop or laptop computer. Thus, ANNA is useful in an era when spectrographs increasingly have the capability to collect dozens to hundreds of spectra each night. This talk will cover the basic features included in ANNA and demonstrate its performance in two use cases: an open cluster abundance analysis involving several hundred spectra, and a metal-rich field star study. Applicability of the code to large survey datasets will also be discussed.

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.

Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder.

PubMed

Rothkirch, Marcus; Tonn, Jonas; Köhler, Stephan; Sterzer, Philipp

2017-04-01

According to current concepts, major depressive disorder is strongly related to dysfunctional neural processing of motivational information, entailing impairments in reinforcement learning. While computational modelling can reveal the precise nature of neural learning signals, it has not been used to study learning-related neural dysfunctions in unmedicated patients with major depressive disorder so far. We thus aimed at comparing the neural coding of reward and punishment prediction errors, representing indicators of neural learning-related processes, between unmedicated patients with major depressive disorder and healthy participants. To this end, a group of unmedicated patients with major depressive disorder (n = 28) and a group of age- and sex-matched healthy control participants (n = 30) completed an instrumental learning task involving monetary gains and losses during functional magnetic resonance imaging. The two groups did not differ in their learning performance. Patients and control participants showed the same level of prediction error-related activity in the ventral striatum and the anterior insula. In contrast, neural coding of reward prediction errors in the medial orbitofrontal cortex was reduced in patients. Moreover, neural reward prediction error signals in the medial orbitofrontal cortex and ventral striatum showed negative correlations with anhedonia severity. Using a standard instrumental learning paradigm we found no evidence for an overall impairment of reinforcement learning in medication-free patients with major depressive disorder. Importantly, however, the attenuated neural coding of reward in the medial orbitofrontal cortex and the relation between anhedonia and reduced reward prediction error-signalling in the medial orbitofrontal cortex and ventral striatum likely reflect an impairment in experiencing pleasure from rewarding events as a key mechanism of anhedonia in major depressive disorder. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Anticipatory anxiety disrupts neural valuation during risky choice.

PubMed

Engelmann, Jan B; Meyer, Friederike; Fehr, Ernst; Ruff, Christian C

2015-02-18

Incidental negative emotions unrelated to the current task, such as background anxiety, can strongly influence decisions. This is most evident in psychiatric disorders associated with generalized emotional disturbances. However, the neural mechanisms by which incidental emotions may affect choices remain poorly understood. Here we study the effects of incidental anxiety on human risky decision making, focusing on both behavioral preferences and their underlying neural processes. Although observable choices remained stable across affective contexts with high and low incidental anxiety, we found a clear change in neural valuation signals: during high incidental anxiety, activity in ventromedial prefrontal cortex and ventral striatum showed a marked reduction in (1) neural coding of the expected subjective value (ESV) of risky options, (2) prediction of observed choices, (3) functional coupling with other areas of the valuation system, and (4) baseline activity. At the same time, activity in the anterior insula showed an increase in coding the negative ESV of risky lotteries, and this neural activity predicted whether the risky lotteries would be rejected. This pattern of results suggests that incidental anxiety can shift the focus of neural valuation from possible positive consequences to anticipated negative consequences of choice options. Moreover, our findings show that these changes in neural value coding can occur in the absence of changes in overt behavior. This suggest a possible pathway by which background anxiety may lead to the development of chronic reward desensitization and a maladaptive focus on negative cognitions, as prevalent in affective and anxiety disorders. Copyright © 2015 the authors 0270-6474/15/353085-15$15.00/0.

Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

PubMed

Xia, Kewei; Huo, Wei

2016-05-01

This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Hardware Acceleration of Adaptive Neural Algorithms.

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

James, Conrad D.

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - worldmore » conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.« less

A neural learning classifier system with self-adaptive constructivism for mobile robot control.

PubMed

Hurst, Jacob; Bull, Larry

2006-01-01

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

Application of Adaptive Autopilot Designs for an Unmanned Aerial Vehicle

NASA Technical Reports Server (NTRS)

Shin, Yoonghyun; Calise, Anthony J.; Motter, Mark A.

2005-01-01

This paper summarizes the application of two adaptive approaches to autopilot design, and presents an evaluation and comparison of the two approaches in simulation for an unmanned aerial vehicle. One approach employs two-stage dynamic inversion and the other employs feedback dynamic inversions based on a command augmentation system. Both are augmented with neural network based adaptive elements. The approaches permit adaptation to both parametric uncertainty and unmodeled dynamics, and incorporate a method that permits adaptation during periods of control saturation. Simulation results for an FQM-117B radio controlled miniature aerial vehicle are presented to illustrate the performance of the neural network based adaptation.

Image Understanding by Image-Seeking Adaptive Networks (ISAN).

DTIC Science & Technology

1987-08-10

our reserch on adaptive neural networks in the visual and sensory-motor cortex of cats. We demonstrate that, under certain conditions, plasticity is...understanding in organisms proceeds directly from adaptively seeking whole images and not via a preliminary analysis of elementary features, followed by object...empirical reserch has always been that ultimately any neural system has to serve behavior and that behavior serves survival. Evolutionary selection makes it

Differential receptive field organizations give rise to nearly identical neural correlations across three parallel sensory maps in weakly electric fish

PubMed Central

2017-01-01

Understanding how neural populations encode sensory information thereby leading to perception and behavior (i.e., the neural code) remains an important problem in neuroscience. When investigating the neural code, one must take into account the fact that neural activities are not independent but are actually correlated with one another. Such correlations are seen ubiquitously and have a strong impact on neural coding. Here we investigated how differences in the antagonistic center-surround receptive field (RF) organization across three parallel sensory maps influence correlations between the activities of electrosensory pyramidal neurons. Using a model based on known anatomical differences in receptive field center size and overlap, we initially predicted large differences in correlated activity across the maps. However, in vivo electrophysiological recordings showed that, contrary to modeling predictions, electrosensory pyramidal neurons across all three segments displayed nearly identical correlations. To explain this surprising result, we incorporated the effects of RF surround in our model. By systematically varying both the RF surround gain and size relative to that of the RF center, we found that multiple RF structures gave rise to similar levels of correlation. In particular, incorporating known physiological differences in RF structure between the three maps in our model gave rise to similar levels of correlation. Our results show that RF center overlap alone does not determine correlations which has important implications for understanding how RF structure influences correlated neural activity. PMID:28863136

Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery

PubMed Central

Taravat, Alireza; Oppelt, Natascha

2014-01-01

Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR), as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM) and MultiLayer Perceptron (MLP) neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN) model generates poor accuracies. PMID:25474376

ER fluid applications to vibration control devices and an adaptive neural-net controller

NASA Astrophysics Data System (ADS)

Morishita, Shin; Ura, Tamaki

1993-07-01

Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

Studying the neural bases of prism adaptation using fMRI: A technical and design challenge.

PubMed

Bultitude, Janet H; Farnè, Alessandro; Salemme, Romeo; Ibarrola, Danielle; Urquizar, Christian; O'Shea, Jacinta; Luauté, Jacques

2017-12-01

Prism adaptation induces rapid recalibration of visuomotor coordination. The neural mechanisms of prism adaptation have come under scrutiny since the observations that the technique can alleviate hemispatial neglect following stroke, and can alter spatial cognition in healthy controls. Relative to non-imaging behavioral studies, fMRI investigations of prism adaptation face several challenges arising from the confined physical environment of the scanner and the supine position of the participants. Any researcher who wishes to administer prism adaptation in an fMRI environment must adjust their procedures enough to enable the experiment to be performed, but not so much that the behavioral task departs too much from true prism adaptation. Furthermore, the specific temporal dynamics of behavioral components of prism adaptation present additional challenges for measuring their neural correlates. We developed a system for measuring the key features of prism adaptation behavior within an fMRI environment. To validate our configuration, we present behavioral (pointing) and head movement data from 11 right-hemisphere lesioned patients and 17 older controls who underwent sham and real prism adaptation in an MRI scanner. Most participants could adapt to prismatic displacement with minimal head movements, and the procedure was well tolerated. We propose recommendations for fMRI studies of prism adaptation based on the design-specific constraints and our results.

Predicting Time-to-Relapse in Breast Cancer Using Neural Networks

DTIC Science & Technology

1997-12-01

CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 118. SECURITY CLASSIFICATION OF THIS PAGE Unclassified 19. SECURITY CLASSIFICATION OF...Lowell WE, and Davis GL. A neural network that predicts psychiatric length of stay. MD Computing 10:87-92, 1993. Ebell MH. Artificial neural netowrks

Verification and Validation of Neural Networks for Aerospace Systems

NASA Technical Reports Server (NTRS)

Mackall, Dale; Nelson, Stacy; Schumman, Johann; Clancy, Daniel (Technical Monitor)

2002-01-01

The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: 1) Overview of Adaptive Systems; and 2) V&V Processes/Methods.

Verification and Validation of Neural Networks for Aerospace Systems

NASA Technical Reports Server (NTRS)

Mackall, Dale; Nelson, Stacy; Schumann, Johann

2002-01-01

The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.

Neural network based adaptive output feedback control: Applications and improvements

NASA Astrophysics Data System (ADS)

Kutay, Ali Turker

Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in simulation, a fictitious actuator model is developed that fits experimentally observed characteristics of flow control actuators in static flight conditions as well as possible coupling effects between actuation, the dynamics of flow field, and the rigid body dynamics of the vehicle.

Adaptive coding of MSS imagery. [Multi Spectral band Scanners

NASA Technical Reports Server (NTRS)

Habibi, A.; Samulon, A. S.; Fultz, G. L.; Lumb, D.

1977-01-01

A number of adaptive data compression techniques are considered for reducing the bandwidth of multispectral data. They include adaptive transform coding, adaptive DPCM, adaptive cluster coding, and a hybrid method. The techniques are simulated and their performance in compressing the bandwidth of Landsat multispectral images is evaluated and compared using signal-to-noise ratio and classification consistency as fidelity criteria.

Multiscale analysis of neural spike trains.

PubMed

Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

2014-01-30

This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code. Copyright © 2013 John Wiley & Sons, Ltd.

Moving stimuli are less effectively masked using traditional continuous flash suppression (CFS) compared to a moving Mondrian mask (MMM): a test case for feature-selective suppression and retinotopic adaptation.

PubMed

Moors, Pieter; Wagemans, Johan; de-Wit, Lee

2014-01-01

Continuous flash suppression (CFS) is a powerful interocular suppression technique, which is often described as an effective means to reliably suppress stimuli from visual awareness. Suppression through CFS has been assumed to depend upon a reduction in (retinotopically specific) neural adaptation caused by the continual updating of the contents of the visual input to one eye. In this study, we started from the observation that suppressing a moving stimulus through CFS appeared to be more effective when using a mask that was actually more prone to retinotopically specific neural adaptation, but in which the properties of the mask were more similar to those of the to-be-suppressed stimulus. In two experiments, we find that using a moving Mondrian mask (i.e., one that includes motion) is more effective in suppressing a moving stimulus than a regular CFS mask. The observed pattern of results cannot be explained by a simple simulation that computes the degree of retinotopically specific neural adaptation over time, suggesting that this kind of neural adaptation does not play a large role in predicting the differences between conditions in this context. We also find some evidence consistent with the idea that the most effective CFS mask is the one that matches the properties (speed) of the suppressed stimulus. These results question the general importance of retinotopically specific neural adaptation in CFS, and potentially help to explain an implicit trend in the literature to adapt one's CFS mask to match one's to-be-suppressed stimuli. Finally, the results should help to guide the methodological development of future research where continuous suppression of moving stimuli is desired.

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

PubMed

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

Dynamic plasticity in coupled avian midbrain maps

NASA Astrophysics Data System (ADS)

Atwal, Gurinder Singh

2004-12-01

Internal mapping of the external environment is carried out using the receptive fields of topographic neurons in the brain, and in a normal barn owl the aural and visual subcortical maps are aligned from early experiences. However, instantaneous misalignment of the aural and visual stimuli has been observed to result in adaptive behavior, manifested by functional and anatomical changes of the auditory processing system. Using methods of information theory and statistical mechanics a model of the adaptive dynamics of the aural receptive field is presented and analyzed. The dynamics is determined by maximizing the mutual information between the neural output and the weighted sensory neural inputs, admixed with noise, subject to biophysical constraints. The reduced costs of neural rewiring, as in the case of young barn owls, reveal two qualitatively different types of receptive field adaptation depending on the magnitude of the audiovisual misalignment. By letting the misalignment increase with time, it is shown that the ability to adapt can be increased even when neural rewiring costs are high, in agreement with recent experimental reports of the increased plasticity of the auditory space map in adult barn owls due to incremental learning. Finally, a critical speed of misalignment is identified, demarcating the crossover from adaptive to nonadaptive behavior.

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.

An Energy Model of Place Cell Network in Three Dimensional Space.

PubMed

Wang, Yihong; Xu, Xuying; Wang, Rubin

2018-01-01

Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

A Subsonic Aircraft Design Optimization With Neural Network and Regression Approximators

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.; Haller, William J.

2004-01-01

The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated.

Coding stimulus amplitude by correlated neural activity

NASA Astrophysics Data System (ADS)

Metzen, Michael G.; Ávila-Åkerberg, Oscar; Chacron, Maurice J.

2015-04-01

While correlated activity is observed ubiquitously in the brain, its role in neural coding has remained controversial. Recent experimental results have demonstrated that correlated but not single-neuron activity can encode the detailed time course of the instantaneous amplitude (i.e., envelope) of a stimulus. These have furthermore demonstrated that such coding required and was optimal for a nonzero level of neural variability. However, a theoretical understanding of these results is still lacking. Here we provide a comprehensive theoretical framework explaining these experimental findings. Specifically, we use linear response theory to derive an expression relating the correlation coefficient to the instantaneous stimulus amplitude, which takes into account key single-neuron properties such as firing rate and variability as quantified by the coefficient of variation. The theoretical prediction was in excellent agreement with numerical simulations of various integrate-and-fire type neuron models for various parameter values. Further, we demonstrate a form of stochastic resonance as optimal coding of stimulus variance by correlated activity occurs for a nonzero value of noise intensity. Thus, our results provide a theoretical explanation of the phenomenon by which correlated but not single-neuron activity can code for stimulus amplitude and how key single-neuron properties such as firing rate and variability influence such coding. Correlation coding by correlated but not single-neuron activity is thus predicted to be a ubiquitous feature of sensory processing for neurons responding to weak input.

Authorship attribution of source code by using back propagation neural network based on particle swarm optimization

PubMed Central

Xu, Guoai; Li, Qi; Guo, Yanhui; Zhang, Miao

2017-01-01

Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead. PMID:29095934

The anatomy of the bifurcated neural spine and its occurrence within Tetrapoda.

PubMed

Woodruff, D Cary

2014-09-01

Vertebral neural spine bifurcation has been historically treated as largely restrictive to sauropodomorph dinosaurs; wherein it is inferred to be an adaptation in response to the increasing weight from the horizontally extended cervical column. Because no extant terrestrial vertebrates have massive, horizontally extended necks, extant forms with large cranial masses were examined for the presence of neural spine bifurcation. Here, I report for the first time on the soft tissue surrounding neural spine bifurcation in a terrestrial quadruped through the dissection of three Ankole-Watusi cattle. With horns weighing up to a combined 90 kg, the Ankole-Watusi is unlike any other breed of cattle in terms of cranial weight and presence of neural spine bifurcation. Using the Ankole-Watusi as a model, it appears that neural spine bifurcation plays a critical role in supporting a large mobile weight adjacent to the girdles. In addition to neural spine bifurcation being recognized within nonavian dinosaurs, this vertebral feature is also documented within many members of temnospondyls, captorhinids, seymouriamorphs, diadectomorphs, Aves, marsupials, artiodactyls, perissodactyls, and Primates, amongst others. This phylogenetic distribution indicates that spine bifurcation is more common than previously thought, and that this vertebral adaptation has contributed throughout the evolutionary history of tetrapods. Neural spine bifurcation should now be recognized as an anatomical component adapted by some vertebrates to deal with massive, horizontal, mobile weights adjacent the girdles. © 2014 Wiley Periodicals, Inc.

Neural Integration of Information Specifying Human Structure from Form, Motion, and Depth

PubMed Central

Jackson, Stuart; Blake, Randolph

2010-01-01

Recent computational models of biological motion perception operate on ambiguous two-dimensional representations of the body (e.g., snapshots, posture templates) and contain no explicit means for disambiguating the three-dimensional orientation of a perceived human figure. Are there neural mechanisms in the visual system that represent a moving human figure’s orientation in three dimensions? To isolate and characterize the neural mechanisms mediating perception of biological motion, we used an adaptation paradigm together with bistable point-light (PL) animations whose perceived direction of heading fluctuates over time. After exposure to a PL walker with a particular stereoscopically defined heading direction, observers experienced a consistent aftereffect: a bistable PL walker, which could be perceived in the adapted orientation or reversed in depth, was perceived predominantly reversed in depth. A phase-scrambled adaptor produced no aftereffect, yet when adapting and test walkers differed in size or appeared on opposite sides of fixation aftereffects did occur. Thus, this heading direction aftereffect cannot be explained by local, disparity-specific motion adaptation, and the properties of scale and position invariance imply higher-level origins of neural adaptation. Nor is disparity essential for producing adaptation: when suspended on top of a stereoscopically defined, rotating globe, a context-disambiguated “globetrotter” was sufficient to bias the bistable walker’s direction, as were full-body adaptors. In sum, these results imply that the neural signals supporting biomotion perception integrate information on the form, motion, and three-dimensional depth orientation of the moving human figure. Models of biomotion perception should incorporate mechanisms to disambiguate depth ambiguities in two-dimensional body representations. PMID:20089892

Neuromapping: Inflight Evaluation of Cognition and Adaptability

NASA Technical Reports Server (NTRS)

Kofman, I. S.; De Dios, Y. E.; Lawrence, K.; Schade, A.; Reschke, M. F.; Bloomberg, J. J.; Wood, S. J.; Mulavara, A. P.; Seidle, R. D.

2016-01-01

In consideration of the health and performance of crewmembers during flight and postflight, we are conducting a controlled prospective longitudinal study to investigate the effects of spaceflight on the extent, longevity and neural bases of sensorimotor, cognitive, and neural changes. Previous studies investigating sensorimotor adaptation to the microgravity environment longitudinally inflight have shown reduction in the ability to perform complex dual tasks. In this study we perform a series of tests investigating the longitudinal effects of adaptation to the microgravity environment and how it affects spatial cognition, manual visuo-motor adaption and dual tasking.

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.

NETS

NASA Technical Reports Server (NTRS)

Baffes, Paul T.

1993-01-01

NETS development tool provides environment for simulation and development of neural networks - computer programs that "learn" from experience. Written in ANSI standard C, program allows user to generate C code for implementation of neural network.

Adaptive Neural Networks for Automatic Negotiation

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

Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

The use of fuzzy logic and fuzzy neural networks has been found effective for the modelling of the uncertain relations between the parameters of a negotiation procedure. The problem with these configurations is that they are static, that is, any new knowledge from theory or experiment lead to the construction of entirely new models. To overcome this difficulty, we apply in this work, an adaptive neural topology to model the negotiation process. Finally a simple simulation is carried in order to test the new method.

The neural code for face orientation in the human fusiform face area.

PubMed

Ramírez, Fernando M; Cichy, Radoslaw M; Allefeld, Carsten; Haynes, John-Dylan

2014-09-03

Humans recognize faces and objects with high speed and accuracy regardless of their orientation. Recent studies have proposed that orientation invariance in face recognition involves an intermediate representation where neural responses are similar for mirror-symmetric views. Here, we used fMRI, multivariate pattern analysis, and computational modeling to investigate the neural encoding of faces and vehicles at different rotational angles. Corroborating previous studies, we demonstrate a representation of face orientation in the fusiform face-selective area (FFA). We go beyond these studies by showing that this representation is category-selective and tolerant to retinal translation. Critically, by controlling for low-level confounds, we found the representation of orientation in FFA to be compatible with a linear angle code. Aspects of mirror-symmetric coding cannot be ruled out when FFA mean activity levels are considered as a dimension of coding. Finally, we used a parametric family of computational models, involving a biased sampling of view-tuned neuronal clusters, to compare different face angle encoding models. The best fitting model exhibited a predominance of neuronal clusters tuned to frontal views of faces. In sum, our findings suggest a category-selective and monotonic code of face orientation in the human FFA, in line with primate electrophysiology studies that observed mirror-symmetric tuning of neural responses at higher stages of the visual system, beyond the putative homolog of human FFA. Copyright © 2014 the authors 0270-6474/14/3412155-13$15.00/0.

Hexacopter trajectory control using a neural network

NASA Astrophysics Data System (ADS)

Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.

2013-10-01

The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.

Verification and Validation of Adaptive and Intelligent Systems with Flight Test Results

NASA Technical Reports Server (NTRS)

Burken, John J.; Larson, Richard R.

2009-01-01

F-15 IFCS project goals are: a) Demonstrate Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions [A] & [B] failures. b) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs with a Pilot in the Loop. Gen II objectives include; a) Implement and Fly a Direct Adaptive Neural Network Based Flight Controller; b) Demonstrate the Ability of the System to Adapt to Simulated System Failures: 1) Suppress Transients Associated with Failure; 2) Re-Establish Sufficient Control and Handling of Vehicle for Safe Recovery. c) Provide Flight Experience for Development of Verification and Validation Processes for Flight Critical Neural Network Software.

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.

Recent Visual Experience Shapes Visual Processing in Rats through Stimulus-Specific Adaptation and Response Enhancement.

PubMed

Vinken, Kasper; Vogels, Rufin; Op de Beeck, Hans

2017-03-20

From an ecological point of view, it is generally suggested that the main goal of vision in rats and mice is navigation and (aerial) predator evasion [1-3]. The latter requires fast and accurate detection of a change in the visual environment. An outstanding question is whether there are mechanisms in the rodent visual system that would support and facilitate visual change detection. An experimental protocol frequently used to investigate change detection in humans is the oddball paradigm, in which a rare, unexpected stimulus is presented in a train of stimulus repetitions [4]. A popular "predictive coding" theory of cortical responses states that neural responses should decrease for expected sensory input and increase for unexpected input [5, 6]. Despite evidence for response suppression and enhancement in noninvasive scalp recordings in humans with this paradigm [7, 8], it has proven challenging to observe both phenomena in invasive action potential recordings in other animals [9-11]. During a visual oddball experiment, we recorded multi-unit spiking activity in rat primary visual cortex (V1) and latero-intermediate area (LI), which is a higher area of the rodent ventral visual stream. In rat V1, there was only evidence for response suppression related to stimulus-specific adaptation, and not for response enhancement. However, higher up in area LI, spiking activity showed clear surprise-based response enhancement in addition to stimulus-specific adaptation. These results show that neural responses along the rat ventral visual stream become increasingly sensitive to changes in the visual environment, suggesting a system specialized in the detection of unexpected events. Copyright © 2017 Elsevier Ltd. All rights reserved.

The basolateral amygdala in reward learning and addiction.

PubMed

Wassum, Kate M; Izquierdo, Alicia

2015-10-01

Sophisticated behavioral paradigms partnered with the emergence of increasingly selective techniques to target the basolateral amygdala (BLA) have resulted in an enhanced understanding of the role of this nucleus in learning and using reward information. Due to the wide variety of behavioral approaches many questions remain on the circumscribed role of BLA in appetitive behavior. In this review, we integrate conclusions of BLA function in reward-related behavior using traditional interference techniques (lesion, pharmacological inactivation) with those using newer methodological approaches in experimental animals that allow in vivo manipulation of cell type-specific populations and neural recordings. Secondly, from a review of appetitive behavioral tasks in rodents and monkeys and recent computational models of reward procurement, we derive evidence for BLA as a neural integrator of reward value, history, and cost parameters. Taken together, BLA codes specific and temporally dynamic outcome representations in a distributed network to orchestrate adaptive responses. We provide evidence that experiences with opiates and psychostimulants alter these outcome representations in BLA, resulting in long-term modified action. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automatic voice recognition using traditional and artificial neural network approaches

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1989-01-01

The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

Prototype-Incorporated Emotional Neural Network.

PubMed

Oyedotun, Oyebade K; Khashman, Adnan

2017-08-15

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Stimulus information contaminates summation tests of independent neural representations of features

NASA Technical Reports Server (NTRS)

Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.

2002-01-01

Many models of visual processing assume that visual information is analyzed into separable and independent neural codes, or features. A common psychophysical test of independent features is known as a summation study, which measures performance in a detection, discrimination, or visual search task as the number of proposed features increases. Improvement in human performance with increasing number of available features is typically attributed to the summation, or combination, of information across independent neural coding of the features. In many instances, however, increasing the number of available features also increases the stimulus information in the task, as assessed by an optimal observer that does not include the independent neural codes. In a visual search task with spatial frequency and orientation as the component features, a particular set of stimuli were chosen so that all searches had equivalent stimulus information, regardless of the number of features. In this case, human performance did not improve with increasing number of features, implying that the improvement observed with additional features may be due to stimulus information and not the combination across independent features.

Training adaptations in the behavior of human motor units.

PubMed

Duchateau, Jacques; Semmler, John G; Enoka, Roger M

2006-12-01

The purpose of this brief review is to examine the neural adaptations associated with training, by focusing on the behavior of single motor units. The review synthesizes current understanding on motor unit recruitment and rate coding during voluntary contractions, briefly describes the techniques used to record motor unit activity, and then evaluates the adaptations that have been observed in motor unit activity during maximal and submaximal contractions. Relatively few studies have directly compared motor unit behavior before and after training. Although some studies suggest that the voluntary activation of muscle can increase slightly with strength training, it is not known how the discharge of motor units changes to produce this increase in activation. The evidence indicates that the increase is not attributable to changes in motor unit synchronization. It has been demonstrated, however, that training can increase both the rate of torque development and the discharge rate of motor units. Furthermore, both strength training and practice of a force-matching task can evoke adaptations in the discharge characteristics of motor units. Because the variability in discharge rate has a significant influence on the fluctuations in force during submaximal contractions, the changes produced with training can influence motor performance during activities of daily living. Little is known, however, about the relative contributions of the descending drive, afferent feedback, spinal circuitry, and motor neuron properties to the observed adaptations in motor unit activity.

Individual differences in adaptive coding of face identity are linked to individual differences in face recognition ability.

PubMed

Rhodes, Gillian; Jeffery, Linda; Taylor, Libby; Hayward, William G; Ewing, Louise

2014-06-01

Despite their similarity as visual patterns, we can discriminate and recognize many thousands of faces. This expertise has been linked to 2 coding mechanisms: holistic integration of information across the face and adaptive coding of face identity using norms tuned by experience. Recently, individual differences in face recognition ability have been discovered and linked to differences in holistic coding. Here we show that they are also linked to individual differences in adaptive coding of face identity, measured using face identity aftereffects. Identity aftereffects correlated significantly with several measures of face-selective recognition ability. They also correlated marginally with own-race face recognition ability, suggesting a role for adaptive coding in the well-known other-race effect. More generally, these results highlight the important functional role of adaptive face-coding mechanisms in face expertise, taking us beyond the traditional focus on holistic coding mechanisms. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Predictions of Speech Chimaera Intelligibility Using Auditory Nerve Mean-Rate and Spike-Timing Neural Cues.

PubMed

Wirtzfeld, Michael R; Ibrahim, Rasha A; Bruce, Ian C

2017-10-01

Perceptual studies of speech intelligibility have shown that slow variations of acoustic envelope (ENV) in a small set of frequency bands provides adequate information for good perceptual performance in quiet, whereas acoustic temporal fine-structure (TFS) cues play a supporting role in background noise. However, the implications for neural coding are prone to misinterpretation because the mean-rate neural representation can contain recovered ENV cues from cochlear filtering of TFS. We investigated ENV recovery and spike-time TFS coding using objective measures of simulated mean-rate and spike-timing neural representations of chimaeric speech, in which either the ENV or the TFS is replaced by another signal. We (a) evaluated the levels of mean-rate and spike-timing neural information for two categories of chimaeric speech, one retaining ENV cues and the other TFS; (b) examined the level of recovered ENV from cochlear filtering of TFS speech; (c) examined and quantified the contribution to recovered ENV from spike-timing cues using a lateral inhibition network (LIN); and (d) constructed linear regression models with objective measures of mean-rate and spike-timing neural cues and subjective phoneme perception scores from normal-hearing listeners. The mean-rate neural cues from the original ENV and recovered ENV partially accounted for perceptual score variability, with additional variability explained by the recovered ENV from the LIN-processed TFS speech. The best model predictions of chimaeric speech intelligibility were found when both the mean-rate and spike-timing neural cues were included, providing further evidence that spike-time coding of TFS cues is important for intelligibility when the speech envelope is degraded.

The Feedback-related Negativity Codes Components of Abstract Inference during Reward-based Decision-making.

PubMed

Reiter, Andrea M F; Koch, Stefan P; Schröger, Erich; Hinrichs, Hermann; Heinze, Hans-Jochen; Deserno, Lorenz; Schlagenhauf, Florian

2016-08-01

Behavioral control is influenced not only by learning from the choices made and the rewards obtained but also by "what might have happened," that is, inference about unchosen options and their fictive outcomes. Substantial progress has been made in understanding the neural signatures of direct learning from choices that are actually made and their associated rewards via reward prediction errors (RPEs). However, electrophysiological correlates of abstract inference in decision-making are less clear. One seminal theory suggests that the so-called feedback-related negativity (FRN), an ERP peaking 200-300 msec after a feedback stimulus at frontocentral sites of the scalp, codes RPEs. Hitherto, the FRN has been predominantly related to a so-called "model-free" RPE: The difference between the observed outcome and what had been expected. Here, by means of computational modeling of choice behavior, we show that individuals employ abstract, "double-update" inference on the task structure by concurrently tracking values of chosen stimuli (associated with observed outcomes) and unchosen stimuli (linked to fictive outcomes). In a parametric analysis, model-free RPEs as well as their modification because of abstract inference were regressed against single-trial FRN amplitudes. We demonstrate that components related to abstract inference uniquely explain variance in the FRN beyond model-free RPEs. These findings advance our understanding of the FRN and its role in behavioral adaptation. This might further the investigation of disturbed abstract inference, as proposed, for example, for psychiatric disorders, and its underlying neural correlates.

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

NASA Astrophysics Data System (ADS)

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

2017-04-01

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton-Jacobi-Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

NASA Astrophysics Data System (ADS)

Lin, Tsung-Chih

2010-12-01

In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.

The Generalization of Visuomotor Learning to Untrained Movements and Movement Sequences Based on Movement Vector and Goal Location Remapping

PubMed Central

Wu, Howard G.

2013-01-01

The planning of goal-directed movements is highly adaptable; however, the basic mechanisms underlying this adaptability are not well understood. Even the features of movement that drive adaptation are hotly debated, with some studies suggesting remapping of goal locations and others suggesting remapping of the movement vectors leading to goal locations. However, several previous motor learning studies and the multiplicity of the neural coding underlying visually guided reaching movements stand in contrast to this either/or debate on the modes of motor planning and adaptation. Here we hypothesize that, during visuomotor learning, the target location and movement vector of trained movements are separately remapped, and we propose a novel computational model for how motor plans based on these remappings are combined during the control of visually guided reaching in humans. To test this hypothesis, we designed a set of experimental manipulations that effectively dissociated the effects of remapping goal location and movement vector by examining the transfer of visuomotor adaptation to untrained movements and movement sequences throughout the workspace. The results reveal that (1) motor adaptation differentially remaps goal locations and movement vectors, and (2) separate motor plans based on these features are effectively averaged during motor execution. We then show that, without any free parameters, the computational model we developed for combining movement-vector-based and goal-location-based planning predicts nearly 90% of the variance in novel movement sequences, even when multiple attributes are simultaneously adapted, demonstrating for the first time the ability to predict how motor adaptation affects movement sequence planning. PMID:23804099

Image Coding Based on Address Vector Quantization.

NASA Astrophysics Data System (ADS)

Feng, Yushu

Image coding is finding increased application in teleconferencing, archiving, and remote sensing. This thesis investigates the potential of Vector Quantization (VQ), a relatively new source coding technique, for compression of monochromatic and color images. Extensions of the Vector Quantization technique to the Address Vector Quantization method have been investigated. In Vector Quantization, the image data to be encoded are first processed to yield a set of vectors. A codeword from the codebook which best matches the input image vector is then selected. Compression is achieved by replacing the image vector with the index of the code-word which produced the best match, the index is sent to the channel. Reconstruction of the image is done by using a table lookup technique, where the label is simply used as an address for a table containing the representative vectors. A code-book of representative vectors (codewords) is generated using an iterative clustering algorithm such as K-means, or the generalized Lloyd algorithm. A review of different Vector Quantization techniques are given in chapter 1. Chapter 2 gives an overview of codebook design methods including the Kohonen neural network to design codebook. During the encoding process, the correlation of the address is considered and Address Vector Quantization is developed for color image and monochrome image coding. Address VQ which includes static and dynamic processes is introduced in chapter 3. In order to overcome the problems in Hierarchical VQ, Multi-layer Address Vector Quantization is proposed in chapter 4. This approach gives the same performance as that of the normal VQ scheme but the bit rate is about 1/2 to 1/3 as that of the normal VQ method. In chapter 5, a Dynamic Finite State VQ based on a probability transition matrix to select the best subcodebook to encode the image is developed. In chapter 6, a new adaptive vector quantization scheme, suitable for color video coding, called "A Self -Organizing Adaptive VQ Technique" is presented. In addition to chapters 2 through 6 which report on new work, this dissertation includes one chapter (chapter 1) and part of chapter 2 which review previous work on VQ and image coding, respectively. Finally, a short discussion of directions for further research is presented in conclusion.

Slot-like capacity and resource-like coding in a neural model of multiple-item working memory.

PubMed

Standage, Dominic; Pare, Martin

2018-06-27

For the past decade, research on the storage limitations of working memory has been dominated by two fundamentally different hypotheses. On the one hand, the contents of working memory may be stored in a limited number of `slots', each with a fixed resolution. On the other hand, any number of items may be stored, but with decreasing resolution. These two hypotheses have been invaluable in characterizing the computational structure of working memory, but neither provides a complete account of the available experimental data, nor speaks to the neural basis of the limitations it characterizes. To address these shortcomings, we simulated a multiple-item working memory task with a cortical network model, the cellular resolution of which allowed us to quantify the coding fidelity of memoranda as a function of memory load, as measured by the discriminability, regularity and reliability of simulated neural spiking. Our simulations account for a wealth of neural and behavioural data from human and non-human primate studies, and they demonstrate that feedback inhibition lowers both capacity and coding fidelity. Because the strength of inhibition scales with the number of items stored by the network, increasing this number progressively lowers fidelity until capacity is reached. Crucially, the model makes specific, testable predictions for neural activity on multiple-item working memory tasks.

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

PubMed Central

Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-01-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. PMID:27304526

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

PubMed

Huang, Chao; Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-06-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

The neural oscillations of conflict adaptation in the human frontal region.

PubMed

Tang, Dandan; Hu, Li; Chen, Antao

2013-07-01

Incongruency between print color and the semantic meaning of a word in a classical Stroop task activates the human conflict monitoring system and triggers a behavioral conflict. Conflict adaptation has been suggested to mediate the cortical processing of neural oscillations in such a conflict situation. However, the basic mechanisms that underlie the influence of conflict adaptation on the changes of neural oscillations are not clear. In the present study, electroencephalography (EEG) data were recorded from sixteen healthy human participants while they were performing a color-word Stroop task within a novel look-to-do transition design that included two response modalities. In the 'look' condition, participants were informed to look at the color of presented words but no responses were required; in the 'do' condition, they were informed to make arranged responses to the color of presented words. Behaviorally, a reliable conflict adaptation was observed. Time-frequency analysis revealed that (1) in the 'look' condition, theta-band activity in the left- and right-frontal regions reflected a conflict-related process at a response inhibition level; and (2) in the 'do' condition, both theta-band activity in the left-frontal region and alpha-band activity in the left-, right-, and centro-frontal regions reflected a process of conflict control, which triggered neural and behavioral adaptation. Taken together, these results suggest that there are frontal mechanisms involving neural oscillations that can mediate response inhibition processes and control behavioral conflict. Copyright © 2013 Elsevier B.V. All rights reserved.

Diminished Neural Adaptation during Implicit Learning in Autism

PubMed Central

Schipul, Sarah E.; Just, Marcel Adam

2015-01-01

Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions. Sixteen high-functioning adults with ASD and sixteen neurotypical control participants were trained on and performed an implicit dot pattern prototype learning task in a functional magnetic resonance imaging (fMRI) session. During the preliminary exposure to the type of implicit prototype learning task later to be used in the scanner, the ASD participants took longer than the neurotypical group to learn the task, demonstrating altered implicit learning in ASD. After equating task structure learning, the two groups’ brain activation differed during their learning of a new prototype in the subsequent scanning session. The main findings indicated that neural adaptations in a distributed task network were reduced in the ASD group, relative to the neurotypical group, and were related to ASD symptom severity. Functional connectivity was reduced and did not change as much during learning for the ASD group, and was related to ASD symptom severity. These findings suggest that individuals with ASD show altered neural adaptations during learning, as seen in both activation and functional connectivity measures. This finding suggests why many real-world implicit learning situations may pose special challenges for ASD. PMID:26484826

Neural coding of basic reward terms of animal learning theory, game theory, microeconomics and behavioural ecology.

PubMed

Schultz, Wolfram

2004-04-01

Neurons in a small number of brain structures detect rewards and reward-predicting stimuli and are active during the expectation of predictable food and liquid rewards. These neurons code the reward information according to basic terms of various behavioural theories that seek to explain reward-directed learning, approach behaviour and decision-making. The involved brain structures include groups of dopamine neurons, the striatum including the nucleus accumbens, the orbitofrontal cortex and the amygdala. The reward information is fed to brain structures involved in decision-making and organisation of behaviour, such as the dorsolateral prefrontal cortex and possibly the parietal cortex. The neural coding of basic reward terms derived from formal theories puts the neurophysiological investigation of reward mechanisms on firm conceptual grounds and provides neural correlates for the function of rewards in learning, approach behaviour and decision-making.

The iso-response method: measuring neuronal stimulus integration with closed-loop experiments

PubMed Central

Gollisch, Tim; Herz, Andreas V. M.

2012-01-01

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments. PMID:23267315

Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

DTIC Science & Technology

2010-09-24

system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems.

PubMed

Zhao, Xudong; Wang, Xinyong; Zong, Guangdeng; Zheng, Xiaolong

2017-10-01

This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.

Autistic traits are linked to reduced adaptive coding of face identity and selectively poorer face recognition in men but not women.

PubMed

Rhodes, Gillian; Jeffery, Linda; Taylor, Libby; Ewing, Louise

2013-11-01

Our ability to discriminate and recognize thousands of faces despite their similarity as visual patterns relies on adaptive, norm-based, coding mechanisms that are continuously updated by experience. Reduced adaptive coding of face identity has been proposed as a neurocognitive endophenotype for autism, because it is found in autism and in relatives of individuals with autism. Autistic traits can also extend continuously into the general population, raising the possibility that reduced adaptive coding of face identity may be more generally associated with autistic traits. In the present study, we investigated whether adaptive coding of face identity decreases as autistic traits increase in an undergraduate population. Adaptive coding was measured using face identity aftereffects, and autistic traits were measured using the Autism-Spectrum Quotient (AQ) and its subscales. We also measured face and car recognition ability to determine whether autistic traits are selectively related to face recognition difficulties. We found that men who scored higher on levels of autistic traits related to social interaction had reduced adaptive coding of face identity. This result is consistent with the idea that atypical adaptive face-coding mechanisms are an endophenotype for autism. Autistic traits were also linked with face-selective recognition difficulties in men. However, there were some unexpected sex differences. In women, autistic traits were linked positively, rather than negatively, with adaptive coding of identity, and were unrelated to face-selective recognition difficulties. These sex differences indicate that autistic traits can have different neurocognitive correlates in men and women and raise the intriguing possibility that endophenotypes of autism can differ in males and females. © 2013 Elsevier Ltd. All rights reserved.

Novel adaptive neural control design for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi

2016-03-01

This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.

Coarse-coded higher-order neural networks for PSRI object recognition. [position, scale, and rotation invariant

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1993-01-01

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.

Sparse bursts optimize information transmission in a multiplexed neural code.

PubMed

Naud, Richard; Sprekeler, Henning

2018-06-22

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets. Copyright © 2018 the Author(s). Published by PNAS.

Spatiotemporal properties of microsaccades: Model predictions and experimental tests

NASA Astrophysics Data System (ADS)

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

2016-10-01

Microsaccades are involuntary and very small eye movements during fixation. Recently, the microsaccade-related neural dynamics have been extensively investigated both in experiments and by constructing neural network models. Experimentally, microsaccades also exhibit many behavioral properties. It’s well known that the behavior properties imply the underlying neural dynamical mechanisms, and so are determined by neural dynamics. The behavioral properties resulted from neural responses to microsaccades, however, are not yet understood and are rarely studied theoretically. Linking neural dynamics to behavior is one of the central goals of neuroscience. In this paper, we provide behavior predictions on spatiotemporal properties of microsaccades according to microsaccade-induced neural dynamics in a cascading network model, which includes both retinal adaptation and short-term depression (STD) at thalamocortical synapses. We also successfully give experimental tests in the statistical sense. Our results provide the first behavior description of microsaccades based on neural dynamics induced by behaving activity, and so firstly link neural dynamics to behavior of microsaccades. These results indicate strongly that the cascading adaptations play an important role in the study of microsaccades. Our work may be useful for further investigations of the microsaccadic behavioral properties and of the underlying neural dynamical mechanisms responsible for the behavioral properties.

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

NASA Astrophysics Data System (ADS)

Masri Husam Fayiz, Al

2017-01-01

The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots

PubMed Central

Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment. PMID:24523694

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

NASA Astrophysics Data System (ADS)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

Unipolar Terminal-Attractor Based Neural Associative Memory with Adaptive Threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1996-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner-product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Unipolar terminal-attractor based neural associative memory with adaptive threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1993-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Control Reallocation Strategies for Damage Adaptation in Transport Class Aircraft

NASA Technical Reports Server (NTRS)

Gundy-Burlet, Karen; Krishnakumar, K.; Limes, Greg; Bryant, Don

2003-01-01

This paper examines the feasibility, potential benefits and implementation issues associated with retrofitting a neural-adaptive flight control system (NFCS) to existing transport aircraft, including both cable/hydraulic and fly-by-wire configurations. NFCS uses a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and to make the controller easy to apply when implemented on different aircraft. Full-motion piloted simulation studies were performed on two different transport models: the Boeing 747-400 and the Boeing C-17. Subjects included NASA, Air Force and commercial airline pilots. Results demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.

Neural coding of high-frequency tones

NASA Technical Reports Server (NTRS)

Howes, W. L.

1976-01-01

Available evidence was presented indicating that neural discharges in the auditory nerve display characteristic periodicities in response to any tonal stimulus including high-frequency stimuli, and that this periodicity corresponds to the subjective pitch.

Navigating the Neural Space in Search of the Neural Code.

PubMed

Jazayeri, Mehrdad; Afraz, Arash

2017-03-08

The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

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

Susmikanti, Mike, E-mail: mike@batan.go.id; Sulistyo, Jos, E-mail: soj@batan.go.id

2014-09-30

Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to developmore » code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.« less

Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

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

Disney, Adam; Reynolds, John

2015-01-01

Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

Critical Neural Substrates for Correcting Unexpected Trajectory Errors and Learning from Them

ERIC Educational Resources Information Center

Mutha, Pratik K.; Sainburg, Robert L.; Haaland, Kathleen Y.

2011-01-01

Our proficiency at any skill is critically dependent on the ability to monitor our performance, correct errors and adapt subsequent movements so that errors are avoided in the future. In this study, we aimed to dissociate the neural substrates critical for correcting unexpected trajectory errors and learning to adapt future movements based on…

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

PubMed

Poirazi, Panayiota; Neocleous, Costas; Pattichis, Costantinos S; Schizas, Christos N

2004-05-01

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

The Impact of Ecological Niche on Adaptive Flexibility of Sensory Circuitry

PubMed Central

Pallas, Sarah L.

2017-01-01

Evolution and development are interdependent, particularly with regard to the construction of the nervous system and its position as the machine that produces behavior. On the one hand, the processes directing development and plasticity of the brain provide avenues through which natural selection can sculpt neural cell fate and connectivity, and on the other hand, they are themselves subject to selection pressure. For example, mutations that produce heritable perturbations in neuronal birth and death rates, transcription factor expression, or availability of axon guidance factors within sensory pathways can markedly affect the development of form and thus the function of stimulus decoding circuitry. This evolvability of flexible circuits makes them more adaptable to environmental variation. Although there is general agreement on this point, whether the sensitivity of circuits to environmental influence and the mechanisms underlying development and plasticity of sensory pathways are similar across species from different ecological niches has received almost no attention. Neural circuits are generally more sensitive to environmental influences during an early critical period, but not all niches afford the same access to stimuli in early life. Furthermore, depending on predictability of the habitat and ecological niche, sensory coding circuits might be more susceptible to sensory experience in some species than in others. Despite decades of work on understanding the mechanisms underlying critical period plasticity, the importance of ecological niche in visual pathway development has received little attention. Here, I will explore the relationship between critical period plasticity and ecological niche in mammalian sensory pathways. PMID:28701910

The Impact of Ecological Niche on Adaptive Flexibility of Sensory Circuitry.

PubMed

Pallas, Sarah L

2017-01-01

Evolution and development are interdependent, particularly with regard to the construction of the nervous system and its position as the machine that produces behavior. On the one hand, the processes directing development and plasticity of the brain provide avenues through which natural selection can sculpt neural cell fate and connectivity, and on the other hand, they are themselves subject to selection pressure. For example, mutations that produce heritable perturbations in neuronal birth and death rates, transcription factor expression, or availability of axon guidance factors within sensory pathways can markedly affect the development of form and thus the function of stimulus decoding circuitry. This evolvability of flexible circuits makes them more adaptable to environmental variation. Although there is general agreement on this point, whether the sensitivity of circuits to environmental influence and the mechanisms underlying development and plasticity of sensory pathways are similar across species from different ecological niches has received almost no attention. Neural circuits are generally more sensitive to environmental influences during an early critical period, but not all niches afford the same access to stimuli in early life. Furthermore, depending on predictability of the habitat and ecological niche, sensory coding circuits might be more susceptible to sensory experience in some species than in others. Despite decades of work on understanding the mechanisms underlying critical period plasticity, the importance of ecological niche in visual pathway development has received little attention. Here, I will explore the relationship between critical period plasticity and ecological niche in mammalian sensory pathways.

Aging Affects Adaptation to Sound-Level Statistics in Human Auditory Cortex.

PubMed

Herrmann, Björn; Maess, Burkhard; Johnsrude, Ingrid S

2018-02-21

Optimal perception requires efficient and adaptive neural processing of sensory input. Neurons in nonhuman mammals adapt to the statistical properties of acoustic feature distributions such that they become sensitive to sounds that are most likely to occur in the environment. However, whether human auditory responses adapt to stimulus statistical distributions and how aging affects adaptation to stimulus statistics is unknown. We used MEG to study how exposure to different distributions of sound levels affects adaptation in auditory cortex of younger (mean: 25 years; n = 19) and older (mean: 64 years; n = 20) adults (male and female). Participants passively listened to two sound-level distributions with different modes (either 15 or 45 dB sensation level). In a control block with long interstimulus intervals, allowing neural populations to recover from adaptation, neural response magnitudes were similar between younger and older adults. Critically, both age groups demonstrated adaptation to sound-level stimulus statistics, but adaptation was altered for older compared with younger people: in the older group, neural responses continued to be sensitive to sound level under conditions in which responses were fully adapted in the younger group. The lack of full adaptation to the statistics of the sensory environment may be a physiological mechanism underlying the known difficulty that older adults have with filtering out irrelevant sensory information. SIGNIFICANCE STATEMENT Behavior requires efficient processing of acoustic stimulation. Animal work suggests that neurons accomplish efficient processing by adjusting their response sensitivity depending on statistical properties of the acoustic environment. Little is known about the extent to which this adaptation to stimulus statistics generalizes to humans, particularly to older humans. We used MEG to investigate how aging influences adaptation to sound-level statistics. Listeners were presented with sounds drawn from sound-level distributions with different modes (15 vs 45 dB). Auditory cortex neurons adapted to sound-level statistics in younger and older adults, but adaptation was incomplete in older people. The data suggest that the aging auditory system does not fully capitalize on the statistics available in sound environments to tune the perceptual system dynamically. Copyright © 2018 the authors 0270-6474/18/381989-11$15.00/0.

Accuracy comparison among different machine learning techniques for detecting malicious codes

NASA Astrophysics Data System (ADS)

Narang, Komal

2016-03-01

In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

Neural controller for adaptive movements with unforeseen payloads.

PubMed

Kuperstein, M; Wang, J

1990-01-01

A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3% of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

Cognitive and Neural Sciences Division 1991 Programs

DTIC Science & Technology

1991-08-01

FUNDING NUMBERS Cognitive and Neural Sciences Division 1991 Programs PE 61153N 6. AUTHOR(S) Edited by Willard S. Vaughan 7. PERFORMING ORGANIZATION...NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER Office of Naval Research 0CNR !1491-19 Cognitive and Neural Sciences Division Code 1142...NOTES iN This is a compilation of abstracts representing R&D sponsored by the ONR Cognitive and Neural Sciences Division. 12a. DISTRIBUTION

An Application Development Platform for Neuromorphic Computing

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

Dean, Mark; Chan, Jason; Daffron, Christopher

2016-01-01

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model arti cial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware and software implementations of DANNA, including their features, functionalities and performance. We then describe the development of an Application Development Platform (ADP) to support efficient application implementation and testing of DANNA based solutions. We conclude with future directions.

Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

NASA Technical Reports Server (NTRS)

Grantham, Katie

2003-01-01

Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

Feedback Synthesizes Neural Codes for Motion.

PubMed

Clarke, Stephen E; Maler, Leonard

2017-05-08

In senses as diverse as vision, hearing, touch, and the electrosense, sensory neurons receive bottom-up input from the environment, as well as top-down input from feedback loops involving higher brain regions [1-4]. Through connectivity with local inhibitory interneurons, these feedback loops can exert both positive and negative control over fundamental aspects of neural coding, including bursting [5, 6] and synchronous population activity [7, 8]. Here we show that a prominent midbrain feedback loop synthesizes a neural code for motion reversal in the hindbrain electrosensory ON- and OFF-type pyramidal cells. This top-down mechanism generates an accurate bidirectional encoding of object position, despite the inability of the electrosensory afferents to generate a consistent bottom-up representation [9, 10]. The net positive activity of this midbrain feedback is additionally regulated through a hindbrain feedback loop, which reduces stimulus-induced bursting and also dampens the ON and OFF cell responses to interfering sensory input [11]. We demonstrate that synthesis of motion representations and cancellation of distracting signals are mediated simultaneously by feedback, satisfying an accepted definition of spatial attention [12]. The balance of excitatory and inhibitory feedback establishes a "focal" distance for optimized neural coding, whose connection to a classic motion-tracking behavior provides new insight into the computational roles of feedback and active dendrites in spatial localization [13, 14]. Copyright © 2017 Elsevier Ltd. All rights reserved.

Coding of sounds in the auditory system and its relevance to signal processing and coding in cochlear implants.

PubMed

Moore, Brian C J

2003-03-01

To review how the properties of sounds are "coded" in the normal auditory system and to discuss the extent to which cochlear implants can and do represent these codes. Data are taken from published studies of the response of the cochlea and auditory nerve to simple and complex stimuli, in both the normal and the electrically stimulated ear. REVIEW CONTENT: The review describes: 1) the coding in the normal auditory system of overall level (which partly determines perceived loudness), spectral shape (which partly determines perceived timbre and the identity of speech sounds), periodicity (which partly determines pitch), and sound location; 2) the role of the active mechanism in the cochlea, and particularly the fast-acting compression associated with that mechanism; 3) the neural response patterns evoked by cochlear implants; and 4) how the response patterns evoked by implants differ from those observed in the normal auditory system in response to sound. A series of specific issues is then discussed, including: 1) how to compensate for the loss of cochlear compression; 2) the effective number of independent channels in a normal ear and in cochlear implantees; 3) the importance of independence of responses across neurons; 4) the stochastic nature of normal neural responses; 5) the possible role of across-channel coincidence detection; and 6) potential benefits of binaural implantation. Current cochlear implants do not adequately reproduce several aspects of the neural coding of sound in the normal auditory system. Improved electrode arrays and coding systems may lead to improved coding and, it is hoped, to better performance.

Different Timescales for the Neural Coding of Consonant and Vowel Sounds

PubMed Central

Perez, Claudia A.; Engineer, Crystal T.; Jakkamsetti, Vikram; Carraway, Ryan S.; Perry, Matthew S.

2013-01-01

Psychophysical, clinical, and imaging evidence suggests that consonant and vowel sounds have distinct neural representations. This study tests the hypothesis that consonant and vowel sounds are represented on different timescales within the same population of neurons by comparing behavioral discrimination with neural discrimination based on activity recorded in rat inferior colliculus and primary auditory cortex. Performance on 9 vowel discrimination tasks was highly correlated with neural discrimination based on spike count and was not correlated when spike timing was preserved. In contrast, performance on 11 consonant discrimination tasks was highly correlated with neural discrimination when spike timing was preserved and not when spike timing was eliminated. These results suggest that in the early stages of auditory processing, spike count encodes vowel sounds and spike timing encodes consonant sounds. These distinct coding strategies likely contribute to the robust nature of speech sound representations and may help explain some aspects of developmental and acquired speech processing disorders. PMID:22426334

Absence of Sex-Contingent Gaze Direction Aftereffects Suggests a Limit to Contingencies in Face Aftereffects

PubMed Central

Kloth, Nadine; Rhodes, Gillian; Schweinberger, Stefan R.

2015-01-01

Face aftereffects (e.g., expression aftereffects) can be simultaneously induced in opposite directions for different face categories (e.g., male and female faces). Such aftereffects are typically interpreted as indicating that distinct neural populations code the categories on which adaptation is contingent, e.g., male and female faces. Moreover, they suggest that these distinct populations selectively respond to variations in the secondary stimulus dimension, e.g., emotional expression. However, contingent aftereffects have now been reported for so many different combinations of face characteristics, that one might question this interpretation. Instead, the selectivity might be generated during the adaptation procedure, for instance as a result of associative learning, and not indicate pre-existing response selectivity in the face perception system. To alleviate this concern, one would need to demonstrate some limit to contingent aftereffects. Here, we report a clear limit, showing that gaze direction aftereffects are not contingent on face sex. We tested 36 young Caucasian adults in a gaze adaptation paradigm. We initially established their ability to discriminate the gaze direction of male and female test faces in a pre-adaptation phase. Afterwards, half of the participants adapted to female faces looking left and male faces looking right, and half adapted to the reverse pairing. We established the effects of this adaptation on the perception of gaze direction in subsequently presented male and female test faces. We found that adaptation induced pronounced gaze direction aftereffects, i.e., participants were biased to perceive small gaze deviations to both the left and right as direct. Importantly, however, aftereffects were identical for male and female test faces, showing that the contingency of face sex and gaze direction participants experienced during the adaptation procedure had no effect. PMID:26648890

Adaptation in sound localization processing induced by interaural time difference in amplitude envelope at high frequencies.

PubMed

Kawashima, Takayuki; Sato, Takao

2012-01-01

When a second sound follows a long first sound, its location appears to be perceived away from the first one (the localization/lateralization aftereffect). This aftereffect has often been considered to reflect an efficient neural coding of sound locations in the auditory system. To understand determinants of the localization aftereffect, the current study examined whether it is induced by an interaural temporal difference (ITD) in the amplitude envelope of high frequency transposed tones (over 2 kHz), which is known to function as a sound localization cue. In Experiment 1, participants were required to adjust the position of a pointer to the perceived location of test stimuli before and after adaptation. Test and adapter stimuli were amplitude modulated (AM) sounds presented at high frequencies and their positional differences were manipulated solely by the envelope ITD. Results showed that the adapter's ITD systematically affected the perceived position of test sounds to the directions expected from the localization/lateralization aftereffect when the adapter was presented at ±600 µs ITD; a corresponding significant effect was not observed for a 0 µs ITD adapter. In Experiment 2, the observed adapter effect was confirmed using a forced-choice task. It was also found that adaptation to the AM sounds at high frequencies did not significantly change the perceived position of pure-tone test stimuli in the low frequency region (128 and 256 Hz). The findings in the current study indicate that ITD in the envelope at high frequencies induces the localization aftereffect. This suggests that ITD in the high frequency region is involved in adaptive plasticity of auditory localization processing.

Structures of Neural Correlation and How They Favor Coding

PubMed Central

Franke, Felix; Fiscella, Michele; Sevelev, Maksim; Roska, Botond; Hierlemann, Andreas; da Silveira, Rava Azeredo

2017-01-01

Summary The neural representation of information suffers from “noise”—the trial-to-trial variability in the response of neurons. The impact of correlated noise upon population coding has been debated, but a direct connection between theory and experiment remains tenuous. Here, we substantiate this connection and propose a refined theoretical picture. Using simultaneous recordings from a population of direction-selective retinal ganglion cells, we demonstrate that coding benefits from noise correlations. The effect is appreciable already in small populations, yet it is a collective phenomenon. Furthermore, the stimulus-dependent structure of correlation is key. We develop simple functional models that capture the stimulus-dependent statistics. We then use them to quantify the performance of population coding, which depends upon interplays of feature sensitivities and noise correlations in the population. Because favorable structures of correlation emerge robustly in circuits with noisy, nonlinear elements, they will arise and benefit coding beyond the confines of retina. PMID:26796692

Oscillatory phase dynamics in neural entrainment underpin illusory percepts of time.

PubMed

Herrmann, Björn; Henry, Molly J; Grigutsch, Maren; Obleser, Jonas

2013-10-02

Neural oscillatory dynamics are a candidate mechanism to steer perception of time and temporal rate change. While oscillator models of time perception are strongly supported by behavioral evidence, a direct link to neural oscillations and oscillatory entrainment has not yet been provided. In addition, it has thus far remained unaddressed how context-induced illusory percepts of time are coded for in oscillator models of time perception. To investigate these questions, we used magnetoencephalography and examined the neural oscillatory dynamics that underpin pitch-induced illusory percepts of temporal rate change. Human participants listened to frequency-modulated sounds that varied over time in both modulation rate and pitch, and judged the direction of rate change (decrease vs increase). Our results demonstrate distinct neural mechanisms of rate perception: Modulation rate changes directly affected listeners' rate percept as well as the exact frequency of the neural oscillation. However, pitch-induced illusory rate changes were unrelated to the exact frequency of the neural responses. The rate change illusion was instead linked to changes in neural phase patterns, which allowed for single-trial decoding of percepts. That is, illusory underestimations or overestimations of perceived rate change were tightly coupled to increased intertrial phase coherence and changes in cerebro-acoustic phase lag. The results provide insight on how illusory percepts of time are coded for by neural oscillatory dynamics.

FPGA-based rate-adaptive LDPC-coded modulation for the next generation of optical communication systems.

PubMed

Zou, Ding; Djordjevic, Ivan B

2016-09-05

In this paper, we propose a rate-adaptive FEC scheme based on LDPC codes together with its software reconfigurable unified FPGA architecture. By FPGA emulation, we demonstrate that the proposed class of rate-adaptive LDPC codes based on shortening with an overhead from 25% to 42.9% provides a coding gain ranging from 13.08 dB to 14.28 dB at a post-FEC BER of 10^-15 for BPSK transmission. In addition, the proposed rate-adaptive LDPC coding combined with higher-order modulations have been demonstrated including QPSK, 8-QAM, 16-QAM, 32-QAM, and 64-QAM, which covers a wide range of signal-to-noise ratios. Furthermore, we apply the unequal error protection by employing different LDPC codes on different bits in 16-QAM and 64-QAM, which results in additional 0.5dB gain compared to conventional LDPC coded modulation with the same code rate of corresponding LDPC code.

Design of neurophysiologically motivated structures of time-pulse coded neurons

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Lobodzinska, Raisa F.

2009-04-01

The common methodology of biologically motivated concept of building of processing sensors systems with parallel input and picture operands processing and time-pulse coding are described in paper. Advantages of such coding for creation of parallel programmed 2D-array structures for the next generation digital computers which require untraditional numerical systems for processing of analog, digital, hybrid and neuro-fuzzy operands are shown. The optoelectronic time-pulse coded intelligent neural elements (OETPCINE) simulation results and implementation results of a wide set of neuro-fuzzy logic operations are considered. The simulation results confirm engineering advantages, intellectuality, circuit flexibility of OETPCINE for creation of advanced 2D-structures. The developed equivalentor-nonequivalentor neural element has power consumption of 10mW and processing time about 10...100us.

Preface.

PubMed

Ditlevsen, Susanne; Lansky, Petr

2016-06-01

This Special Issue of Mathematical Biosciences and Engineering contains 11 selected papers presented at the Neural Coding 2014 workshop. The workshop was held in the royal city of Versailles in France, October 6-10, 2014. This was the 11th of a series of international workshops on this subject, the first held in Prague (1995), then Versailles (1997), Osaka (1999), Plymouth (2001), Aulla (2003), Marburg (2005), Montevideo (2007), Tainan (2009), Limassol (2010), and again in Prague (2012). Also selected papers from Prague were published as a special issue of Mathematical Biosciences and Engineering and in this way a tradition was started. Similarly to the previous workshops, this was a single track multidisciplinary event bringing together experimental and computational neuroscientists. The Neural Coding Workshops are traditionally biennial symposia. They are relatively small in size, interdisciplinary with major emphasis on the search for common principles in neural coding. The workshop was conceived to bring together scientists from different disciplines for an in-depth discussion of mathematical model-building and computational strategies. Further information on the meeting can be found at the NC2014 website at https://colloque6.inra.fr/neural_coding_2014. The meeting was supported by French National Institute for Agricultural Research, the world's leading institution in this field. This Special Issue of Mathematical Biosciences and Engineering contains 11 selected papers presented at the Neural Coding 2014 workshop. The workshop was held in the royal city of Versailles in France, October 6-10, 2014. This was the 11th of a series of international workshops on this subject, the first held in Prague (1995), then Versailles (1997), Osaka (1999), Plymouth (2001), Aulla (2003), Marburg (2005), Montevideo (2007), Tainan (2009), Limassol (2010), and again in Prague (2012). Also selected papers from Prague were published as a special issue of Mathematical Biosciences and Engineering and in this way a tradition was started. Similarly to the previous workshops, this was a single track multidisciplinary event bringing together experimental and computational neuroscientists. The Neural Coding Workshops are traditionally biennial symposia. They are relatively small in size, interdisciplinary with major emphasis on the search for common principles in neural coding. The workshop was conceived to bring together scientists from different disciplines for an in-depth discussion of mathematical model-building and computational strategies. Further information on the meeting can be found at the NC2014 website at https://colloque6.inra.fr/neural_coding_2014. The meeting was supported by French National Institute for Agricultural Research, the world's leading institution in this field. Understanding how the brain processes information is one of the most challenging subjects in neuroscience. The papers presented in this special issue show a small corner of the huge diversity of this field, and illustrate how scientists with different backgrounds approach this vast subject. The diversity of disciplines engaged in these investigations is remarkable: biologists, mathematicians, physicists, psychologists, computer scientists, and statisticians, all have original tools and ideas by which to try to elucidate the underlying mechanisms. In this issue, emphasis is put on mathematical modeling of single neurons. A variety of problems in computational neuroscience accompanied with a rich diversity of mathematical tools and approaches are presented. We hope it will inspire and challenge the readers in their own research. We would like to thank the authors for their valuable contributions and the referees for their priceless effort of reviewing the manuscripts. Finally, we would like to thank Yang Kuang for supporting us and making this publication possible.

Categorical speech processing in Broca's area: an fMRI study using multivariate pattern-based analysis.

PubMed

Lee, Yune-Sang; Turkeltaub, Peter; Granger, Richard; Raizada, Rajeev D S

2012-03-14

Although much effort has been directed toward understanding the neural basis of speech processing, the neural processes involved in the categorical perception of speech have been relatively less studied, and many questions remain open. In this functional magnetic resonance imaging (fMRI) study, we probed the cortical regions mediating categorical speech perception using an advanced brain-mapping technique, whole-brain multivariate pattern-based analysis (MVPA). Normal healthy human subjects (native English speakers) were scanned while they listened to 10 consonant-vowel syllables along the /ba/-/da/ continuum. Outside of the scanner, individuals' own category boundaries were measured to divide the fMRI data into /ba/ and /da/ conditions per subject. The whole-brain MVPA revealed that Broca's area and the left pre-supplementary motor area evoked distinct neural activity patterns between the two perceptual categories (/ba/ vs /da/). Broca's area was also found when the same analysis was applied to another dataset (Raizada and Poldrack, 2007), which previously yielded the supramarginal gyrus using a univariate adaptation-fMRI paradigm. The consistent MVPA findings from two independent datasets strongly indicate that Broca's area participates in categorical speech perception, with a possible role of translating speech signals into articulatory codes. The difference in results between univariate and multivariate pattern-based analyses of the same data suggest that processes in different cortical areas along the dorsal speech perception stream are distributed on different spatial scales.

The person within: memory codes for persons and traits using fMRI repetition suppression

PubMed Central

Heleven, Elien

2016-01-01

Neuroimaging studies on trait inference demonstrated that the ventral medial prefrontal cortex (mPFC) houses neural representations of memory codes for traits . In this study, we investigate the neural code not only of traits, but also of persons who exemplify these traits. We used repetition suppression, which is a rapid suppression of the neuroimaging signal upon repeated presentation of the same stimulus or core stimulus characteristics—in this case, the implied trait and person. Participants inferred familiar person’s traits. At each trial, a critical (target) sentence described a behavior that implied a trait, and was preceded by a (prime) sentence that implied the same trait and portrayed the same person, the same trait but portrayed a different person or did not imply a trait and portrayed a different person. As predicted, we found partly overlapping repetition suppression areas in the ventral mPFC when persons and traits were repeated, indicating that not only traits but also familiar persons have a neural code in the ventral mPFC. We also found a negative correlation between activation when reading about a new person and participants’ social network size, indicating that experience with larger social groups results in less recruitment of a person code. PMID:26371337

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

The representation of object viewpoint in human visual cortex.

PubMed

Andresen, David R; Vinberg, Joakim; Grill-Spector, Kalanit

2009-04-01

Understanding the nature of object representations in the human brain is critical for understanding the neural basis of invariant object recognition. However, the degree to which object representations are sensitive to object viewpoint is unknown. Using fMRI we employed a parametric approach to examine the sensitivity to object view as a function of rotation (0 degrees-180 degrees ), category (animal/vehicle) and fMRI-adaptation paradigm (short or long-lagged). For both categories and fMRI-adaptation paradigms, object-selective regions recovered from adaptation when a rotated view of an object was shown after adaptation to a specific view of that object, suggesting that representations are sensitive to object rotation. However, we found evidence for differential representations across categories and ventral stream regions. Rotation cross-adaptation was larger for animals than vehicles, suggesting higher sensitivity to vehicle than animal rotation, and was largest in the left fusiform/occipito-temporal sulcus (pFUS/OTS), suggesting that this region has low sensitivity to rotation. Moreover, right pFUS/OTS and FFA responded more strongly to front than back views of animals (without adaptation) and rotation cross-adaptation depended both on the level of rotation and the adapting view. This result suggests a prevalence of neurons that prefer frontal views of animals in fusiform regions. Using a computational model of view-tuned neurons, we demonstrate that differential neural view tuning widths and relative distributions of neural-tuned populations in fMRI voxels can explain the fMRI results. Overall, our findings underscore the utility of parametric approaches for studying the neural basis of object invariance and suggest that there is no complete invariance to object view in the human ventral stream.

Adaptation to a cortex controlled robot attached at the pelvis and engaged during locomotion in rats

PubMed Central

Song, Weiguo; Giszter, Simon F.

2011-01-01

Brain Machine Interfaces (BMIs) should ideally show robust adaptation of the BMI across different tasks and daily activities. Most BMIs have used over-practiced tasks. Little is known about BMIs in dynamic environments. How are mechanically body-coupled BMIs integrated into ongoing rhythmic dynamics, e.g., in locomotion? To examine this we designed a novel BMI using neural discharge in the hindlimb/trunk motor cortex in rats during locomotion to control a robot attached at the pelvis. We tested neural adaptation when rats experienced (a) control locomotion, (b) ‘simple elastic load’ (a robot load on locomotion without any BMI neural control) and (c) ‘BMI with elastic load’ (in which the robot loaded locomotion and a BMI neural control could counter this load). Rats significantly offset applied loads with the BMI while preserving more normal pelvic height compared to load alone. Adaptation occurred over about 100–200 step cycles in a trial. Firing rates increased in both the loaded conditions compared to baseline. Mean phases of cells’ discharge in the step cycle shifted significantly between BMI and the simple load condition. Over time more BMI cells became positively correlated with the external force and modulated more deeply, and neurons’ network correlations on a 100ms timescale increased. Loading alone showed none of these effects. The BMI neural changes of rate and force correlations persisted or increased over repeated trials. Our results show that rats have the capacity to use motor adaptation and motor learning to fairly rapidly engage hindlimb/trunk coupled BMIs in their locomotion. PMID:21414932

Adaptive Calibration of Dynamic Accommodation—Implications for Accommodating Intraocular Lenses

PubMed Central

Schor, Clifton M.; Bharadwaj, Shrikant R.

2009-01-01

PURPOSE When the aging lens is replaced with prosthetic accommodating intraocular lenses (IOLs), with effective viscoelasticities different from those of the natural lens, mismatches could arise between the neural control of accommodation and the biomechanical properties of the new lens. These mismatches could lead to either unstable oscillations or sluggishness of dynamic accommodation. Using computer simulations, we investigated whether optimal accommodative responses could be restored through recalibration of the neural control of accommodation. Using human experiments, we also investigated whether the accommodative system has the capacity for adaptive recalibration in response to changes in lens biomechanics. METHODS Dynamic performance of two accommodating IOL prototypes was simulated for a 45-year-old accommodative system, before and after neural recalibration, using a dynamic model of accommodation. Accommodating IOL I, a prototype for an injectable accommodating IOL, was less stiff and less viscous than the natural 45-year-old lens. Accommodating IOL II, a prototype for a translating accommodating IOL, was less stiff and more viscous than the natural 45-year-old lens. Short-term adaptive recalibration of dynamic accommodation was stimulated using a double-step adaptation paradigm that optically induced changes in neuromuscular effort mimicking responses to changes in lens biomechanics. RESULTS Model simulations indicate that the unstable oscillations or sluggishness of dynamic accommodation resulting from mismatches between neural control and lens biomechanics might be restored through neural recalibration. CONCLUSIONS Empirical measures reveal that the accommodative system is capable of adaptive recalibration in response to optical loads that simulate effects of changing lens biomechanics. PMID:19044245

Decoding "us" and "them": Neural representations of generalized group concepts.

PubMed

Cikara, Mina; Van Bavel, Jay J; Ingbretsen, Zachary A; Lau, Tatiana

2017-05-01

Humans form social coalitions in every society on earth, yet we know very little about how the general concepts us and them are represented in the brain. Evolutionary psychologists have argued that the human capacity for group affiliation is a byproduct of adaptations that evolved for tracking coalitions in general. These theories suggest that humans possess a common neural code for the concepts in-group and out-group, regardless of the category by which group boundaries are instantiated. The authors used multivoxel pattern analysis to identify the neural substrates of generalized group concept representations. They trained a classifier to encode how people represented the most basic instantiation of a specific social group (i.e., arbitrary teams created in the lab with no history of interaction or associated stereotypes) and tested how well the neural data decoded membership along an objectively orthogonal, real-world category (i.e., political parties). The dorsal anterior cingulate cortex/middle cingulate cortex and anterior insula were associated with representing groups across multiple social categories. Restricting the analyses to these regions in a separate sample of participants performing an explicit categorization task, the authors replicated cross-categorization classification in anterior insula. Classification accuracy across categories was driven predominantly by the correct categorization of in-group targets, consistent with theories indicating in-group preference is more central than out-group derogation to group perception and cognition. These findings highlight the extent to which social group concepts rely on domain-general circuitry associated with encoding stimuli's functional significance. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

The Basis of Hyperspecificity in Autism: A Preliminary Suggestion Based on Properties of Neural Nets.

ERIC Educational Resources Information Center

McClelland, James L.

2000-01-01

This article discusses representation of information in neural networks and the apparent hyperspecificity that is often seen in the application of previously acquired information by children with autism. Hyperspecificity is seen as reflecting a possible feature of the neural codes used to represent concepts in the autistic brain. (Contains 12…

Adaptive Neural Star Tracker Calibration for Precision Spacecraft Pointing and Tracking

NASA Technical Reports Server (NTRS)

Bayard, David S.

1996-01-01

The Star Tracker is an essential sensor for precision pointing and tracking in most 3-axis stabilized spacecraft. In the interest (of) improving pointing performance by taking advantage of dramatic increases in flight computer power and memory anticipated over the next decade, this paper investigates the use of a neural net for adaptive in-flight calibration of the Star Tracker.

Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders.

PubMed

Mahmoudi, Babak; Principe, Jose C; Sanchez, Justin C

2010-01-01

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

PubMed

Maharlou, Hamidreza; Niakan Kalhori, Sharareh R; Shahbazi, Shahrbanoo; Ravangard, Ramin

2018-04-01

Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

Adaptive enhanced sampling by force-biasing using neural networks

NASA Astrophysics Data System (ADS)

Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

Adaptive critic learning techniques for engine torque and air-fuel ratio control.

PubMed

Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

2008-08-01

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

Dual adaptive dynamic control of mobile robots using neural networks.

PubMed

Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

2009-02-01

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel.

PubMed

Zhao, Haiquan; Zhang, Jiashu

2009-04-01

This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Data systems and computer science: Neural networks base R/T program overview

NASA Technical Reports Server (NTRS)

Gulati, Sandeep

1991-01-01

The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

NASA Astrophysics Data System (ADS)

Williams, Rube B.

2004-02-01

Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

Dynamics of cortical dendritic membrane potential and spikes in freely behaving rats.

PubMed

Moore, Jason J; Ravassard, Pascal M; Ho, David; Acharya, Lavanya; Kees, Ashley L; Vuong, Cliff; Mehta, Mayank R

2017-03-24

Neural activity in vivo is primarily measured using extracellular somatic spikes, which provide limited information about neural computation. Hence, it is necessary to record from neuronal dendrites, which can generate dendritic action potentials (DAPs) in vitro, which can profoundly influence neural computation and plasticity. We measured neocortical sub- and suprathreshold dendritic membrane potential (DMP) from putative distal-most dendrites using tetrodes in freely behaving rats over multiple days with a high degree of stability and submillisecond temporal resolution. DAP firing rates were several-fold larger than somatic rates. DAP rates were also modulated by subthreshold DMP fluctuations, which were far larger than DAP amplitude, indicating hybrid, analog-digital coding in the dendrites. Parietal DAP and DMP exhibited egocentric spatial maps comparable to pyramidal neurons. These results have important implications for neural coding and plasticity. Copyright © 2017, American Association for the Advancement of Science.

Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.

PubMed

Berkes, Pietro; Orbán, Gergo; Lengyel, Máté; Fiser, József

2011-01-07

The brain maintains internal models of its environment to interpret sensory inputs and to prepare actions. Although behavioral studies have demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.

Variable Neural Adaptive Robust Control: A Switched System Approach

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

Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

2015-05-01

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewisemore » quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.« less

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Perceptual consequences of disrupted auditory nerve activity.

PubMed

Zeng, Fan-Gang; Kong, Ying-Yee; Michalewski, Henry J; Starr, Arnold

2005-06-01

Perceptual consequences of disrupted auditory nerve activity were systematically studied in 21 subjects who had been clinically diagnosed with auditory neuropathy (AN), a recently defined disorder characterized by normal outer hair cell function but disrupted auditory nerve function. Neurological and electrophysical evidence suggests that disrupted auditory nerve activity is due to desynchronized or reduced neural activity or both. Psychophysical measures showed that the disrupted neural activity has minimal effects on intensity-related perception, such as loudness discrimination, pitch discrimination at high frequencies, and sound localization using interaural level differences. In contrast, the disrupted neural activity significantly impairs timing related perception, such as pitch discrimination at low frequencies, temporal integration, gap detection, temporal modulation detection, backward and forward masking, signal detection in noise, binaural beats, and sound localization using interaural time differences. These perceptual consequences are the opposite of what is typically observed in cochlear-impaired subjects who have impaired intensity perception but relatively normal temporal processing after taking their impaired intensity perception into account. These differences in perceptual consequences between auditory neuropathy and cochlear damage suggest the use of different neural codes in auditory perception: a suboptimal spike count code for intensity processing, a synchronized spike code for temporal processing, and a duplex code for frequency processing. We also proposed two underlying physiological models based on desynchronized and reduced discharge in the auditory nerve to successfully account for the observed neurological and behavioral data. These methods and measures cannot differentiate between these two AN models, but future studies using electric stimulation of the auditory nerve via a cochlear implant might. These results not only show the unique contribution of neural synchrony to sensory perception but also provide guidance for translational research in terms of better diagnosis and management of human communication disorders.

PARAMESH: A Parallel Adaptive Mesh Refinement Community Toolkit

NASA Technical Reports Server (NTRS)

MacNeice, Peter; Olson, Kevin M.; Mobarry, Clark; deFainchtein, Rosalinda; Packer, Charles

1999-01-01

In this paper, we describe a community toolkit which is designed to provide parallel support with adaptive mesh capability for a large and important class of computational models, those using structured, logically cartesian meshes. The package of Fortran 90 subroutines, called PARAMESH, is designed to provide an application developer with an easy route to extend an existing serial code which uses a logically cartesian structured mesh into a parallel code with adaptive mesh refinement. Alternatively, in its simplest use, and with minimal effort, it can operate as a domain decomposition tool for users who want to parallelize their serial codes, but who do not wish to use adaptivity. The package can provide them with an incremental evolutionary path for their code, converting it first to uniformly refined parallel code, and then later if they so desire, adding adaptivity.

Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image.

PubMed

Kumar, M; Mishra, S K

2017-01-01

The clinical magnetic resonance imaging (MRI) images may get corrupted due to the presence of the mixture of different types of noises such as Rician, Gaussian, impulse, etc. Most of the available filtering algorithms are noise specific, linear, and non-adaptive. There is a need to develop a nonlinear adaptive filter that adapts itself according to the requirement and effectively applied for suppression of mixed noise from different MRI images. In view of this, a novel nonlinear neural network based adaptive filter i.e. functional link artificial neural network (FLANN) whose weights are trained by a recently developed derivative free meta-heuristic technique i.e. teaching learning based optimization (TLBO) is proposed and implemented. The performance of the proposed filter is compared with five other adaptive filters and analyzed by considering quantitative metrics and evaluating the nonparametric statistical test. The convergence curve and computational time are also included for investigating the efficiency of the proposed as well as competitive filters. The simulation outcomes of proposed filter outperform the other adaptive filters. The proposed filter can be hybridized with other evolutionary technique and utilized for removing different noise and artifacts from others medical images more competently.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Self-Organization: Complex Dynamical Systems in the Evolution of Speech

NASA Astrophysics Data System (ADS)

Oudeyer, Pierre-Yves

Human vocalization systems are characterized by complex structural properties. They are combinatorial, based on the systematic reuse of phonemes, and the set of repertoires in human languages is characterized by both strong statistical regularities—universals—and a great diversity. Besides, they are conventional codes culturally shared in each community of speakers. What are the origins of the forms of speech? What are the mechanisms that permitted their evolution in the course of phylogenesis and cultural evolution? How can a shared speech code be formed in a community of individuals? This chapter focuses on the way the concept of self-organization, and its interaction with natural selection, can throw light on these three questions. In particular, a computational model is presented which shows that a basic neural equipment for adaptive holistic vocal imitation, coupling directly motor and perceptual representations in the brain, can generate spontaneously shared combinatorial systems of vocalizations in a society of babbling individuals. Furthermore, we show how morphological and physiological innate constraints can interact with these self-organized mechanisms to account for both the formation of statistical regularities and diversity in vocalization systems.

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

Social categories shape the neural representation of emotion: evidence from a visual face adaptation task

PubMed Central

Otten, Marte; Banaji, Mahzarin R.

2012-01-01

A number of recent behavioral studies have shown that emotional expressions are differently perceived depending on the race of a face, and that perception of race cues is influenced by emotional expressions. However, neural processes related to the perception of invariant cues that indicate the identity of a face (such as race) are often described to proceed independently of processes related to the perception of cues that can vary over time (such as emotion). Using a visual face adaptation paradigm, we tested whether these behavioral interactions between emotion and race also reflect interdependent neural representation of emotion and race. We compared visual emotion aftereffects when the adapting face and ambiguous test face differed in race or not. Emotion aftereffects were much smaller in different race (DR) trials than same race (SR) trials, indicating that the neural representation of a facial expression is significantly different depending on whether the emotional face is black or white. It thus seems that invariable cues such as race interact with variable face cues such as emotion not just at a response level, but also at the level of perception and neural representation. PMID:22403531

DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

PubMed

Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

2017-07-18

Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

"What" precedes "which": developmental neural tuning in face- and place-related cortex.

PubMed

Scherf, K Suzanne; Luna, Beatriz; Avidan, Galia; Behrmann, Marlene

2011-09-01

Although category-specific activation for faces in the ventral visual pathway appears adult-like in adolescence, recognition abilities for individual faces are still immature. We investigated how the ability to represent "individual" faces and houses develops at the neural level. Category-selective regions of interest (ROIs) for faces in the fusiform gyrus (FG) and for places in the parahippocampal place area (PPA) were identified individually in children, adolescents, and adults. Then, using an functional magnetic resonance imaging adaptation paradigm, we measured category selectivity and individual-level adaptation for faces and houses in each ROI. Only adults exhibited both category selectivity and individual-level adaptation bilaterally for faces in the FG and for houses in the PPA. Adolescents showed category selectivity bilaterally for faces in the FG and houses in the PPA. Despite this profile of category selectivity, adolescents only exhibited individual-level adaptation for houses bilaterally in the PPA and for faces in the "left" FG. Children only showed category-selective responses for houses in the PPA, and they failed to exhibit category-selective responses for faces in the FG and individual-level adaptation effects anywhere in the brain. These results indicate that category-level neural tuning develops prior to individual-level neural tuning and that face-related cortex is disproportionately slower in this developmental transition than is place-related cortex.

“What” Precedes “Which”: Developmental Neural Tuning in Face- and Place-Related Cortex

PubMed Central

Luna, Beatriz; Avidan, Galia; Behrmann, Marlene

2011-01-01

Although category-specific activation for faces in the ventral visual pathway appears adult-like in adolescence, recognition abilities for individual faces are still immature. We investigated how the ability to represent “individual” faces and houses develops at the neural level. Category-selective regions of interest (ROIs) for faces in the fusiform gyrus (FG) and for places in the parahippocampal place area (PPA) were identified individually in children, adolescents, and adults. Then, using an functional magnetic resonance imaging adaptation paradigm, we measured category selectivity and individual-level adaptation for faces and houses in each ROI. Only adults exhibited both category selectivity and individual-level adaptation bilaterally for faces in the FG and for houses in the PPA. Adolescents showed category selectivity bilaterally for faces in the FG and houses in the PPA. Despite this profile of category selectivity, adolescents only exhibited individual-level adaptation for houses bilaterally in the PPA and for faces in the “left” FG. Children only showed category-selective responses for houses in the PPA, and they failed to exhibit category-selective responses for faces in the FG and individual-level adaptation effects anywhere in the brain. These results indicate that category-level neural tuning develops prior to individual-level neural tuning and that face-related cortex is disproportionately slower in this developmental transition than is place-related cortex. PMID:21257673

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Hybrid information privacy system: integration of chaotic neural network and RSA coding

NASA Astrophysics Data System (ADS)

Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.

2005-03-01

Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.

SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Optical neural networks based on holographic correlators

NASA Astrophysics Data System (ADS)

Sokolov, V. K.; Shubnikov, E. I.

1995-10-01

The three most important models of neural networks — a bidirectional associative memory, Hopfield networks, and adaptive resonance networks — are used as examples to show that a holographic correlator has its place in the neural computing paradigm.

A neural net based architecture for the segmentation of mixed gray-level and binary pictures

NASA Technical Reports Server (NTRS)

Tabatabai, Ali; Troudet, Terry P.

1991-01-01

A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

Genomewide analysis of Drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation

PubMed Central

Westholm, Jakub O.; Miura, Pedro; Olson, Sara; Shenker, Sol; Joseph, Brian; Sanfilippo, Piero; Celniker, Susan E.; Graveley, Brenton R.; Lai, Eric C.

2014-01-01

Circularization was recently recognized to broadly expand transcriptome complexity. Here, we exploit massive Drosophila total RNA-sequencing data, >5 billion paired-end reads from >100 libraries covering diverse developmental stages, tissues and cultured cells, to rigorously annotate >2500 fruitfly circular RNAs. These mostly derive from back-splicing of protein-coding genes and lack poly(A) tails, and circularization of hundreds of genes is conserved across multiple Drosophila species. We elucidate structural and sequence properties of Drosophila circular RNAs, which exhibit commonalities and distinctions from mammalian circles. Notably, Drosophila circular RNAs harbor >1000 well-conserved canonical miRNA seed matches, especially within coding regions, and coding conserved miRNA sites reside preferentially within circularized exons. Finally, we analyze the developmental and tissue specificity of circular RNAs, and note their preferred derivation from neural genes and enhanced accumulation in neural tissues. Interestingly, circular isoforms increase dramatically relative to linear isoforms during CNS aging, and constitute a novel aging biomarker. PMID:25544350

Sensorineural hearing loss amplifies neural coding of envelope information in the central auditory system of chinchillas

PubMed Central

Zhong, Ziwei; Henry, Kenneth S.; Heinz, Michael G.

2014-01-01

People with sensorineural hearing loss often have substantial difficulty understanding speech under challenging listening conditions. Behavioral studies suggest that reduced sensitivity to the temporal structure of sound may be responsible, but underlying neurophysiological pathologies are incompletely understood. Here, we investigate the effects of noise-induced hearing loss on coding of envelope (ENV) structure in the central auditory system of anesthetized chinchillas. ENV coding was evaluated noninvasively using auditory evoked potentials recorded from the scalp surface in response to sinusoidally amplitude modulated tones with carrier frequencies of 1, 2, 4, and 8 kHz and a modulation frequency of 140 Hz. Stimuli were presented in quiet and in three levels of white background noise. The latency of scalp-recorded ENV responses was consistent with generation in the auditory midbrain. Hearing loss amplified neural coding of ENV at carrier frequencies of 2 kHz and above. This result may reflect enhanced ENV coding from the periphery and/or an increase in the gain of central auditory neurons. In contrast to expectations, hearing loss was not associated with a stronger adverse effect of increasing masker intensity on ENV coding. The exaggerated neural representation of ENV information shown here at the level of the auditory midbrain helps to explain previous findings of enhanced sensitivity to amplitude modulation in people with hearing loss under some conditions. Furthermore, amplified ENV coding may potentially contribute to speech perception problems in people with cochlear hearing loss by acting as a distraction from more salient acoustic cues, particularly in fluctuating backgrounds. PMID:24315815

Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

2000-01-01

The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.

Spatiotemporal Computations of an Excitable and Plastic Brain: Neuronal Plasticity Leads to Noise-Robust and Noise-Constructive Computations

PubMed Central

Toutounji, Hazem; Pipa, Gordon

2014-01-01

It is a long-established fact that neuronal plasticity occupies the central role in generating neural function and computation. Nevertheless, no unifying account exists of how neurons in a recurrent cortical network learn to compute on temporally and spatially extended stimuli. However, these stimuli constitute the norm, rather than the exception, of the brain's input. Here, we introduce a geometric theory of learning spatiotemporal computations through neuronal plasticity. To that end, we rigorously formulate the problem of neural representations as a relation in space between stimulus-induced neural activity and the asymptotic dynamics of excitable cortical networks. Backed up by computer simulations and numerical analysis, we show that two canonical and widely spread forms of neuronal plasticity, that is, spike-timing-dependent synaptic plasticity and intrinsic plasticity, are both necessary for creating neural representations, such that these computations become realizable. Interestingly, the effects of these forms of plasticity on the emerging neural code relate to properties necessary for both combating and utilizing noise. The neural dynamics also exhibits features of the most likely stimulus in the network's spontaneous activity. These properties of the spatiotemporal neural code resulting from plasticity, having their grounding in nature, further consolidate the biological relevance of our findings. PMID:24651447

The face evoked steady-state visual potentials are sensitive to the orientation, viewpoint, expression and configuration of the stimuli.

PubMed

Vakli, Pál; Németh, Kornél; Zimmer, Márta; Kovács, Gyula

2014-12-01

Previous studies demonstrated that the steady-state visual-evoked potential (SSVEP) is reduced to the repetition of the same identity face when compared with the presentation of different identities, suggesting high-level neural adaptation to face identity. Here we investigated whether the SSVEP is sensitive to the orientation, viewpoint, expression and configuration of faces (Experiment 1), and whether adaptation to identity at the level of the SSVEP is robust enough to generalize across these properties (Experiment 2). In Experiment 1, repeating the same identity face with continuously changing orientation, viewpoint or expression evoked a larger SSVEP than the repetition of an unchanged face, presumably reflecting a release of adaptation. A less robust effect was observed in the case of changes affecting face configuration. In Experiment 2, we found a similar release of adaptation for faces with changing orientation, viewpoint and configuration, as there was no difference between the SSVEP for the same and different identity faces. However, we found an adaptation effect for faces with changing expressions, suggesting that face identity coding, as reflected in the SSVEP, is largely independent of the emotion displayed by faces. Taken together, these results imply that the SSVEP taps high-level face representations which abstract away from the changeable aspects of the face and likely incorporate information about face configuration, but which are specific to the orientation and viewpoint of the face. Copyright © 2014 Elsevier B.V. All rights reserved.

Physical mechanisms may be as important as brain mechanisms in evolution of speech.

PubMed

de Boer, Bart; Perlman, Marcus

2014-12-01

We present two arguments why physical adaptations for vocalization may be as important as neural adaptations. First, fine control over vocalization is not easy for physical reasons, and modern humans may be exceptional. Second, we present an example of a gorilla that shows rudimentary voluntary control over vocalization, indicating that some neural control is already shared with great apes.

Neural network technologies

NASA Technical Reports Server (NTRS)

Villarreal, James A.

1991-01-01

A whole new arena of computer technologies is now beginning to form. Still in its infancy, neural network technology is a biologically inspired methodology which draws on nature's own cognitive processes. The Software Technology Branch has provided a software tool, Neural Execution and Training System (NETS), to industry, government, and academia to facilitate and expedite the use of this technology. NETS is written in the C programming language and can be executed on a variety of machines. Once a network has been debugged, NETS can produce a C source code which implements the network. This code can then be incorporated into other software systems. Described here are various software projects currently under development with NETS and the anticipated future enhancements to NETS and the technology.

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.

A Two-Hit Model of Autism: Adolescence as the Second Hit

PubMed Central

Picci, Giorgia; Scherf, K. Suzanne

2015-01-01

Adolescence brings dramatic changes in behavior and neural organization. Unfortunately, for some 30% of individuals with autism, there is marked decline in adaptive functioning during adolescence. We propose a two-hit model of autism. First, early perturbations in neural development function as a “first hit” that sets up a neural system that is “built to fail” in the face of a second hit. Second, the confluence of pubertal hormones, neural reorganization, and increasing social demands during adolescence provides the “second hit” that interferes with the ability to transition into adult social roles and levels of adaptive functioning. In support of this model, we review evidence about adolescent-specific neural and behavioral development in autism. We conclude with predictions and recommendations for empirical investigation about several domains in which developmental trajectories for individuals with autism may be uniquely deterred in adolescence. PMID:26609500

Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach.

PubMed

Noel, Jean-Paul; Blanke, Olaf; Magosso, Elisa; Serino, Andrea

2018-06-01

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons' RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant's peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons' RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli. NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic properties are unexplained. We demonstrate with a psychophysical approach that PPS enlarges as incoming stimulus velocity increases, efficiently preventing contacts with faster approaching objects. We present a neurocomputational model of multisensory PPS implementing neural adaptation to persistent stimulation to propose a neurophysiological mechanism underlying this effect.

Transcranial Alternating Current Stimulation Attenuates Neuronal Adaptation.

PubMed

Kar, Kohitij; Duijnhouwer, Jacob; Krekelberg, Bart

2017-03-01

We previously showed that brief application of 2 mA (peak-to-peak) transcranial currents alternating at 10 Hz significantly reduces motion adaptation in humans. This is but one of many behavioral studies showing that weak currents applied to the scalp modulate neural processing. Transcranial stimulation has been shown to improve perception, learning, and a range of clinical symptoms. Few studies, however, have measured the neural consequences of transcranial current stimulation. We capitalized on the strong link between motion perception and neural activity in the middle temporal (MT) area of the macaque monkey to study the neural mechanisms that underlie the behavioral consequences of transcranial alternating current stimulation. First, we observed that 2 mA currents generated substantial intracranial fields, which were much stronger in the stimulated hemisphere (0.12 V/m) than on the opposite side of the brain (0.03 V/m). Second, we found that brief application of transcranial alternating current stimulation at 10 Hz reduced spike-frequency adaptation of MT neurons and led to a broadband increase in the power spectrum of local field potentials. Together, these findings provide a direct demonstration that weak electric fields applied to the scalp significantly affect neural processing in the primate brain and that this includes a hitherto unknown mechanism that attenuates sensory adaptation. SIGNIFICANCE STATEMENT Transcranial stimulation has been claimed to improve perception, learning, and a range of clinical symptoms. Little is known, however, how transcranial current stimulation generates such effects, and the search for better stimulation protocols proceeds largely by trial and error. We investigated, for the first time, the neural consequences of stimulation in the monkey brain. We found that even brief application of alternating current stimulation reduced the effects of adaptation on single-neuron firing rates and local field potentials; this mechanistic insight explains previous behavioral findings and suggests a novel way to modulate neural information processing using transcranial currents. In addition, by developing an animal model to help understand transcranial stimulation, this study will aid the rational design of stimulation protocols for the treatment of mental illnesses, and the improvement of perception and learning. Copyright © 2017 the authors 0270-6474/17/372325-11$15.00/0.

A phase transition in the first passage of a Brownian process through a fluctuating boundary with implications for neural coding.

PubMed

Taillefumier, Thibaud; Magnasco, Marcelo O

2013-04-16

Finding the first time a fluctuating quantity reaches a given boundary is a deceptively simple-looking problem of vast practical importance in physics, biology, chemistry, neuroscience, economics, and industrial engineering. Problems in which the bound to be traversed is itself a fluctuating function of time include widely studied problems in neural coding, such as neuronal integrators with irregular inputs and internal noise. We show that the probability p(t) that a Gauss-Markov process will first exceed the boundary at time t suffers a phase transition as a function of the roughness of the boundary, as measured by its Hölder exponent H. The critical value occurs when the roughness of the boundary equals the roughness of the process, so for diffusive processes the critical value is Hc = 1/2. For smoother boundaries, H > 1/2, the probability density is a continuous function of time. For rougher boundaries, H < 1/2, the probability is concentrated on a Cantor-like set of zero measure: the probability density becomes divergent, almost everywhere either zero or infinity. The critical point Hc = 1/2 corresponds to a widely studied case in the theory of neural coding, in which the external input integrated by a model neuron is a white-noise process, as in the case of uncorrelated but precisely balanced excitatory and inhibitory inputs. We argue that this transition corresponds to a sharp boundary between rate codes, in which the neural firing probability varies smoothly, and temporal codes, in which the neuron fires at sharply defined times regardless of the intensity of internal noise.

Neural coding of sound envelope in reverberant environments.

PubMed

Slama, Michaël C C; Delgutte, Bertrand

2015-03-11

Speech reception depends critically on temporal modulations in the amplitude envelope of the speech signal. Reverberation encountered in everyday environments can substantially attenuate these modulations. To assess the effect of reverberation on the neural coding of amplitude envelope, we recorded from single units in the inferior colliculus (IC) of unanesthetized rabbit using sinusoidally amplitude modulated (AM) broadband noise stimuli presented in simulated anechoic and reverberant environments. Although reverberation degraded both rate and temporal coding of AM in IC neurons, in most neurons, the degradation in temporal coding was smaller than the AM attenuation in the stimulus. This compensation could largely be accounted for by the compressive shape of the modulation input-output function (MIOF), which describes the nonlinear transformation of modulation depth from acoustic stimuli into neural responses. Additionally, in a subset of neurons, the temporal coding of AM was better for reverberant stimuli than for anechoic stimuli having the same modulation depth at the ear. Using hybrid anechoic stimuli that selectively possess certain properties of reverberant sounds, we show that this reverberant advantage is not caused by envelope distortion, static interaural decorrelation, or spectral coloration. Overall, our results suggest that the auditory system may possess dual mechanisms that make the coding of amplitude envelope relatively robust in reverberation: one general mechanism operating for all stimuli with small modulation depths, and another mechanism dependent on very specific properties of reverberant stimuli, possibly the periodic fluctuations in interaural correlation at the modulation frequency. Copyright © 2015 the authors 0270-6474/15/354452-17$15.00/0.

Monitoring the Performance of a Neuro-Adaptive Controller

NASA Technical Reports Server (NTRS)

Schumann, Johann; Gupta, Pramod

2004-01-01

Traditional control has proven to be ineffective to deal with catastrophic changes or slow degradation of complex, highly nonlinear systems like aircraft or spacecraft, robotics, or flexible manufacturing systems. Control systems which can adapt toward changes in the plant have been proposed as they offer many advantages (e.g., better performance, controllability of aircraft despite of a damaged wing). In the last few years, use of neural networks in adaptive controllers (neuro-adaptive control) has been studied actively. Neural networks of various architectures have been used successfully for online learning adaptive controllers. In such a typical control architecture, the neural network receives as an input the current deviation between desired and actual plant behavior and, by on-line training, tries to minimize this discrepancy (e.g.; by producing a control augmentation signal). Even though neuro-adaptive controllers offer many advantages, they have not been used in mission- or safety-critical applications, because performance and safety guarantees cannot b e provided at development time-a major prerequisite for safety certification (e.g., by the FAA or NASA). Verification and Validation (V&V) of an adaptive controller requires the development of new analysis techniques which can demonstrate that the control system behaves safely under all operating conditions. Because of the requirement to adapt toward unforeseen changes during operation, i.e., in real time, design-time V&V is not sufficient.

An emergence of coordinated communication in populations of agents.

PubMed

Kvasnicka, V; Pospichal, J

1999-01-01

The purpose of this article is to demonstrate that coordinated communication spontaneously emerges in a population composed of agents that are capable of specific cognitive activities. Internal states of agents are characterized by meaning vectors. Simple neural networks composed of one layer of hidden neurons perform cognitive activities of agents. An elementary communication act consists of the following: (a) two agents are selected, where one of them is declared the speaker and the other the listener; (b) the speaker codes a selected meaning vector onto a sequence of symbols and sends it to the listener as a message; and finally, (c) the listener decodes this message into a meaning vector and adapts his or her neural network such that the differences between speaker and listener meaning vectors are decreased. A Darwinian evolution enlarged by ideas from the Baldwin effect and Dawkins' memes is simulated by a simple version of an evolutionary algorithm without crossover. The agent fitness is determined by success of the mutual pairwise communications. It is demonstrated that agents in the course of evolution gradually do a better job of decoding received messages (they are closer to meaning vectors of speakers) and all agents gradually start to use the same vocabulary for the common communication. Moreover, if agent meaning vectors contain regularities, then these regularities are manifested also in messages created by agent speakers, that is, similar parts of meaning vectors are coded by similar symbol substrings. This observation is considered a manifestation of the emergence of a grammar system in the common coordinated communication.

Perceptually-Based Adaptive JPEG Coding

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Rosenholtz, Ruth; Null, Cynthia H. (Technical Monitor)

1996-01-01

An extension to the JPEG standard (ISO/IEC DIS 10918-3) allows spatial adaptive coding of still images. As with baseline JPEG coding, one quantization matrix applies to an entire image channel, but in addition the user may specify a multiplier for each 8 x 8 block, which multiplies the quantization matrix, yielding the new matrix for the block. MPEG 1 and 2 use much the same scheme, except there the multiplier changes only on macroblock boundaries. We propose a method for perceptual optimization of the set of multipliers. We compute the perceptual error for each block based upon DCT quantization error adjusted according to contrast sensitivity, light adaptation, and contrast masking, and pick the set of multipliers which yield maximally flat perceptual error over the blocks of the image. We investigate the bitrate savings due to this adaptive coding scheme and the relative importance of the different sorts of masking on adaptive coding.

The time course of symbolic number adaptation: oscillatory EEG activity and event-related potential analysis.

PubMed

Hsu, Yi-Fang; Szűcs, Dénes

2012-02-15

Several functional magnetic resonance imaging (fMRI) studies have used neural adaptation paradigms to detect anatomical locations of brain activity related to number processing. However, currently not much is known about the temporal structure of number adaptation. In the present study, we used electroencephalography (EEG) to elucidate the time course of neural events in symbolic number adaptation. The numerical distance of deviants relative to standards was manipulated. In order to avoid perceptual confounds, all levels of deviants consisted of perceptually identical stimuli. Multiple successive numerical distance effects were detected in event-related potentials (ERPs). Analysis of oscillatory activity further showed at least two distinct stages of neural processes involved in the automatic analysis of numerical magnitude, with the earlier effect emerging at around 200ms and the later effect appearing at around 400ms. The findings support for the hypothesis that numerical magnitude processing involves a succession of cognitive events. Crown Copyright © 2011. Published by Elsevier Inc. All rights reserved.

Neural networks for simultaneous classification and parameter estimation in musical instrument control

NASA Astrophysics Data System (ADS)

Lee, Michael; Freed, Adrian; Wessel, David

1992-08-01

In this report we present our tools for prototyping adaptive user interfaces in the context of real-time musical instrument control. Characteristic of most human communication is the simultaneous use of classified events and estimated parameters. We have integrated a neural network object into the MAX language to explore adaptive user interfaces that considers these facets of human communication. By placing the neural processing in the context of a flexible real-time musical programming environment, we can rapidly prototype experiments on applications of adaptive interfaces and learning systems to musical problems. We have trained networks to recognize gestures from a Mathews radio baton, Nintendo Power GloveTM, and MIDI keyboard gestural input devices. In one experiment, a network successfully extracted classification and attribute data from gestural contours transduced by a continuous space controller, suggesting their application in the interpretation of conducting gestures and musical instrument control. We discuss network architectures, low-level features extracted for the networks to operate on, training methods, and musical applications of adaptive techniques.

Adaptive Neural Control for a Class of Pure-Feedback Nonlinear Systems via Dynamic Surface Technique.

PubMed

Liu, Zongcheng; Dong, Xinmin; Xue, Jianping; Li, Hongbo; Chen, Yong

2016-09-01

This brief addresses the adaptive control problem for a class of pure-feedback systems with nonaffine functions possibly being nondifferentiable. Without using the mean value theorem, the difficulty of the control design for pure-feedback systems is overcome by modeling the nonaffine functions appropriately. With the help of neural network approximators, an adaptive neural controller is developed by combining the dynamic surface control (DSC) and minimal learning parameter (MLP) techniques. The key features of our approach are that, first, the restrictive assumptions on the partial derivative of nonaffine functions are removed, second, the DSC technique is used to avoid "the explosion of complexity" in the backstepping design, and the number of adaptive parameters is reduced significantly using the MLP technique, third, smooth robust compensators are employed to circumvent the influences of approximation errors and disturbances. Furthermore, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, the simulation results are provided to demonstrate the effectiveness of the designed method.

Adaptive neural network motion control for aircraft under uncertainty conditions

NASA Astrophysics Data System (ADS)

Efremov, A. V.; Tiaglik, M. S.; Tiumentsev, Yu V.

2018-02-01

We need to provide motion control of modern and advanced aircraft under diverse uncertainty conditions. This problem can be solved by using adaptive control laws. We carry out an analysis of the capabilities of these laws for such adaptive systems as MRAC (Model Reference Adaptive Control) and MPC (Model Predictive Control). In the case of a nonlinear control object, the most efficient solution to the adaptive control problem is the use of neural network technologies. These technologies are suitable for the development of both a control object model and a control law for the object. The approximate nature of the ANN model was taken into account by introducing additional compensating feedback into the control system. The capabilities of adaptive control laws under uncertainty in the source data are considered. We also conduct simulations to assess the contribution of adaptivity to the behavior of the system.

Neural and receptor cochlear potentials obtained by transtympanic electrocochleography in auditory neuropathy.

PubMed

Santarelli, Rosamaria; Starr, Arnold; Michalewski, Henry J; Arslan, Edoardo

2008-05-01

Transtympanic electrocochleography (ECochG) was recorded bilaterally in children and adults with auditory neuropathy (AN) to evaluate receptor and neural generators. Test stimuli were clicks from 60 to 120dB p.e. SPL. Measures obtained from eight AN subjects were compared to 16 normally hearing children. Receptor cochlear microphonics (CMs) in AN were of normal or enhanced amplitude. Neural compound action potentials (CAPs) and receptor summating potentials (SPs) were identified in five AN ears. ECochG potentials in those ears without CAPs were of negative polarity and of normal or prolonged duration. We used adaptation to rapid stimulus rates to distinguish whether the generators of the negative potentials were of neural or receptor origin. Adaptation in controls resulted in amplitude reduction of CAP twice that of SP without affecting the duration of ECochG potentials. In seven AN ears without CAP and with prolonged negative potential, adaptation was accompanied by reduction of both amplitude and duration of the negative potential to control values consistent with neural generation. In four ears without CAP and with normal duration potentials, adaptation was without effect consistent with receptor generation. In five AN ears with CAP, there was reduction in amplitude of CAP and SP as controls but with a significant decrease in response duration. Three patterns of cochlear potentials were identified in AN: (1) presence of receptor SP without CAP consistent with pre-synaptic disorder of inner hair cells; (2) presence of both SP and CAP consistent with post-synaptic disorder of proximal auditory nerve; (3) presence of prolonged neural potentials without a CAP consistent with post-synaptic disorder of nerve terminals. Cochlear potential measures may identify pre- and post-synaptic disorders of inner hair cells and auditory nerves in AN.

Training-specific functional, neural, and hypertrophic adaptations to explosive- vs. sustained-contraction strength training.

PubMed

Balshaw, Thomas G; Massey, Garry J; Maden-Wilkinson, Thomas M; Tillin, Neale A; Folland, Jonathan P

2016-06-01

Training specificity is considered important for strength training, although the functional and underpinning physiological adaptations to different types of training, including brief explosive contractions, are poorly understood. This study compared the effects of 12 wk of explosive-contraction (ECT, n = 13) vs. sustained-contraction (SCT, n = 16) strength training vs. control (n = 14) on the functional, neural, hypertrophic, and intrinsic contractile characteristics of healthy young men. Training involved 40 isometric knee extension repetitions (3 times/wk): contracting as fast and hard as possible for ∼1 s (ECT) or gradually increasing to 75% of maximum voluntary torque (MVT) before holding for 3 s (SCT). Torque and electromyography during maximum and explosive contractions, torque during evoked octet contractions, and total quadriceps muscle volume (QUADSVOL) were quantified pre and post training. MVT increased more after SCT than ECT [23 vs. 17%; effect size (ES) = 0.69], with similar increases in neural drive, but greater QUADSVOL changes after SCT (8.1 vs. 2.6%; ES = 0.74). ECT improved explosive torque at all time points (17-34%; 0.54 ≤ ES ≤ 0.76) because of increased neural drive (17-28%), whereas only late-phase explosive torque (150 ms, 12%; ES = 1.48) and corresponding neural drive (18%) increased after SCT. Changes in evoked torque indicated slowing of the contractile properties of the muscle-tendon unit after both training interventions. These results showed training-specific functional changes that appeared to be due to distinct neural and hypertrophic adaptations. ECT produced a wider range of functional adaptations than SCT, and given the lesser demands of ECT, this type of training provides a highly efficient means of increasing function. Copyright © 2016 the American Physiological Society.

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.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

PubMed Central

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

Visual reliability and information rate in the retina of a nocturnal bee.

PubMed

Frederiksen, Rikard; Wcislo, William T; Warrant, Eric J

2008-03-11

Nocturnal animals relying on vision typically have eyes that are optically and morphologically adapted for both increased sensitivity and greater information capacity in dim light. Here, we investigate whether adaptations for increased sensitivity also are found in their photoreceptors by using closely related and fast-flying nocturnal and diurnal bees as model animals. The nocturnal bee Megalopta genalis is capable of foraging and homing by using visually discriminated landmarks at starlight intensities. Megalopta's near relative, Lasioglossum leucozonium, performs these tasks only in bright sunshine. By recording intracellular responses to Gaussian white-noise stimuli, we show that photoreceptors in Megalopta actually code less information at most light levels than those in Lasioglossum. However, as in several other nocturnal arthropods, Megalopta's photoreceptors possess a much greater gain of transduction, indicating that nocturnal photoreceptors trade information capacity for sensitivity. By sacrificing photoreceptor signal-to-noise ratio and information capacity in dim light for an increased gain and, thus, an increased sensitivity, this strategy can benefit nocturnal insects that use neural summation to improve visual reliability at night.

Software Helps Retrieve Information Relevant to the User

NASA Technical Reports Server (NTRS)

Mathe, Natalie; Chen, James

2003-01-01

The Adaptive Indexing and Retrieval Agent (ARNIE) is a code library, designed to be used by an application program, that assists human users in retrieving desired information in a hypertext setting. Using ARNIE, the program implements a computational model for interactively learning what information each human user considers relevant in context. The model, called a "relevance network," incrementally adapts retrieved information to users individual profiles on the basis of feedback from the users regarding specific queries. The model also generalizes such knowledge for subsequent derivation of relevant references for similar queries and profiles, thereby, assisting users in filtering information by relevance. ARNIE thus enables users to categorize and share information of interest in various contexts. ARNIE encodes the relevance and structure of information in a neural network dynamically configured with a genetic algorithm. ARNIE maintains an internal database, wherein it saves associations, and from which it returns associated items in response to a query. A C++ compiler for a platform on which ARNIE will be utilized is necessary for creating the ARNIE library but is not necessary for the execution of the software.

Animal-to-Animal Variation in Odor Preference and Neural Representation of Odors

NASA Astrophysics Data System (ADS)

Honegger, Kyle; Smith, Matthew; Turner, Glenn; de Bivort, Benjamin

Across any population of animals, individuals exhibit diverse behaviors and reactions to sensory stimuli like tastes and odors. While idiosyncratic behavior is ubiquitous, its biological basis is poorly understood. In this talk, I will present evidence that individual fruit flies (Drosophila melanogaster) display idiosyncratic olfactory behaviors and discuss our ongoing efforts to map these behavioral differences to variation in neural circuits. Using a high-throughput, single-fly assay for odor preference, we have demonstrated that highly inbred flies display substantial animal-to-animal variability, beyond that expected from experimental error, and that these preferences persist over days. Using in vivo two-photon calcium imaging, we are beginning to examine the idiosyncrasy of neural coding in the fly olfactory pathway and find that the odor responses of individual processing channels in the antennal lobe can vary substantially from fly to fly. These results imply that individual differences in neural coding may be used to predict the idiosyncratic behavior of an individual - a hypothesis we are currently testing by imaging neural activity from flies after measuring their odor preferences.

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

Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

PubMed

Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

2010-09-01

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

The person within: memory codes for persons and traits using fMRI repetition suppression.

PubMed

Heleven, Elien; Van Overwalle, Frank

2016-01-01

Neuroimaging studies on trait inference demonstrated that the ventral medial prefrontal cortex (mPFC) houses neural representations of memory codes for traits . In this study, we investigate the neural code not only of traits, but also of persons who exemplify these traits. We used repetition suppression, which is a rapid suppression of the neuroimaging signal upon repeated presentation of the same stimulus or core stimulus characteristics-in this case, the implied trait and person. Participants inferred familiar person's traits. At each trial, a critical (target) sentence described a behavior that implied a trait, and was preceded by a (prime) sentence that implied the same trait and portrayed the same person, the same trait but portrayed a different person or did not imply a trait and portrayed a different person. As predicted, we found partly overlapping repetition suppression areas in the ventral mPFC when persons and traits were repeated, indicating that not only traits but also familiar persons have a neural code in the ventral mPFC. We also found a negative correlation between activation when reading about a new person and participants' social network size, indicating that experience with larger social groups results in less recruitment of a person code. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

The Human Retrosplenial Cortex and Thalamus Code Head Direction in a Global Reference Frame.

PubMed

Shine, Jonathan P; Valdés-Herrera, José P; Hegarty, Mary; Wolbers, Thomas

2016-06-15

Spatial navigation is a multisensory process involving integration of visual and body-based cues. In rodents, head direction (HD) cells, which are most abundant in the thalamus, integrate these cues to code facing direction. Human fMRI studies examining HD coding in virtual environments (VE) have reported effects in retrosplenial complex and (pre-)subiculum, but not the thalamus. Furthermore, HD coding appeared insensitive to global landmarks. These tasks, however, provided only visual cues for orientation, and attending to global landmarks did not benefit task performance. In the present study, participants explored a VE comprising four separate locales, surrounded by four global landmarks. To provide body-based cues, participants wore a head-mounted display so that physical rotations changed facing direction in the VE. During subsequent MRI scanning, subjects saw stationary views of the environment and judged whether their orientation was the same as in the preceding trial. Parameter estimates extracted from retrosplenial cortex and the thalamus revealed significantly reduced BOLD responses when HD was repeated. Moreover, consistent with rodent findings, the signal did not continue to adapt over repetitions of the same HD. These results were supported by a whole-brain analysis showing additional repetition suppression in the precuneus. Together, our findings suggest that: (1) consistent with the rodent literature, the human thalamus may integrate visual and body-based, orientation cues; (2) global reference frame cues can be used to integrate HD across separate individual locales; and (3) immersive training procedures providing full body-based cues may help to elucidate the neural mechanisms supporting spatial navigation. In rodents, head direction (HD) cells signal facing direction in the environment via increased firing when the animal assumes a certain orientation. Distinct brain regions, the retrosplenial cortex (RSC) and thalamus, code for visual and vestibular cues of orientation, respectively. Putative HD signals have been observed in human RSC but not the thalamus, potentially because body-based cues were not provided. Here, participants encoded HD in a novel virtual environment while wearing a head-mounted display to provide body-based cues for orientation. In subsequent fMRI scanning, we found evidence of an HD signal in RSC, thalamus, and precuneus. These findings harmonize rodent and human data, and suggest that immersive training procedures provide a viable way to examine the neural basis of navigation. Copyright © 2016 the authors 0270-6474/16/366371-11$15.00/0.

A cost minimisation and Bayesian inference model predicts startle reflex modulation across species.

PubMed

Bach, Dominik R

2015-04-07

In many species, rapid defensive reflexes are paramount to escaping acute danger. These reflexes are modulated by the state of the environment. This is exemplified in fear-potentiated startle, a more vigorous startle response during conditioned anticipation of an unrelated threatening event. Extant explanations of this phenomenon build on descriptive models of underlying psychological states, or neural processes. Yet, they fail to predict invigorated startle during reward anticipation and instructed attention, and do not explain why startle reflex modulation evolved. Here, we fill this lacuna by developing a normative cost minimisation model based on Bayesian optimality principles. This model predicts the observed pattern of startle modification by rewards, punishments, instructed attention, and several other states. Moreover, the mathematical formalism furnishes predictions that can be tested experimentally. Comparing the model with existing data suggests a specific neural implementation of the underlying computations which yields close approximations to the optimal solution under most circumstances. This analysis puts startle modification into the framework of Bayesian decision theory and predictive coding, and illustrates the importance of an adaptive perspective to interpret defensive behaviour across species. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

Development of the auditory system

PubMed Central

Litovsky, Ruth

2015-01-01

Auditory development involves changes in the peripheral and central nervous system along the auditory pathways, and these occur naturally, and in response to stimulation. Human development occurs along a trajectory that can last decades, and is studied using behavioral psychophysics, as well as physiologic measurements with neural imaging. The auditory system constructs a perceptual space that takes information from objects and groups, segregates sounds, and provides meaning and access to communication tools such as language. Auditory signals are processed in a series of analysis stages, from peripheral to central. Coding of information has been studied for features of sound, including frequency, intensity, loudness, and location, in quiet and in the presence of maskers. In the latter case, the ability of the auditory system to perform an analysis of the scene becomes highly relevant. While some basic abilities are well developed at birth, there is a clear prolonged maturation of auditory development well into the teenage years. Maturation involves auditory pathways. However, non-auditory changes (attention, memory, cognition) play an important role in auditory development. The ability of the auditory system to adapt in response to novel stimuli is a key feature of development throughout the nervous system, known as neural plasticity. PMID:25726262

Dynamical encoding of looming, receding, and focussing

NASA Astrophysics Data System (ADS)

Longtin, Andre; Clarke, Stephen Elisha; Maler, Leonard; CenterNeural Dynamics Collaboration

This talk will discuss a non-conventional neural coding task that may apply more broadly to many senses in higher vertebrates. We ask whether and how a non-visual sensory system can focus on an object. We present recent experimental and modeling work that shows how the early sensory circuitry of electric sense can perform such neuronal focusing that is manifested behaviorally. This sense is the main one used by weakly electric fish to navigate, locate prey and communicate in the murky waters of their natural habitat. We show that there is a distance at which the Fisher information of a neuron's response to a looming and receding object is maximized, and that this distance corresponds to a behaviorally relevant one chosen by these animals. Strikingly, this maximum occurs at a bifurcation between tonic firing and bursting. We further discuss how the invariance of this distance to signal attributes can arise, a process that first involves power-law spike frequency adaptation. The talk will also highlight the importance of expanding the classic dual neural encoding of contrast using ON and OFF cells in the context of looming and receding stimuli. The authors acknowledge support from CIHR and NSERC.

The behavioral and neural binding phenomena during visuomotor integration of angry facial expressions.

PubMed

Coll, Sélim Yahia; Ceravolo, Leonardo; Frühholz, Sascha; Grandjean, Didier

2018-05-02

Different parts of our brain code the perceptual features and actions related to an object, causing a binding problem, in which the brain has to integrate information related to an event without any interference regarding the features and actions involved in other concurrently processed events. Using a paradigm similar to Hommel, who revealed perception-action bindings, we showed that emotion could bind with motor actions when relevant, and in specific conditions, irrelevant for the task. By adapting our protocol to a functional Magnetic Resonance Imaging paradigm we investigated, in the present study, the neural bases of the emotion-action binding with task-relevant angry faces. Our results showed that emotion bound with motor responses. This integration revealed increased activity in distributed brain areas involved in: (i) memory, including the hippocampi; (ii) motor actions with the precentral gyri; (iii) and emotion processing with the insula. Interestingly, increased activations in the cingulate gyri and putamen, highlighted their potential key role in the emotion-action binding, due to their involvement in emotion processing, motor actions, and memory. The present study confirmed our previous results and point out for the first time the functional brain activity related to the emotion-action association.

Shared developmental and evolutionary origins for neural basis of vocal–acoustic and pectoral–gestural signaling

PubMed Central

Bass, Andrew H.; Chagnaud, Boris P.

2012-01-01

Acoustic signaling behaviors are widespread among bony vertebrates, which include the majority of living fishes and tetrapods. Developmental studies in sound-producing fishes and tetrapods indicate that central pattern generating networks dedicated to vocalization originate from the same caudal hindbrain rhombomere (rh) 8-spinal compartment. Together, the evidence suggests that vocalization and its morphophysiological basis, including mechanisms of vocal–respiratory coupling that are widespread among tetrapods, are ancestral characters for bony vertebrates. Premotor-motor circuitry for pectoral appendages that function in locomotion and acoustic signaling develops in the same rh8-spinal compartment. Hence, vocal and pectoral phenotypes in fishes share both developmental origins and roles in acoustic communication. These findings lead to the proposal that the coupling of more highly derived vocal and pectoral mechanisms among tetrapods, including those adapted for nonvocal acoustic and gestural signaling, originated in fishes. Comparative studies further show that rh8 premotor populations have distinct neurophysiological properties coding for equally distinct behavioral attributes such as call duration. We conclude that neural network innovations in the spatiotemporal patterning of vocal and pectoral mechanisms of social communication, including forelimb gestural signaling, have their evolutionary origins in the caudal hindbrain of fishes. PMID:22723366

Adaptive Neurons For Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1990-01-01

Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

Neural network applications in telecommunications

NASA Technical Reports Server (NTRS)

Alspector, Joshua

1994-01-01

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Neural self-tuning adaptive control of non-minimum phase system

NASA Technical Reports Server (NTRS)

Ho, Long T.; Bialasiewicz, Jan T.; Ho, Hai T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

Adaptive variable-length coding for efficient compression of spacecraft television data.

NASA Technical Reports Server (NTRS)

Rice, R. F.; Plaunt, J. R.

1971-01-01

An adaptive variable length coding system is presented. Although developed primarily for the proposed Grand Tour missions, many features of this system clearly indicate a much wider applicability. Using sample to sample prediction, the coding system produces output rates within 0.25 bit/picture element (pixel) of the one-dimensional difference entropy for entropy values ranging from 0 to 8 bit/pixel. This is accomplished without the necessity of storing any code words. Performance improvements of 0.5 bit/pixel can be simply achieved by utilizing previous line correlation. A Basic Compressor, using concatenated codes, adapts to rapid changes in source statistics by automatically selecting one of three codes to use for each block of 21 pixels. The system adapts to less frequent, but more dramatic, changes in source statistics by adjusting the mode in which the Basic Compressor operates on a line-to-line basis. Furthermore, the compression system is independent of the quantization requirements of the pulse-code modulation system.

Reduced Order Adaptive Controllers for Distributed Parameter Systems

DTIC Science & Technology

2005-09-01

pitch moment [J313. Neural Network adaptive output feedback control for intensive care unit sedation and intraop- erative anesthesia . Neural network...depth of anesthesia for noncardiac surgery [C3, J15]. These results present an extension of [C8, J9, J10]. Modelling and vibration control of...for Intensive Care Unit Sedation and Operating Room Hypnosis , Submitted to 6 Special Issue of SIAM Journal of Control and Optimization on Control

Autism as an adaptive common variant pathway for human brain development.

PubMed

Johnson, Mark H

2017-06-01

While research on focal perinatal lesions has provided evidence for recovery of function, much less is known about processes of brain adaptation resulting from mild but widespread disturbances to neural processing over the early years (such as alterations in synaptic efficiency). Rather than being viewed as a direct behavioral consequence of life-long neural dysfunction, I propose that autism is best viewed as the end result of engaging adaptive processes during a sensitive period. From this perspective, autism is not appropriately described as a disorder of neurodevelopment, but rather as an adaptive common variant pathway of human functional brain development. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Neuroplasticity beyond Sounds: Neural Adaptations Following Long-Term Musical Aesthetic Experiences

PubMed Central

Reybrouck, Mark; Brattico, Elvira

2015-01-01

Capitalizing from neuroscience knowledge on how individuals are affected by the sound environment, we propose to adopt a cybernetic and ecological point of view on the musical aesthetic experience, which includes subprocesses, such as feature extraction and integration, early affective reactions and motor actions, style mastering and conceptualization, emotion and proprioception, evaluation and preference. In this perspective, the role of the listener/composer/performer is seen as that of an active “agent” coping in highly individual ways with the sounds. The findings concerning the neural adaptations in musicians, following long-term exposure to music, are then reviewed by keeping in mind the distinct subprocesses of a musical aesthetic experience. We conclude that these neural adaptations can be conceived of as the immediate and lifelong interactions with multisensorial stimuli (having a predominant auditory component), which result in lasting changes of the internal state of the “agent”. In a continuous loop, these changes affect, in turn, the subprocesses involved in a musical aesthetic experience, towards the final goal of achieving better perceptual, motor and proprioceptive responses to the immediate demands of the sounding environment. The resulting neural adaptations in musicians closely depend on the duration of the interactions, the starting age, the involvement of attention, the amount of motor practice and the musical genre played. PMID:25807006

Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

PubMed

Li, Zhijun; Su, Chun-Yi

2013-09-01

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

The Cognitive, Perceptual, and Neural Bases of Skilled Performance

DTIC Science & Technology

1994-02-01

technical report 3/15/90-3/14/93 4. TITLE AND SUBTITLE S. FUNDING NUMBERS The Cognitive , Perceptual, and Neural Bases AFOSR 90-0175 of Skilled... COGNITIVE , PERCEPTUAL, AND NEURAL BASES OF SKILLED PERFORMANCE March 15, 1990-March 14, 1993 Principal Investigator: Stephen Grossberg Wang Professor of... Cognitive and Neural Systems Professor of Mathematics, Psychology, and Biomedical Engineering Director, Center for Adaptive Systems Chairman, Department

Nonlinear neural control with power systems applications

NASA Astrophysics Data System (ADS)

Chen, Dingguo

1998-12-01

Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal cases. The adaptive hierarchical neural control scheme is also proposed to deal with the time-varying nature of loads. Further, adaptive neural control, which is based on the on-line updating of the weights and biases of the neural networks, is studied. Simulations provided on the faulted power systems with unknown loads suggest that the proposed adaptive hierarchical neural control schemes should be useful for practical power applications.

Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

PubMed Central

LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W.

2014-01-01

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

Feature reconstruction of LFP signals based on PLSR in the neural information decoding study.

PubMed

Yonghui Dong; Zhigang Shang; Mengmeng Li; Xinyu Liu; Hong Wan

2017-07-01

To solve the problems of Signal-to-Noise Ratio (SNR) and multicollinearity when the Local Field Potential (LFP) signals is used for the decoding of animal motion intention, a feature reconstruction of LFP signals based on partial least squares regression (PLSR) in the neural information decoding study is proposed in this paper. Firstly, the feature information of LFP coding band is extracted based on wavelet transform. Then the PLSR model is constructed by the extracted LFP coding features. According to the multicollinearity characteristics among the coding features, several latent variables which contribute greatly to the steering behavior are obtained, and the new LFP coding features are reconstructed. Finally, the K-Nearest Neighbor (KNN) method is used to classify the reconstructed coding features to verify the decoding performance. The results show that the proposed method can achieve the highest accuracy compared to the other three methods and the decoding effect of the proposed method is robust.

Cascaded neural networks for sequenced propagation estimation, multiuser detection, and adaptive radio resource control of third-generation wireless networks for multimedia services

NASA Astrophysics Data System (ADS)

Hortos, William S.

1999-03-01

A hybrid neural network approach is presented to estimate radio propagation characteristics and multiuser interference and to evaluate their combined impact on throughput, latency and information loss in third-generation (3G) wireless networks. The latter three performance parameters influence the quality of service (QoS) for multimedia services under consideration for 3G networks. These networks, based on a hierarchical architecture of overlaying macrocells on top of micro- and picocells, are planned to operate in mobile urban and indoor environments with service demands emanating from circuit-switched, packet-switched and satellite-based traffic sources. Candidate radio interfaces for these networks employ a form of wideband CDMA in 5-MHz and wider-bandwidth channels, with possible asynchronous operation of the mobile subscribers. The proposed neural network (NN) architecture allocates network resources to optimize QoS metrics. Parameters of the radio propagation channel are estimated, followed by control of an adaptive antenna array at the base station to minimize interference, and then joint multiuser detection is performed at the base station receiver. These adaptive processing stages are implemented as a sequence of NN techniques that provide their estimates as inputs to a final- stage Kohonen self-organizing feature map (SOFM). The SOFM optimizes the allocation of available network resources to satisfy QoS requirements for variable-rate voice, data and video services. As the first stage of the sequence, a modified feed-forward multilayer perceptron NN is trained on the pilot signals of the mobile subscribers to estimate the parameters of shadowing, multipath fading and delays on the uplinks. A recurrent NN (RNN) forms the second stage to control base stations' adaptive antenna arrays to minimize intra-cell interference. The third stage is based on a Hopfield NN (HNN), modified to detect multiple users on the uplink radio channels to mitigate multiaccess interference, control carrier-sense multiple-access (CSMA) protocols, and refine call handoff procedures. In the final stage, the Kohonen SOFM, operating in a hybrid continuous and discrete space, adaptively allocates the resources of antenna-based cell sectorization, activity monitoring, variable-rate coding, power control, handoff and caller admission to meet user demands for various multimedia services at minimum QoS levels. The performance of the NN cascade is evaluated through simulation of a candidate 3G wireless network using W-CDMA parameters in a small-cell environment. The simulated network consists of a representative number of cells. Mobile users with typical movement patterns are assumed. QoS requirements for different classes of multimedia services are considered. The proposed method is shown to provide relatively low probability of new call blocking and handoff dropping, while maintaining efficient use of the network's radio resources.

A neural circuit transforming temporal periodicity information into a rate-based representation in the mammalian auditory system.

PubMed

Dicke, Ulrike; Ewert, Stephan D; Dau, Torsten; Kollmeier, Birger

2007-01-01

Periodic amplitude modulations (AMs) of an acoustic stimulus are presumed to be encoded in temporal activity patterns of neurons in the cochlear nucleus. Physiological recordings indicate that this temporal AM code is transformed into a rate-based periodicity code along the ascending auditory pathway. The present study suggests a neural circuit for the transformation from the temporal to the rate-based code. Due to the neural connectivity of the circuit, bandpass shaped rate modulation transfer functions are obtained that correspond to recorded functions of inferior colliculus (IC) neurons. In contrast to previous modeling studies, the present circuit does not employ a continuously changing temporal parameter to obtain different best modulation frequencies (BMFs) of the IC bandpass units. Instead, different BMFs are yielded from varying the number of input units projecting onto different bandpass units. In order to investigate the compatibility of the neural circuit with a linear modulation filterbank analysis as proposed in psychophysical studies, complex stimuli such as tones modulated by the sum of two sinusoids, narrowband noise, and iterated rippled noise were processed by the model. The model accounts for the encoding of AM depth over a large dynamic range and for modulation frequency selective processing of complex sounds.

Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing

PubMed Central

Schoppe, Oliver; King, Andrew J.; Schnupp, Jan W.H.; Harper, Nicol S.

2016-01-01

Adaptation to stimulus statistics, such as the mean level and contrast of recently heard sounds, has been demonstrated at various levels of the auditory pathway. It allows the nervous system to operate over the wide range of intensities and contrasts found in the natural world. Yet current standard models of the response properties of auditory neurons do not incorporate such adaptation. Here we present a model of neural responses in the ferret auditory cortex (the IC Adaptation model), which takes into account adaptation to mean sound level at a lower level of processing: the inferior colliculus (IC). The model performs high-pass filtering with frequency-dependent time constants on the sound spectrogram, followed by half-wave rectification, and passes the output to a standard linear–nonlinear (LN) model. We find that the IC Adaptation model consistently predicts cortical responses better than the standard LN model for a range of synthetic and natural stimuli. The IC Adaptation model introduces no extra free parameters, so it improves predictions without sacrificing parsimony. Furthermore, the time constants of adaptation in the IC appear to be matched to the statistics of natural sounds, suggesting that neurons in the auditory midbrain predict the mean level of future sounds and adapt their responses appropriately. SIGNIFICANCE STATEMENT An ability to accurately predict how sensory neurons respond to novel stimuli is critical if we are to fully characterize their response properties. Attempts to model these responses have had a distinguished history, but it has proven difficult to improve their predictive power significantly beyond that of simple, mostly linear receptive field models. Here we show that auditory cortex receptive field models benefit from a nonlinear preprocessing stage that replicates known adaptation properties of the auditory midbrain. This improves their predictive power across a wide range of stimuli but keeps model complexity low as it introduces no new free parameters. Incorporating the adaptive coding properties of neurons will likely improve receptive field models in other sensory modalities too. PMID:26758822

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

PubMed

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers

NASA Astrophysics Data System (ADS)

Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.

1994-03-01

This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Neural Tuning to Numerosity Relates to Perceptual Tuning in 3-6-Year-Old Children.

PubMed

Kersey, Alyssa J; Cantlon, Jessica F

2017-01-18

Neural representations of approximate numerical value, or numerosity, have been observed in the intraparietal sulcus (IPS) in monkeys and humans, including children. Using functional magnetic resonance imaging, we show that children as young as 3-4 years old exhibit neural tuning to cardinal numerosities in the IPS and that their neural responses are accounted for by a model of numerosity coding that has been used to explain neural responses in the adult IPS. We also found that the sensitivity of children's neural tuning to number in the right IPS was comparable to their numerical discrimination sensitivity observed behaviorally, outside of the scanner. Children's neural tuning curves in the right IPS were significantly sharper than in the left IPS, indicating that numerical representations are more precise and mature more rapidly in the right hemisphere than in the left. Further, we show that children's perceptual sensitivity to numerosity can be predicted by the development of their neural sensitivity to numerosity. This research provides novel evidence of developmental continuity in the neural code underlying numerical representation and demonstrates that children's neural sensitivity to numerosity is related to their cognitive development. Here we test for the existence of neural tuning to numerosity in the developing brain in the youngest sample of children tested with fMRI to date. Although previous research shows evidence of numerical distance effects in the intraparietal sulcus of the developing brain, those effects could be explained by patterns of neural activity that do not represent neural tuning to numerosity. These data provide the first robust evidence that from as early as 3-4 years of age there is developmental continuity in how the intraparietal sulcus represents the values of numerosities. Moreover, the study goes beyond previous research by examining the relation between neural tuning and perceptual tuning in children. Copyright © 2017 the authors 0270-6474/17/370512-11$15.00/0.

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

PubMed Central

Pohlmeyer, Eric A.; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W.; Sanchez, Justin C.

2014-01-01

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

Direction-selective circuits shape noise to ensure a precise population code

PubMed Central

Zylberberg, Joel; Cafaro, Jon; Turner, Maxwell H

2016-01-01

Summary Neural responses are noisy, and circuit structure can correlate this noise across neurons. Theoretical studies show that noise correlations can have diverse effects on population coding, but these studies rarely explore stimulus dependence of noise correlations. Here, we show that noise correlations in responses of ON-OFF direction-selective retinal ganglion cells are strongly stimulus dependent and we uncover the circuit mechanisms producing this stimulus dependence. A population model based on these mechanistic studies shows that stimulus-dependent noise correlations improve the encoding of motion direction two-fold compared to independent noise. This work demonstrates a mechanism by which a neural circuit effectively shapes its signal and noise in concert, minimizing corruption of signal by noise. Finally, we generalize our findings beyond direction coding in the retina and show that stimulus-dependent correlations will generally enhance information coding in populations of diversely tuned neurons. PMID:26796691

Differential expression and emerging functions of non-coding RNAs in cold adaptation.

PubMed

Frigault, Jacques J; Morin, Mathieu D; Morin, Pier Jr

2017-01-01

Several species undergo substantial physiological and biochemical changes to confront the harsh conditions associated with winter. Small mammalian hibernators and cold-hardy insects are examples of natural models of cold adaptation that have been amply explored. While the molecular picture associated with cold adaptation has started to become clearer in recent years, notably through the use of high-throughput experimental approaches, the underlying cold-associated functions attributed to several non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), remain to be better characterized. Nevertheless, key pioneering work has provided clues on the likely relevance of these molecules in cold adaptation. With an emphasis on mammalian hibernation and insect cold hardiness, this work first reviews various molecular changes documented so far in these processes. The cascades leading to miRNA and lncRNA production as well as the mechanisms of action of these non-coding RNAs are subsequently described. Finally, we present examples of differentially expressed non-coding RNAs in models of cold adaptation and elaborate on the potential significance of this modulation with respect to low-temperature adaptation.

Age Differences in the Neuroelectric Adaptation to Meaningful Sounds

PubMed Central

Leung, Ada W. S.; He, Yu; Grady, Cheryl L.; Alain, Claude

2013-01-01

Much of what we know regarding the effect of stimulus repetition on neuroelectric adaptation comes from studies using artificially produced pure tones or harmonic complex sounds. Little is known about the neural processes associated with the representation of everyday sounds and how these may be affected by aging. In this study, we used real life, meaningful sounds presented at various azimuth positions and found that auditory evoked responses peaking at about 100 and 180 ms after sound onset decreased in amplitude with stimulus repetition. This neural adaptation was greater in young than in older adults and was more pronounced when the same sound was repeated at the same location. Moreover, the P2 waves showed differential patterns of domain-specific adaptation when location and identity was repeated among young adults. Background noise decreased ERP amplitudes and modulated the magnitude of repetition effects on both the N1 and P2 amplitude, and the effects were comparable in young and older adults. These findings reveal an age-related difference in the neural processes associated with adaptation to meaningful sounds, which may relate to older adults’ difficulty in ignoring task-irrelevant stimuli. PMID:23935900

Listening to Brain Microcircuits for Interfacing With External World—Progress in Wireless Implantable Microelectronic Neuroengineering Devices

PubMed Central

Nurmikko, Arto V.; Donoghue, John P.; Hochberg, Leigh R.; Patterson, William R.; Song, Yoon-Kyu; Bull, Christopher W.; Borton, David A.; Laiwalla, Farah; Park, Sunmee; Ming, Yin; Aceros, Juan

2011-01-01

Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature’s amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic “brain-interfaces” within the body, a point of special emphasis of this paper. PMID:21654935

Python scripting in the nengo simulator.

PubMed

Stewart, Terrence C; Tripp, Bryan; Eliasmith, Chris

2009-01-01

Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

Python Scripting in the Nengo Simulator

PubMed Central

Stewart, Terrence C.; Tripp, Bryan; Eliasmith, Chris

2008-01-01

Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models. PMID:19352442

Losing the rose tinted glasses: neural substrates of unbiased belief updating in depression

PubMed Central

Garrett, Neil; Sharot, Tali; Faulkner, Paul; Korn, Christoph W.; Roiser, Jonathan P.; Dolan, Raymond J.

2014-01-01

Recent evidence suggests that a state of good mental health is associated with biased processing of information that supports a positively skewed view of the future. Depression, on the other hand, is associated with unbiased processing of such information. Here, we use brain imaging in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals. Our results reveal that unbiased belief updating in depression is mediated by strong neural coding of estimation errors in response to both good news (in left inferior frontal gyrus and bilateral superior frontal gyrus) and bad news (in right inferior parietal lobule and right inferior frontal gyrus) regarding the future. In contrast, intact mental health was linked to a relatively attenuated neural coding of bad news about the future. These findings identify a neural substrate mediating the breakdown of biased updating in major depression disorder, which may be essential for mental health. PMID:25221492

Learning vector quantization neural networks improve accuracy of transcranial color-coded duplex sonography in detection of middle cerebral artery spasm--preliminary report.

PubMed

Swiercz, Miroslaw; Kochanowicz, Jan; Weigele, John; Hurst, Robert; Liebeskind, David S; Mariak, Zenon; Melhem, Elias R; Krejza, Jaroslaw

2008-01-01

To determine the performance of an artificial neural network in transcranial color-coded duplex sonography (TCCS) diagnosis of middle cerebral artery (MCA) spasm. TCCS was prospectively acquired within 2 h prior to routine cerebral angiography in 100 consecutive patients (54M:46F, median age 50 years). Angiographic MCA vasospasm was classified as mild (<25% of vessel caliber reduction), moderate (25-50%), or severe (>50%). A Learning Vector Quantization neural network classified MCA spasm based on TCCS peak-systolic, mean, and end-diastolic velocity data. During a four-class discrimination task, accurate classification by the network ranged from 64.9% to 72.3%, depending on the number of neurons in the Kohonen layer. Accurate classification of vasospasm ranged from 79.6% to 87.6%, with an accuracy of 84.7% to 92.1% for the detection of moderate-to-severe vasospasm. An artificial neural network may increase the accuracy of TCCS in diagnosis of MCA spasm.

Behavioral training promotes multiple adaptive processes following acute hearing loss.

PubMed

Keating, Peter; Rosenior-Patten, Onayomi; Dahmen, Johannes C; Bell, Olivia; King, Andrew J

2016-03-23

The brain possesses a remarkable capacity to compensate for changes in inputs resulting from a range of sensory impairments. Developmental studies of sound localization have shown that adaptation to asymmetric hearing loss can be achieved either by reinterpreting altered spatial cues or by relying more on those cues that remain intact. Adaptation to monaural deprivation in adulthood is also possible, but appears to lack such flexibility. Here we show, however, that appropriate behavioral training enables monaurally-deprived adult humans to exploit both of these adaptive processes. Moreover, cortical recordings in ferrets reared with asymmetric hearing loss suggest that these forms of plasticity have distinct neural substrates. An ability to adapt to asymmetric hearing loss using multiple adaptive processes is therefore shared by different species and may persist throughout the lifespan. This highlights the fundamental flexibility of neural systems, and may also point toward novel therapeutic strategies for treating sensory disorders.

The NASA F-15 Intelligent Flight Control Systems: Generation II

NASA Technical Reports Server (NTRS)

Buschbacher, Mark; Bosworth, John

2006-01-01

The Second Generation (Gen II) control system for the F-15 Intelligent Flight Control System (IFCS) program implements direct adaptive neural networks to demonstrate robust tolerance to faults and failures. The direct adaptive tracking controller integrates learning neural networks (NNs) with a dynamic inversion control law. The term direct adaptive is used because the error between the reference model and the aircraft response is being compensated or directly adapted to minimize error without regard to knowing the cause of the error. No parameter estimation is needed for this direct adaptive control system. In the Gen II design, the feedback errors are regulated with a proportional-plus-integral (PI) compensator. This basic compensator is augmented with an online NN that changes the system gains via an error-based adaptation law to improve aircraft performance at all times, including normal flight, system failures, mispredicted behavior, or changes in behavior resulting from damage.

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Effects of hypoxia on sympathetic neural control in humans

NASA Technical Reports Server (NTRS)

Smith, M. L.; Muenter, N. K.

2000-01-01

This special issue is principally focused on the time domain of the adaptive mechanisms of ventilatory responses to short-term, long-term and intermittent hypoxia. The purpose of this review is to summarize the limited literature on the sympathetic neural responses to sustained or intermittent hypoxia in humans and attempt to discern the time domain of these responses and potential adaptive processes that are evoked during short and long-term exposures to hypoxia.

Study on application of adaptive fuzzy control and neural network in the automatic leveling system

NASA Astrophysics Data System (ADS)

Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

2015-04-01

This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

PubMed Central

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

NASA Astrophysics Data System (ADS)

Mai, Huanhuan; Song, Gangbing; Liao, Xiaofeng

2013-01-01

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.

Was Wright Right? The Canonical Genetic Code is an Empirical Example of an Adaptive Peak in Nature; Deviant Genetic Codes Evolved Using Adaptive Bridges

PubMed Central

2010-01-01

The canonical genetic code is on a sub-optimal adaptive peak with respect to its ability to minimize errors, and is close to, but not quite, optimal. This is demonstrated by the near-total adjacency of synonymous codons, the similarity of adjacent codons, and comparisons of frequency of amino acid usage with number of codons in the code for each amino acid. As a rare empirical example of an adaptive peak in nature, it shows adaptive peaks are real, not merely theoretical. The evolution of deviant genetic codes illustrates how populations move from a lower to a higher adaptive peak. This is done by the use of “adaptive bridges,” neutral pathways that cross over maladaptive valleys by virtue of masking of the phenotypic expression of some maladaptive aspects in the genotype. This appears to be the general mechanism by which populations travel from one adaptive peak to another. There are multiple routes a population can follow to cross from one adaptive peak to another. These routes vary in the probability that they will be used, and this probability is determined by the number and nature of the mutations that happen along each of the routes. A modification of the depiction of adaptive landscapes showing genetic distances and probabilities of travel along their multiple possible routes would throw light on this important concept. PMID:20711776

Transformation of the neural code for tactile detection from thalamus to cortex.

PubMed

Vázquez, Yuriria; Salinas, Emilio; Romo, Ranulfo

2013-07-09

To understand how sensory-driven neural activity gives rise to perception, it is essential to characterize how various relay stations in the brain encode stimulus presence. Neurons in the ventral posterior lateral (VPL) nucleus of the somatosensory thalamus and in primary somatosensory cortex (S1) respond to vibrotactile stimulation with relatively slow modulations (∼100 ms) of their firing rate. In addition, faster modulations (∼10 ms) time-locked to the stimulus waveform are observed in both areas, but their contribution to stimulus detection is unknown. Furthermore, it is unclear whether VPL and S1 neurons encode stimulus presence with similar accuracy and via the same response features. To address these questions, we recorded single neurons while trained monkeys judged the presence or absence of a vibrotactile stimulus of variable amplitude, and their activity was analyzed with a unique decoding method that is sensitive to the time scale of the firing rate fluctuations. We found that the maximum detection accuracy of single neurons is similar in VPL and S1. However, VPL relies more heavily on fast rate modulations than S1, and as a consequence, the neural code in S1 is more tolerant: its performance degrades less when the readout method or the time scale of integration is suboptimal. Therefore, S1 neurons implement a more robust code, one less sensitive to the temporal integration window used to infer stimulus presence downstream. The differences between VPL and S1 responses signaling the appearance of a stimulus suggest a transformation of the neural code from thalamus to cortex.

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

Strauss, H.R.

This paper describes the code FEMHD, an adaptive finite element MHD code, which is applied in a number of different manners to model MHD behavior and edge plasma phenomena on a diverted tokamak. The code uses an unstructured triangular mesh in 2D and wedge shaped mesh elements in 3D. The code has been adapted to look at neutral and charged particle dynamics in the plasma scrape off region, and into a full MHD-particle code.

Impaired Tuning of Neural Ensembles and the Pathophysiology of Schizophrenia: A Translational and Computational Neuroscience Perspective.

PubMed

Krystal, John H; Anticevic, Alan; Yang, Genevieve J; Dragoi, George; Driesen, Naomi R; Wang, Xiao-Jing; Murray, John D

2017-05-15

The functional optimization of neural ensembles is central to human higher cognitive functions. When the functions through which neural activity is tuned fail to develop or break down, symptoms and cognitive impairments arise. This review considers ways in which disturbances in the balance of excitation and inhibition might develop and be expressed in cortical networks in association with schizophrenia. This presentation is framed within a developmental perspective that begins with disturbances in glutamate synaptic development in utero. It considers developmental correlates and consequences, including compensatory mechanisms that increase intrinsic excitability or reduce inhibitory tone. It also considers the possibility that these homeostatic increases in excitability have potential negative functional and structural consequences. These negative functional consequences of disinhibition may include reduced working memory-related cortical activity associated with the downslope of the "inverted-U" input-output curve, impaired spatial tuning of neural activity and impaired sparse coding of information, and deficits in the temporal tuning of neural activity and its implication for neural codes. The review concludes by considering the functional significance of noisy activity for neural network function. The presentation draws on computational neuroscience and pharmacologic and genetic studies in animals and humans, particularly those involving N-methyl-D-aspartate glutamate receptor antagonists, to illustrate principles of network regulation that give rise to features of neural dysfunction associated with schizophrenia. While this presentation focuses on schizophrenia, the general principles outlined in the review may have broad implications for considering disturbances in the regulation of neural ensembles in psychiatric disorders. Published by Elsevier Inc.

Down-regulation of the non-coding RNA H19 and its derived miR-675 is concomitant with up-regulation of insulin-like growth factor receptor type 1 during neural-like differentiation of human bone marrow mesenchymal stem cells.

PubMed

Farzi-Molan, Asghar; Babashah, Sadegh; Bakhshinejad, Babak; Atashi, Amir; Fakhr Taha, Masoumeh

2018-03-07

The differentiation of human bone marrow mesenchymal stem cells (BMSCs) into specific lineages offers new opportunities to use the therapeutic efficiency of these pluripotent cells in regenerative medicine. Multiple lines of evidence have revealed that non-coding RNAs play major roles in the differentiation of BMSCs into neural cells. Here, we applied a cocktail of neural inducing factors (NIFs) to differentiate BMSCs into neural-like cells. Our data demonstrated that during neurogenic induction, BMSCs obtained a neuron-like morphology. Also, the results of gene expression analysis by qRT-PCR showed progressively increasing expression levels of neuron-specific enolase (NSE) as well as microtubule-associated protein 2 (MAP-2) and immunocytochemical staining detected the expression of these neuron-specific markers along differentiated BMSC bodies and cytoplasmic processes, confirming the differentiation of BMSCs into neuronal lineages. We also compared differences in the expression levels of the long non-coding RNA (lncRNA) H19 and H19-derived miR-675 between undifferentiated and neurally differentiated BMSCs and found that during neural differentiation down-regulation of the lncRNA H19/miR-675 axis is concomitant with up-regulation of insulin-like growth factor type-1 (IGF-1R), a well-established target of miR-675 involved in neurogenesis. The findings of the current study provide support for the hypothesis that miR-675 may confer functionality to H19, suggesting a key role for this miRNA in the neural differentiation of BSMCs. However, further investigation is required to gain deeper insights into the biological roles of this miRNA in the complex process of neurogenesis. © 2018 International Federation for Cell Biology.

Losing the music: aging affects the perception and subcortical neural representation of musical harmony.

PubMed

Bones, Oliver; Plack, Christopher J

2015-03-04

When two musical notes with simple frequency ratios are played simultaneously, the resulting musical chord is pleasing and evokes a sense of resolution or "consonance". Complex frequency ratios, on the other hand, evoke feelings of tension or "dissonance". Consonance and dissonance form the basis of harmony, a central component of Western music. In earlier work, we provided evidence that consonance perception is based on neural temporal coding in the brainstem (Bones et al., 2014). Here, we show that for listeners with clinically normal hearing, aging is associated with a decline in both the perceptual distinction and the distinctiveness of the neural representations of different categories of two-note chords. Compared with younger listeners, older listeners rated consonant chords as less pleasant and dissonant chords as more pleasant. Older listeners also had less distinct neural representations of consonant and dissonant chords as measured using a Neural Consonance Index derived from the electrophysiological "frequency-following response." The results withstood a control for the effect of age on general affect, suggesting that different mechanisms are responsible for the perceived pleasantness of musical chords and affective voices and that, for listeners with clinically normal hearing, age-related differences in consonance perception are likely to be related to differences in neural temporal coding. Copyright © 2015 Bones and Plack.

Losing the Music: Aging Affects the Perception and Subcortical Neural Representation of Musical Harmony

PubMed Central

Plack, Christopher J.

2015-01-01

When two musical notes with simple frequency ratios are played simultaneously, the resulting musical chord is pleasing and evokes a sense of resolution or “consonance”. Complex frequency ratios, on the other hand, evoke feelings of tension or “dissonance”. Consonance and dissonance form the basis of harmony, a central component of Western music. In earlier work, we provided evidence that consonance perception is based on neural temporal coding in the brainstem (Bones et al., 2014). Here, we show that for listeners with clinically normal hearing, aging is associated with a decline in both the perceptual distinction and the distinctiveness of the neural representations of different categories of two-note chords. Compared with younger listeners, older listeners rated consonant chords as less pleasant and dissonant chords as more pleasant. Older listeners also had less distinct neural representations of consonant and dissonant chords as measured using a Neural Consonance Index derived from the electrophysiological “frequency-following response.” The results withstood a control for the effect of age on general affect, suggesting that different mechanisms are responsible for the perceived pleasantness of musical chords and affective voices and that, for listeners with clinically normal hearing, age-related differences in consonance perception are likely to be related to differences in neural temporal coding. PMID:25740534

Homeostatic Scaling of Excitability in Recurrent Neural Networks

PubMed Central

Remme, Michiel W. H.; Wadman, Wytse J.

2012-01-01

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity. PMID:22570604

Neural decoding of collective wisdom with multi-brain computing.

PubMed

Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

2012-01-02

Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally, our methods highlight the potential of multi-brain computing as a technique to rapidly and in parallel gather increased information about the environment as well as to access collective perceptual/cognitive choices and mental states. Copyright © 2011 Elsevier Inc. All rights reserved.

Shades of grey; Assessing the contribution of the magno- and parvocellular systems to neural processing of the retinal input in the human visual system from the influence of neural population size and its discharge activity on the VEP.

PubMed

Marcar, Valentine L; Baselgia, Silvana; Lüthi-Eisenegger, Barbara; Jäncke, Lutz

2018-03-01

Retinal input processing in the human visual system involves a phasic and tonic neural response. We investigated the role of the magno- and parvocellular systems by comparing the influence of the active neural population size and its discharge activity on the amplitude and latency of four VEP components. We recorded the scalp electric potential of 20 human volunteers viewing a series of dartboard images presented as a pattern reversing and pattern on-/offset stimulus. These patterns were designed to vary both neural population size coding the temporal- and spatial luminance contrast property and the discharge activity of the population involved in a systematic manner. When the VEP amplitude reflected the size of the neural population coding the temporal luminance contrast property of the image, the influence of luminance contrast followed the contrast response function of the parvocellular system. When the VEP amplitude reflected the size of the neural population responding to the spatial luminance contrast property the image, the influence of luminance contrast followed the contrast response function of the magnocellular system. The latencies of the VEP components examined exhibited the same behavior across our stimulus series. This investigation demonstrates the complex interplay of the magno- and parvocellular systems on the neural response as captured by the VEP. It also demonstrates a linear relationship between stimulus property, neural response, and the VEP and reveals the importance of feedback projections in modulating the ongoing neural response. In doing so, it corroborates the conclusions of our previous study.

Statistical context shapes stimulus-specific adaptation in human auditory cortex

PubMed Central

Henry, Molly J.; Fromboluti, Elisa Kim; McAuley, J. Devin

2015-01-01

Stimulus-specific adaptation is the phenomenon whereby neural response magnitude decreases with repeated stimulation. Inconsistencies between recent nonhuman animal recordings and computational modeling suggest dynamic influences on stimulus-specific adaptation. The present human electroencephalography (EEG) study investigates the potential role of statistical context in dynamically modulating stimulus-specific adaptation by examining the auditory cortex-generated N1 and P2 components. As in previous studies of stimulus-specific adaptation, listeners were presented with oddball sequences in which the presentation of a repeated tone was infrequently interrupted by rare spectral changes taking on three different magnitudes. Critically, the statistical context varied with respect to the probability of small versus large spectral changes within oddball sequences (half of the time a small change was most probable; in the other half a large change was most probable). We observed larger N1 and P2 amplitudes (i.e., release from adaptation) for all spectral changes in the small-change compared with the large-change statistical context. The increase in response magnitude also held for responses to tones presented with high probability, indicating that statistical adaptation can overrule stimulus probability per se in its influence on neural responses. Computational modeling showed that the degree of coadaptation in auditory cortex changed depending on the statistical context, which in turn affected stimulus-specific adaptation. Thus the present data demonstrate that stimulus-specific adaptation in human auditory cortex critically depends on statistical context. Finally, the present results challenge the implicit assumption of stationarity of neural response magnitudes that governs the practice of isolating established deviant-detection responses such as the mismatch negativity. PMID:25652920

Coding of visual object features and feature conjunctions in the human brain.

PubMed

Martinovic, Jasna; Gruber, Thomas; Müller, Matthias M

2008-01-01

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object's features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process--while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200-400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

PubMed

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

PubMed

Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M

2018-03-01

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

NASA Technical Reports Server (NTRS)

Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

2006-01-01

The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

Quality Scalability Aware Watermarking for Visual Content.

PubMed

Bhowmik, Deepayan; Abhayaratne, Charith

2016-11-01

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.

Resistance Training: Physiological Responses and Adaptations (Part 3 of 4).

ERIC Educational Resources Information Center

Fleck, Steven J.; Kraemer, William J.

1988-01-01

The physiological responses and adaptations which occur as a result of resistance training, such as cardiovascular responses, serum lipid count, body composition, and neural adaptations are discussed. Changes in the endocrine system are also described. (JL)

Cognitive and Neural Sciences Division, 1991 Programs.

ERIC Educational Resources Information Center

Vaughan, Willard S., Ed.

This report documents research and development performed under the sponsorship of the Cognitive and Neural Sciences Division of the Office of Naval Research in fiscal year 1991. It provides abstracts (title, principal investigator, project code, objective, approach, progress, and related reports) of projects of three program divisions (cognitive…

Cognitive and Neural Sciences Division, 1988 Programs.

ERIC Educational Resources Information Center

Vaughan, Willard S., Ed.

The research and development efforts performed by principal investigators under sponsorship of the Office of Naval Research Cognitive and Neural Sciences Division during 1988 are documented. The title, name and affiliation of the principal investigator, project code, contract number, current end date, technical objective, approach, and progress of…

Subsonic Aircraft With Regression and Neural-Network Approximators Designed

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2004-01-01

At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.

An Adaptive Critic Approach to Reference Model Adaptation

NASA Technical Reports Server (NTRS)

Krishnakumar, K.; Limes, G.; Gundy-Burlet, K.; Bryant, D.

2003-01-01

Neural networks have been successfully used for implementing control architectures for different applications. In this work, we examine a neural network augmented adaptive critic as a Level 2 intelligent controller for a C- 17 aircraft. This intelligent control architecture utilizes an adaptive critic to tune the parameters of a reference model, which is then used to define the angular rate command for a Level 1 intelligent controller. The present architecture is implemented on a high-fidelity non-linear model of a C-17 aircraft. The goal of this research is to improve the performance of the C-17 under degraded conditions such as control failures and battle damage. Pilot ratings using a motion based simulation facility are included in this paper. The benefits of using an adaptive critic are documented using time response comparisons for severe damage situations.

Adaptive regularization network based neural modeling paradigm for nonlinear adaptive estimation of cerebral evoked potentials.

PubMed

Zhang, Jian-Hua; Böhme, Johann F

2007-11-01

In this paper we report an adaptive regularization network (ARN) approach to realizing fast blind separation of cerebral evoked potentials (EPs) from background electroencephalogram (EEG) activity with no need to make any explicit assumption on the statistical (or deterministic) signal model. The ARNs are proposed to construct nonlinear EEG and EP signal models. A novel adaptive regularization training (ART) algorithm is proposed to improve the generalization performance of the ARN. Two adaptive neural modeling methods based on the ARN are developed and their implementation and performance analysis are also presented. The computer experiments using simulated and measured visual evoked potential (VEP) data have shown that the proposed ARN modeling paradigm yields computationally efficient and more accurate VEP signal estimation owing to its intrinsic model-free and nonlinear processing characteristics.

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Zhang, Huaguang; Lin, Chong

2016-01-01

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.

Star adaptation for two-algorithms used on serial computers

NASA Technical Reports Server (NTRS)

Howser, L. M.; Lambiotte, J. J., Jr.

1974-01-01

Two representative algorithms used on a serial computer and presently executed on the Control Data Corporation 6000 computer were adapted to execute efficiently on the Control Data STAR-100 computer. Gaussian elimination for the solution of simultaneous linear equations and the Gauss-Legendre quadrature formula for the approximation of an integral are the two algorithms discussed. A description is given of how the programs were adapted for STAR and why these adaptations were necessary to obtain an efficient STAR program. Some points to consider when adapting an algorithm for STAR are discussed. Program listings of the 6000 version coded in 6000 FORTRAN, the adapted STAR version coded in 6000 FORTRAN, and the STAR version coded in STAR FORTRAN are presented in the appendices.

Adaptive format conversion for scalable video coding

NASA Astrophysics Data System (ADS)

Wan, Wade K.; Lim, Jae S.

2001-12-01

The enhancement layer in many scalable coding algorithms is composed of residual coding information. There is another type of information that can be transmitted instead of (or in addition to) residual coding. Since the encoder has access to the original sequence, it can utilize adaptive format conversion (AFC) to generate the enhancement layer and transmit the different format conversion methods as enhancement data. This paper investigates the use of adaptive format conversion information as enhancement data in scalable video coding. Experimental results are shown for a wide range of base layer qualities and enhancement bitrates to determine when AFC can improve video scalability. Since the parameters needed for AFC are small compared to residual coding, AFC can provide video scalability at low enhancement layer bitrates that are not possible with residual coding. In addition, AFC can also be used in addition to residual coding to improve video scalability at higher enhancement layer bitrates. Adaptive format conversion has not been studied in detail, but many scalable applications may benefit from it. An example of an application that AFC is well-suited for is the migration path for digital television where AFC can provide immediate video scalability as well as assist future migrations.

The receptive field is dead. Long live the receptive field?

PubMed Central

Fairhall, Adrienne

2014-01-01

Advances in experimental techniques, including behavioral paradigms using rich stimuli under closed loop conditions and the interfacing of neural systems with external inputs and outputs, reveal complex dynamics in the neural code and require a revisiting of standard concepts of representation. High-throughput recording and imaging methods along with the ability to observe and control neuronal subpopulations allow increasingly detailed access to the neural circuitry that subserves these representations and the computations they support. How do we harness theory to build biologically grounded models of complex neural function? PMID:24618227

Flight Test Comparison of Different Adaptive Augmentations for Fault Tolerant Control Laws for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Hanson, Curtis E.; Lee, James A.; Kaneshige, John T.

2009-01-01

This report describes the improvements and enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research is a follow-on effort to flight tests performed on the NASA F-15 aircraft as part of the Intelligent Flight Control System research effort. Previous flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures.

Watching the brain recalibrate: Neural correlates of renormalization during face adaptation.

PubMed

Kloth, Nadine; Rhodes, Gillian; Schweinberger, Stefan R

2017-07-15

The face perception system flexibly adjusts its neural responses to current face exposure, inducing aftereffects in the perception of subsequent faces. For instance, adaptation to expanded faces makes undistorted faces appear compressed, and adaptation to compressed faces makes undistorted faces appear expanded. Such distortion aftereffects have been proposed to result from renormalization, in which the visual system constantly updates a prototype according to the adaptors' characteristics and evaluates subsequent faces relative to that. However, although consequences of adaptation are easily observed in behavioral aftereffects, it has proven difficult to observe renormalization during adaptation itself. Here we directly measured brain responses during adaptation to establish a neural correlate of renormalization. Given that the face-evoked occipito-temporal P2 event-related brain potential has been found to increase with face prototypicality, we reasoned that the adaptor-elicited P2 could serve as an electrophysiological indicator for renormalization. Participants adapted to sequences of four distorted (compressed or expanded) or undistorted faces, followed by a slightly distorted test face, which they had to classify as undistorted or distorted. We analysed ERPs evoked by each of the adaptors and found that P2 (but not N170) amplitudes evoked by consecutive adaptor faces exhibited an electrophysiological pattern of renormalization during adaptation to distorted faces: P2 amplitudes evoked by both compressed and expanded adaptors significantly increased towards asymptotic levels as adaptation proceeded. P2 amplitudes were smallest for the first adaptor, significantly larger for the second, and yet larger for the third adaptor. We conclude that the sensitivity of the occipito-temporal P2 to the perceived deviation of a face from the norm makes this component an excellent tool to study adaptation-induced renormalization. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Body size and shape misperception and visual adaptation: An overview of an emerging research paradigm.

PubMed

Challinor, Kirsten L; Mond, Jonathan; Stephen, Ian D; Mitchison, Deborah; Stevenson, Richard J; Hay, Phillipa; Brooks, Kevin R

2017-12-01

Although body size and shape misperception (BSSM) is a common feature of anorexia nervosa, bulimia nervosa and muscle dysmorphia, little is known about its underlying neural mechanisms. Recently, a new approach has emerged, based on the long-established non-invasive technique of perceptual adaptation, which allows for inferences about the structure of the neural apparatus responsible for alterations in visual appearance. Here, we describe several recent experimental examples of BSSM, wherein exposure to "extreme" body stimuli causes visual aftereffects of biased perception. The implications of these studies for our understanding of the neural and cognitive representation of human bodies, along with their implications for clinical practice are discussed.