Modulation of steady state functional connectivity in the default mode and working memory networks by cognitive load.

PubMed

Newton, Allen T; Morgan, Victoria L; Rogers, Baxter P; Gore, John C

2011-10-01

Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady-state verbal identity N-back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. To test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks. Copyright © 2010 Wiley-Liss, Inc.

Efficiently modeling neural networks on massively parallel computers

NASA Technical Reports Server (NTRS)

Farber, Robert M.

1993-01-01

Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks.

Memory Compression Techniques for Network Address Management in MPI

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

Guo, Yanfei; Archer, Charles J.; Blocksome, Michael

MPI allows applications to treat processes as a logical collection of integer ranks for each MPI communicator, while internally translating these logical ranks into actual network addresses. In current MPI implementations the management and lookup of such network addresses use memory sizes that are proportional to the number of processes in each communicator. In this paper, we propose a new mechanism, called AV-Rankmap, for managing such translation. AV-Rankmap takes advantage of logical patterns in rank-address mapping that most applications naturally tend to have, and it exploits the fact that some parts of network address structures are naturally more performance criticalmore » than others. It uses this information to compress the memory used for network address management. We demonstrate that AV-Rankmap can achieve performance similar to or better than that of other MPI implementations while using significantly less memory.« less

Sharp wave/ripple network oscillations and learning-associated hippocampal maps.

PubMed

Csicsvari, Jozsef; Dupret, David

2014-02-05

Sharp wave/ripple (SWR, 150-250 Hz) hippocampal events have long been postulated to be involved in memory consolidation. However, more recent work has investigated SWRs that occur during active waking behaviour: findings that suggest that SWRs may also play a role in cell assembly strengthening or spatial working memory. Do such theories of SWR function apply to animal learning? This review discusses how general theories linking SWRs to memory-related function may explain circuit mechanisms related to rodent spatial learning and to the associated stabilization of new cognitive maps.

Changes in Neural Connectivity and Memory Following a Yoga Intervention for Older Adults: A Pilot Study.

PubMed

Eyre, Harris A; Acevedo, Bianca; Yang, Hongyu; Siddarth, Prabha; Van Dyk, Kathleen; Ercoli, Linda; Leaver, Amber M; Cyr, Natalie St; Narr, Katherine; Baune, Bernhard T; Khalsa, Dharma S; Lavretsky, Helen

2016-01-01

No study has explored the effect of yoga on cognitive decline and resting-state functional connectivity. This study explored the relationship between performance on memory tests and resting-state functional connectivity before and after a yoga intervention versus active control for subjects with mild cognitive impairment (MCI). Participants ( ≥ 55 y) with MCI were randomized to receive a yoga intervention or active "gold-standard" control (i.e., memory enhancement training (MET)) for 12 weeks. Resting-state functional magnetic resonance imaging was used to map correlations between brain networks and memory performance changes over time. Default mode networks (DMN), language and superior parietal networks were chosen as networks of interest to analyze the association with changes in verbal and visuospatial memory performance. Fourteen yoga and 11 MET participants completed the study. The yoga group demonstrated a statistically significant improvement in depression and visuospatial memory. We observed improved verbal memory performance correlated with increased connectivity between the DMN and frontal medial cortex, pregenual anterior cingulate cortex, right middle frontal cortex, posterior cingulate cortex, and left lateral occipital cortex. Improved verbal memory performance positively correlated with increased connectivity between the language processing network and the left inferior frontal gyrus. Improved visuospatial memory performance correlated inversely with connectivity between the superior parietal network and the medial parietal cortex. Yoga may be as effective as MET in improving functional connectivity in relation to verbal memory performance. These findings should be confirmed in larger prospective studies.

Stochastic associative memory

NASA Astrophysics Data System (ADS)

Baumann, Erwin W.; Williams, David L.

1993-08-01

Artificial neural networks capable of learning and recalling stochastic associations between non-deterministic quantities have received relatively little attention to date. One potential application of such stochastic associative networks is the generation of sensory 'expectations' based on arbitrary subsets of sensor inputs to support anticipatory and investigate behavior in sensor-based robots. Another application of this type of associative memory is the prediction of how a scene will look in one spectral band, including noise, based upon its appearance in several other wavebands. This paper describes a semi-supervised neural network architecture composed of self-organizing maps associated through stochastic inter-layer connections. This 'Stochastic Associative Memory' (SAM) can learn and recall non-deterministic associations between multi-dimensional probability density functions. The stochastic nature of the network also enables it to represent noise distributions that are inherent in any true sensing process. The SAM architecture, training process, and initial application to sensor image prediction are described. Relationships to Fuzzy Associative Memory (FAM) are discussed.

Rapid consolidation of new knowledge in adulthood via fast mapping.

PubMed

Coutanche, Marc N; Thompson-Schill, Sharon L

2015-09-01

Rapid word learning, where words are 'fast mapped' onto new concepts, may help build vocabulary during childhood. Recent evidence has suggested that fast mapping might help to rapidly integrate information into memory networks of the adult neocortex. The neural basis for this learning by fast mapping determines key properties of the learned information. Copyright © 2015 Elsevier Ltd. All rights reserved.

Topological Schemas of Memory Spaces.

PubMed

Babichev, Andrey; Dabaghian, Yuri A

2018-01-01

Hippocampal cognitive map-a neuronal representation of the spatial environment-is widely discussed in the computational neuroscience literature for decades. However, more recent studies point out that hippocampus plays a major role in producing yet another cognitive framework-the memory space-that incorporates not only spatial, but also non-spatial memories. Unlike the cognitive maps, the memory spaces, broadly understood as "networks of interconnections among the representations of events," have not yet been studied from a theoretical perspective. Here we propose a mathematical approach that allows modeling memory spaces constructively, as epiphenomena of neuronal spiking activity and thus to interlink several important notions of cognitive neurophysiology. First, we suggest that memory spaces have a topological nature-a hypothesis that allows treating both spatial and non-spatial aspects of hippocampal function on equal footing. We then model the hippocampal memory spaces in different environments and demonstrate that the resulting constructions naturally incorporate the corresponding cognitive maps and provide a wider context for interpreting spatial information. Lastly, we propose a formal description of the memory consolidation process that connects memory spaces to the Morris' cognitive schemas-heuristic representations of the acquired memories, used to explain the dynamics of learning and memory consolidation in a given environment. The proposed approach allows evaluating these constructs as the most compact representations of the memory space's structure.

Top-down and bottom-up attention-to-memory: mapping functional connectivity in two distinct networks that underlie cued and uncued recognition memory.

PubMed

Burianová, Hana; Ciaramelli, Elisa; Grady, Cheryl L; Moscovitch, Morris

2012-11-15

The objective of this study was to examine the functional connectivity of brain regions active during cued and uncued recognition memory to test the idea that distinct networks would underlie these memory processes, as predicted by the attention-to-memory (AtoM) hypothesis. The AtoM hypothesis suggests that dorsal parietal cortex (DPC) allocates effortful top-down attention to memory retrieval during cued retrieval, whereas ventral parietal cortex (VPC) mediates spontaneous bottom-up capture of attention by memory during uncued retrieval. To identify networks associated with these two processes, we conducted a functional connectivity analysis of a left DPC and a left VPC region, both identified by a previous analysis of task-related regional activations. We hypothesized that the two parietal regions would be functionally connected with distinct neural networks, reflecting their engagement in the differential mnemonic processes. We found two spatially dissociated networks that overlapped only in the precuneus. During cued trials, DPC was functionally connected with dorsal attention areas, including the superior parietal lobules, right precuneus, and premotor cortex, as well as relevant memory areas, such as the left hippocampus and the middle frontal gyri. During uncued trials, VPC was functionally connected with ventral attention areas, including the supramarginal gyrus, cuneus, and right fusiform gyrus, as well as the parahippocampal gyrus. In addition, activity in the DPC network was associated with faster response times for cued retrieval. This is the first study to show a dissociation of the functional connectivity of posterior parietal regions during episodic memory retrieval, characterized by a top-down AtoM network involving DPC and a bottom-up AtoM network involving VPC. Copyright © 2012 Elsevier Inc. All rights reserved.

Changes in Neural Connectivity and Memory Following a Yoga Intervention for Older Adults: A Pilot Study

PubMed Central

Eyre, Harris A.; Acevedo, Bianca; Yang, Hongyu; Siddarth, Prabha; Van Dyk, Kathleen; Ercoli, Linda; Leaver, Amber M.; Cyr, Natalie St.; Narr, Katherine; Baune, Bernhard T.; Khalsa, Dharma S.; Lavretsky, Helen

2016-01-01

Background: No study has explored the effect of yoga on cognitive decline and resting-state functional connectivity. Objectives: This study explored the relationship between performance on memory tests and resting-state functional connectivity before and after a yoga intervention versus active control for subjects with mild cognitive impairment (MCI). Methods: Participants ( ≥ 55 y) with MCI were randomized to receive a yoga intervention or active “gold-standard” control (i.e., memory enhancement training (MET)) for 12 weeks. Resting-state functional magnetic resonance imaging was used to map correlations between brain networks and memory performance changes over time. Default mode networks (DMN), language and superior parietal networks were chosen as networks of interest to analyze the association with changes in verbal and visuospatial memory performance. Results: Fourteen yoga and 11 MET participants completed the study. The yoga group demonstrated a statistically significant improvement in depression and visuospatial memory. We observed improved verbal memory performance correlated with increased connectivity between the DMN and frontal medial cortex, pregenual anterior cingulate cortex, right middle frontal cortex, posterior cingulate cortex, and left lateral occipital cortex. Improved verbal memory performance positively correlated with increased connectivity between the language processing network and the left inferior frontal gyrus. Improved visuospatial memory performance correlated inversely with connectivity between the superior parietal network and the medial parietal cortex. Conclusion:Yoga may be as effective as MET in improving functional connectivity in relation to verbal memory performance. These findings should be confirmed in larger prospective studies. PMID:27060939

PIYAS-proceeding to intelligent service oriented memory allocation for flash based data centric sensor devices in wireless sensor networks.

PubMed

Rizvi, Sanam Shahla; Chung, Tae-Sun

2010-01-01

Flash memory has become a more widespread storage medium for modern wireless devices because of its effective characteristics like non-volatility, small size, light weight, fast access speed, shock resistance, high reliability and low power consumption. Sensor nodes are highly resource constrained in terms of limited processing speed, runtime memory, persistent storage, communication bandwidth and finite energy. Therefore, for wireless sensor networks supporting sense, store, merge and send schemes, an efficient and reliable file system is highly required with consideration of sensor node constraints. In this paper, we propose a novel log structured external NAND flash memory based file system, called Proceeding to Intelligent service oriented memorY Allocation for flash based data centric Sensor devices in wireless sensor networks (PIYAS). This is the extended version of our previously proposed PIYA [1]. The main goals of the PIYAS scheme are to achieve instant mounting and reduced SRAM space by keeping memory mapping information to a very low size of and to provide high query response throughput by allocation of memory to the sensor data by network business rules. The scheme intelligently samples and stores the raw data and provides high in-network data availability by keeping the aggregate data for a longer period of time than any other scheme has done before. We propose effective garbage collection and wear-leveling schemes as well. The experimental results show that PIYAS is an optimized memory management scheme allowing high performance for wireless sensor networks.

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.

Stability of whole brain and regional network topology within and between resting and cognitive states.

PubMed

Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

2013-01-01

Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Response control networks are selectively modulated by attention to rare events and memory load regardless of the need for inhibition.

PubMed

Wijeakumar, Sobanawartiny; Magnotta, Vincent A; Buss, Aaron T; Ambrose, Joseph P; Wifall, Timothy A; Hazeltine, Eliot; Spencer, John P

2015-10-15

Recent evidence has sparked debate about the neural bases of response selection and inhibition. In the current study, we employed two reactive inhibition tasks, the Go/Nogo (GnG) and Simon tasks, to examine questions central to these debates. First, we investigated whether a fronto-cortical-striatal system was sensitive to the need for inhibition per se or the presentation of infrequent stimuli, by manipulating the proportion of trials that do not require inhibition (Go/Compatible trials) relative to trials that require inhibition (Nogo/Incompatible trials). A cortico-subcortical network composed of insula, putamen, and thalamus showed greater activation on salient and infrequent events, regardless of the need for inhibition. Thus, consistent with recent findings, key parts of the fronto-cortical-striatal system are engaged by salient events and do not appear to play a selective role in response inhibition. Second, we examined how the fronto-cortical-striatal system is modulated by working memory demands by varying the number of stimulus-response (SR) mappings. Right inferior parietal lobule showed decreasing activation as the number of SR mappings increased, suggesting that a form of associative memory - rather than working memory - might underlie performance in these tasks. A broad motor planning and control network showed similar trends that were also modulated by the number of motor responses required in each task. Finally, bilateral lingual gyri were more robustly engaged in the Simon task, consistent with the role of this area in shifts of visuo-spatial attention. The current study sheds light on how the fronto-cortical-striatal network is selectively engaged in reactive control tasks and how control is modulated by manipulations of attention and memory load. Copyright © 2015 Elsevier Inc. All rights reserved.

Gene networks associated with conditional fear in mice identified using a systems genetics approach

PubMed Central

2011-01-01

Background Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. Results A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. Conclusion Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior. PMID:21410935

Coherence time of over a second in a telecom-compatible quantum memory storage material

NASA Astrophysics Data System (ADS)

Rančić, Miloš; Hedges, Morgan P.; Ahlefeldt, Rose L.; Sellars, Matthew J.

2018-01-01

Quantum memories for light will be essential elements in future long-range quantum communication networks. These memories operate by reversibly mapping the quantum state of light onto the quantum transitions of a material system. For networks, the quantum coherence times of these transitions must be long compared to the network transmission times, approximately 100 ms for a global communication network. Due to a lack of a suitable storage material, a quantum memory that operates in the 1,550 nm optical fibre communication band with a storage time greater than 1 μs has not been demonstrated. Here we describe the spin dynamics of 167Er3+: Y2SiO5 in a high magnetic field and demonstrate that this material has the characteristics for a practical quantum memory in the 1,550 nm communication band. We observe a hyperfine coherence time of 1.3 s. We also demonstrate efficient spin pumping of the entire ensemble into a single hyperfine state, a requirement for broadband spin-wave storage. With an absorption of 70 dB cm-1 at 1,538 nm and Λ transitions enabling spin-wave storage, this material is the first candidate identified for an efficient, broadband quantum memory at telecommunication wavelengths.

Mittag-Leffler synchronization of delayed fractional-order bidirectional associative memory neural networks with discontinuous activations: state feedback control and impulsive control schemes.

PubMed

Ding, Xiaoshuai; Cao, Jinde; Zhao, Xuan; Alsaadi, Fuad E

2017-08-01

This paper is concerned with the drive-response synchronization for a class of fractional-order bidirectional associative memory neural networks with time delays, as well as in the presence of discontinuous activation functions. The global existence of solution under the framework of Filippov for such networks is firstly obtained based on the fixed-point theorem for condensing map. Then the state feedback and impulsive controllers are, respectively, designed to ensure the Mittag-Leffler synchronization of these neural networks and two new synchronization criteria are obtained, which are expressed in terms of a fractional comparison principle and Razumikhin techniques. Numerical simulations are presented to validate the proposed methodologies.

Richness of information about novel words influences how episodic and semantic memory networks interact during lexicalization.

PubMed

Takashima, Atsuko; Bakker, Iske; van Hell, Janet G; Janzen, Gabriele; McQueen, James M

2014-01-01

The complementary learning systems account of declarative memory suggests two distinct memory networks, a fast-mapping, episodic system involving the hippocampus, and a slower semantic memory system distributed across the neocortex in which new information is gradually integrated with existing representations. In this study, we investigated the extent to which these two networks are involved in the integration of novel words into the lexicon after extensive learning, and how the involvement of these networks changes after 24h. In particular, we explored whether having richer information at encoding influences the lexicalization trajectory. We trained participants with two sets of novel words, one where exposure was only to the words' phonological forms (the form-only condition), and one where pictures of unfamiliar objects were associated with the words' phonological forms (the picture-associated condition). A behavioral measure of lexical competition (indexing lexicalization) indicated stronger competition effects for the form-only words. Imaging (fMRI) results revealed greater involvement of phonological lexical processing areas immediately after training in the form-only condition, suggesting that tight connections were formed between novel words and existing lexical entries already at encoding. Retrieval of picture-associated novel words involved the episodic/hippocampal memory system more extensively. Although lexicalization was weaker in the picture-associated condition, overall memory strength was greater when tested after a 24hour delay, probably due to the availability of both episodic and lexical memory networks to aid retrieval. It appears that, during lexicalization of a novel word, the relative involvement of different memory networks differs according to the richness of the information about that word available at encoding. © 2013.

Contention Modeling for Multithreaded Distributed Shared Memory Machines: The Cray XMT

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

Secchi, Simone; Tumeo, Antonino; Villa, Oreste

Distributed Shared Memory (DSM) machines are a wide class of multi-processor computing systems where a large virtually-shared address space is mapped on a network of physically distributed memories. High memory latency and network contention are two of the main factors that limit performance scaling of such architectures. Modern high-performance computing DSM systems have evolved toward exploitation of massive hardware multi-threading and fine-grained memory hashing to tolerate irregular latencies, avoid network hot-spots and enable high scaling. In order to model the performance of such large-scale machines, parallel simulation has been proved to be a promising approach to achieve good accuracy inmore » reasonable times. One of the most critical factors in solving the simulation speed-accuracy trade-off is network modeling. The Cray XMT is a massively multi-threaded supercomputing architecture that belongs to the DSM class, since it implements a globally-shared address space abstraction on top of a physically distributed memory substrate. In this paper, we discuss the development of a contention-aware network model intended to be integrated in a full-system XMT simulator. We start by measuring the effects of network contention in a 128-processor XMT machine and then investigate the trade-off that exists between simulation accuracy and speed, by comparing three network models which operate at different levels of accuracy. The comparison and model validation is performed by executing a string-matching algorithm on the full-system simulator and on the XMT, using three datasets that generate noticeably different contention patterns.« less

Entanglement of spin waves among four quantum memories.

PubMed

Choi, K S; Goban, A; Papp, S B; van Enk, S J; Kimble, H J

2010-11-18

Quantum networks are composed of quantum nodes that interact coherently through quantum channels, and open a broad frontier of scientific opportunities. For example, a quantum network can serve as a 'web' for connecting quantum processors for computation and communication, or as a 'simulator' allowing investigations of quantum critical phenomena arising from interactions among the nodes mediated by the channels. The physical realization of quantum networks generically requires dynamical systems capable of generating and storing entangled states among multiple quantum memories, and efficiently transferring stored entanglement into quantum channels for distribution across the network. Although such capabilities have been demonstrated for diverse bipartite systems, entangled states have not been achieved for interconnects capable of 'mapping' multipartite entanglement stored in quantum memories to quantum channels. Here we demonstrate measurement-induced entanglement stored in four atomic memories; user-controlled, coherent transfer of the atomic entanglement to four photonic channels; and characterization of the full quadripartite entanglement using quantum uncertainty relations. Our work therefore constitutes an advance in the distribution of multipartite entanglement across quantum networks. We also show that our entanglement verification method is suitable for studying the entanglement order of condensed-matter systems in thermal equilibrium.

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.

Associative memory in an analog iterated-map neural network

NASA Astrophysics Data System (ADS)

Marcus, C. M.; Waugh, F. R.; Westervelt, R. M.

1990-03-01

The behavior of an analog neural network with parallel dynamics is studied analytically and numerically for two associative-memory learning algorithms, the Hebb rule and the pseudoinverse rule. Phase diagrams in the parameter space of analog gain β and storage ratio α are presented. For both learning rules, the networks have large ``recall'' phases in which retrieval states exist and convergence to a fixed point is guaranteed by a global stability criterion. We also demonstrate numerically that using a reduced analog gain increases the probability of recall starting from a random initial state. This phenomenon is comparable to thermal annealing used to escape local minima but has the advantage of being deterministic, and therefore easily implemented in electronic hardware. Similarities and differences between analog neural networks and networks with two-state neurons at finite temperature are also discussed.

On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus.

PubMed

Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-10-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

On Initial Brain Activity Mapping of Associative Memory Code in the Hippocampus

PubMed Central

Tsien, Joe Z.; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Lei Wang, Phillip; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

2013-01-01

It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. PMID:23838072

Topological Schemas of Memory Spaces

PubMed Central

Babichev, Andrey; Dabaghian, Yuri A.

2018-01-01

Hippocampal cognitive map—a neuronal representation of the spatial environment—is widely discussed in the computational neuroscience literature for decades. However, more recent studies point out that hippocampus plays a major role in producing yet another cognitive framework—the memory space—that incorporates not only spatial, but also non-spatial memories. Unlike the cognitive maps, the memory spaces, broadly understood as “networks of interconnections among the representations of events,” have not yet been studied from a theoretical perspective. Here we propose a mathematical approach that allows modeling memory spaces constructively, as epiphenomena of neuronal spiking activity and thus to interlink several important notions of cognitive neurophysiology. First, we suggest that memory spaces have a topological nature—a hypothesis that allows treating both spatial and non-spatial aspects of hippocampal function on equal footing. We then model the hippocampal memory spaces in different environments and demonstrate that the resulting constructions naturally incorporate the corresponding cognitive maps and provide a wider context for interpreting spatial information. Lastly, we propose a formal description of the memory consolidation process that connects memory spaces to the Morris' cognitive schemas-heuristic representations of the acquired memories, used to explain the dynamics of learning and memory consolidation in a given environment. The proposed approach allows evaluating these constructs as the most compact representations of the memory space's structure. PMID:29740306

Recognition of abstract objects via neural oscillators: interaction among topological organization, associative memory and gamma band synchronization.

PubMed

Ursino, Mauro; Magosso, Elisa; Cuppini, Cristiano

2009-02-01

Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.

Architecture of fluid intelligence and working memory revealed by lesion mapping.

PubMed

Barbey, Aron K; Colom, Roberto; Paul, Erick J; Grafman, Jordan

2014-03-01

Although cognitive neuroscience has made valuable progress in understanding the role of the prefrontal cortex in human intelligence, the functional networks that support adaptive behavior and novel problem solving remain to be well characterized. Here, we studied 158 human brain lesion patients to investigate the cognitive and neural foundations of key competencies for fluid intelligence and working memory. We administered a battery of neuropsychological tests, including the Wechsler Adult Intelligence Scale (WAIS) and the N-Back task. Latent variable modeling was applied to obtain error-free scores of fluid intelligence and working memory, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. The observed latent variable modeling and lesion results support an integrative framework for understanding the architecture of fluid intelligence and working memory and make specific recommendations for the interpretation and application of the WAIS and N-Back task to the study of fluid intelligence in health and disease.

An event map of memory space in the hippocampus

PubMed Central

Deuker, Lorena; Bellmund, Jacob LS; Navarro Schröder, Tobias; Doeller, Christian F

2016-01-01

The hippocampus has long been implicated in both episodic and spatial memory, however these mnemonic functions have been traditionally investigated in separate research strands. Theoretical accounts and rodent data suggest a common mechanism for spatial and episodic memory in the hippocampus by providing an abstract and flexible representation of the external world. Here, we monitor the de novo formation of such a representation of space and time in humans using fMRI. After learning spatio-temporal trajectories in a large-scale virtual city, subject-specific neural similarity in the hippocampus scaled with the remembered proximity of events in space and time. Crucially, the structure of the entire spatio-temporal network was reflected in neural patterns. Our results provide evidence for a common coding mechanism underlying spatial and temporal aspects of episodic memory in the hippocampus and shed new light on its role in interleaving multiple episodes in a neural event map of memory space. DOI: http://dx.doi.org/10.7554/eLife.16534.001 PMID:27710766

Synaptic connectivity and spatial memory: a topological approach

NASA Astrophysics Data System (ADS)

Milton, Russell; Babichev, Andrey; Dabaghian, Yuri

2015-03-01

In the hippocampus, a network of place cells generates a cognitive map of space, in which each cell is responsive to a particular area of the environment - its place field. The peak response of each cell and the size of each place field have considerable variability. Experimental evidence suggests that place cells encode a topological map of space that serves as a basis of spatial memory and spatial awareness. Using a computational model based on Persistent Homology Theory we demonstrate that if the parameters of the place cells spiking activity fall inside of the physiological range, the network correctly encodes the topological features of the environment. We next introduce parameters of synaptic connectivity into the model and demonstrate that failures in synapses that detect coincident neuronal activity lead to spatial learning deficiencies similar to the ones that are observed in rodent models of neurodegenerative diseases. Moreover, we show that these learning deficiencies may be mitigated by increasing the number of active cells and/or by increasing their firing rate, suggesting the existence of a compensatory mechanism inherent to the cognitive map.

Learning to read aloud: A neural network approach using sparse distributed memory

NASA Technical Reports Server (NTRS)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

Accurate Prediction of Protein Contact Maps by Coupling Residual Two-Dimensional Bidirectional Long Short-Term Memory with Convolutional Neural Networks.

PubMed

Hanson, Jack; Paliwal, Kuldip; Litfin, Thomas; Yang, Yuedong; Zhou, Yaoqi

2018-06-19

Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12, (Schaarschmidt et al., 2018)) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC)>0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'. SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/. Supplementary data is available at Bioinformatics online.

What do you mean "drunk"? Convergent validation of multiple methods of mapping alcohol expectancy memory networks.

PubMed

Reich, Richard R; Ariel, Idan; Darkes, Jack; Goldman, Mark S

2012-09-01

The configuration and activation of memory networks have been theorized as mechanisms that underlie the often observed link between alcohol expectancies and drinking. A key component of this network is the expectancy "drunk." The memory network configuration of "drunk" was mapped by using cluster analysis of data gathered from the paired-similarities task (PST) and the Alcohol Expectancy Multi-Axial Assessment (AEMAX). A third task, the free associates task (FA), assessed participants' strongest alcohol expectancy associates and was used as a validity check for the cluster analyses. Six hundred forty-seven 18-19-year-olds completed these measures and a measure of alcohol consumption at baseline assessment for a 5-year longitudinal study. For both the PST and AEMAX, "drunk" clustered with mainly negative and sedating effects (e.g., "sick," "dizzy," "sleepy") in lighter drinkers and with more positive and arousing effects (e.g., "happy," "horny," "outgoing") in heavier drinkers, showing that the cognitive organization of expectancies reflected drinker type (and might influence the choice to drink). Consistent with the cluster analyses, in participants who gave "drunk" as an FA response, heavier drinkers rated the word as more positive and arousing than lighter drinkers. Additionally, gender did not account for the observed drinker-type differences. These results support the notion that for some emerging adults, drinking may be linked to what they mean by the word "drunk." PsycINFO Database Record (c) 2012 APA, all rights reserved.

Information flow in layered networks of non-monotonic units

NASA Astrophysics Data System (ADS)

Schittler Neves, Fabio; Martim Schubert, Benno; Erichsen, Rubem, Jr.

2015-07-01

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.

Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

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

Imam, Neena; Humble, Travis S.; McCaskey, Alex

A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recallmore » accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas.« less

Supercomputing '91; Proceedings of the 4th Annual Conference on High Performance Computing, Albuquerque, NM, Nov. 18-22, 1991

NASA Technical Reports Server (NTRS)

1991-01-01

Various papers on supercomputing are presented. The general topics addressed include: program analysis/data dependence, memory access, distributed memory code generation, numerical algorithms, supercomputer benchmarks, latency tolerance, parallel programming, applications, processor design, networks, performance tools, mapping and scheduling, characterization affecting performance, parallelism packaging, computing climate change, combinatorial algorithms, hardware and software performance issues, system issues. (No individual items are abstracted in this volume)

Brain functional network changes following Prelimbic area inactivation in a spatial memory extinction task.

PubMed

Méndez-Couz, Marta; Conejo, Nélida M; Vallejo, Guillermo; Arias, Jorge L

2015-01-01

Several studies suggest a prefrontal cortex involvement during the acquisition and consolidation of spatial memory, suggesting an active modulating role at late stages of acquisition processes. Recently, we have reported that the prelimbic and infralimbic areas of the prefrontal cortex, among other structures, are also specifically involved in the late phases of spatial memory extinction. This study aimed to evaluate whether the inactivation of the prelimbic area of the prefrontal cortex impaired spatial memory extinction. For this purpose, male Wistar rats were implanted bilaterally with cannulae into the prelimbic region of the prefrontal cortex. Animals were trained during 5 consecutive days in a hidden platform task and tested for reference spatial memory immediately after the last training session. One day after completing the training task, bilateral infusion of the GABAA receptor agonist Muscimol was performed before the extinction protocol was carried out. Additionally, cytochrome c oxidase histochemistry was applied to map the metabolic brain activity related to the spatial memory extinction under prelimbic cortex inactivation. Results show that animals acquired the reference memory task in the water maze, and the extinction task was successfully completed without significant impairment. However, analysis of the functional brain networks involved by cytochrome oxidase activity interregional correlations showed changes in brain networks between the group treated with Muscimol as compared to the saline-treated group, supporting the involvement of the mammillary bodies at a the late stage in the memory extinction process. Copyright © 2015 Elsevier B.V. All rights reserved.

Functional brain microstate predicts the outcome in a visuospatial working memory task.

PubMed

Muthukrishnan, Suriya-Prakash; Ahuja, Navdeep; Mehta, Nalin; Sharma, Ratna

2016-11-01

Humans have limited capacity of processing just up to 4 integrated items of information in the working memory. Thus, it is inevitable to commit more errors when challenged with high memory loads. However, the neural mechanisms that determine the accuracy of response at high memory loads still remain unclear. High temporal resolution of Electroencephalography (EEG) technique makes it the best tool to resolve the temporal dynamics of brain networks. EEG-defined microstate is the quasi-stable scalp electrical potential topography that represents the momentary functional state of brain. Thus, it has been possible to assess the information processing currently performed by the brain using EEG microstate analysis. We hypothesize that the EEG microstate preceding the trial could determine its outcome in a visuospatial working memory (VSWM) task. Twenty-four healthy participants performed a high memory load VSWM task, while their brain activity was recorded using EEG. Four microstate maps were found to represent the functional brain state prior to the trials in the VSWM task. One pre-trial microstate map was found to determine the accuracy of subsequent behavioural response. The intracranial generators of the pre-trial microstate map that determined the response accuracy were localized to the visuospatial processing areas at bilateral occipital, right temporal and limbic cortices. Our results imply that the behavioural outcome in a VSWM task could be determined by the intensity of activation of memory representations in the visuospatial processing brain regions prior to the trial. Copyright © 2016 Elsevier B.V. All rights reserved.

Functional expansion representations of artificial neural networks

NASA Technical Reports Server (NTRS)

Gray, W. Steven

1992-01-01

In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

Two-photon interference of weak coherent laser pulses recalled from separate solid-state quantum memories

NASA Astrophysics Data System (ADS)

Jin, Jeongwan; Slater, Joshua A.; Saglamyurek, Erhan; Sinclair, Neil; George, Mathew; Ricken, Raimund; Oblak, Daniel; Sohler, Wolfgang; Tittel, Wolfgang

2013-08-01

Quantum memories allowing reversible transfer of quantum states between light and matter are central to quantum repeaters, quantum networks and linear optics quantum computing. Significant progress regarding the faithful transfer of quantum information has been reported in recent years. However, none of these demonstrations confirm that the re-emitted photons remain suitable for two-photon interference measurements, such as C-NOT gates and Bell-state measurements, which constitute another key ingredient for all aforementioned applications. Here, using pairs of laser pulses at the single-photon level, we demonstrate two-photon interference and Bell-state measurements after either none, one or both pulses have been reversibly mapped to separate thulium-doped lithium niobate waveguides. As the interference is always near the theoretical maximum, we conclude that our solid-state quantum memories, in addition to faithfully mapping quantum information, also preserve the entire photonic wavefunction. Hence, our memories are generally suitable for future applications of quantum information processing that require two-photon interference.

Two-photon interference of weak coherent laser pulses recalled from separate solid-state quantum memories.

PubMed

Jin, Jeongwan; Slater, Joshua A; Saglamyurek, Erhan; Sinclair, Neil; George, Mathew; Ricken, Raimund; Oblak, Daniel; Sohler, Wolfgang; Tittel, Wolfgang

2013-01-01

Quantum memories allowing reversible transfer of quantum states between light and matter are central to quantum repeaters, quantum networks and linear optics quantum computing. Significant progress regarding the faithful transfer of quantum information has been reported in recent years. However, none of these demonstrations confirm that the re-emitted photons remain suitable for two-photon interference measurements, such as C-NOT gates and Bell-state measurements, which constitute another key ingredient for all aforementioned applications. Here, using pairs of laser pulses at the single-photon level, we demonstrate two-photon interference and Bell-state measurements after either none, one or both pulses have been reversibly mapped to separate thulium-doped lithium niobate waveguides. As the interference is always near the theoretical maximum, we conclude that our solid-state quantum memories, in addition to faithfully mapping quantum information, also preserve the entire photonic wavefunction. Hence, our memories are generally suitable for future applications of quantum information processing that require two-photon interference.

Semantic graphs and associative memories

NASA Astrophysics Data System (ADS)

Pomi, Andrés; Mizraji, Eduardo

2004-12-01

Graphs have been increasingly utilized in the characterization of complex networks from diverse origins, including different kinds of semantic networks. Human memories are associative and are known to support complex semantic nets; these nets are represented by graphs. However, it is not known how the brain can sustain these semantic graphs. The vision of cognitive brain activities, shown by modern functional imaging techniques, assigns renewed value to classical distributed associative memory models. Here we show that these neural network models, also known as correlation matrix memories, naturally support a graph representation of the stored semantic structure. We demonstrate that the adjacency matrix of this graph of associations is just the memory coded with the standard basis of the concept vector space, and that the spectrum of the graph is a code invariant of the memory. As long as the assumptions of the model remain valid this result provides a practical method to predict and modify the evolution of the cognitive dynamics. Also, it could provide us with a way to comprehend how individual brains that map the external reality, almost surely with different particular vector representations, are nevertheless able to communicate and share a common knowledge of the world. We finish presenting adaptive association graphs, an extension of the model that makes use of the tensor product, which provides a solution to the known problem of branching in semantic nets.

JuxtaView - A tool for interactive visualization of large imagery on scalable tiled displays

USGS Publications Warehouse

Krishnaprasad, N.K.; Vishwanath, V.; Venkataraman, S.; Rao, A.G.; Renambot, L.; Leigh, J.; Johnson, A.E.; Davis, B.

2004-01-01

JuxtaView is a cluster-based application for viewing ultra-high-resolution images on scalable tiled displays. We present in JuxtaView, a new parallel computing and distributed memory approach for out-of-core montage visualization, using LambdaRAM, a software-based network-level cache system. The ultimate goal of JuxtaView is to enable a user to interactively roam through potentially terabytes of distributed, spatially referenced image data such as those from electron microscopes, satellites and aerial photographs. In working towards this goal, we describe our first prototype implemented over a local area network, where the image is distributed using LambdaRAM, on the memory of all nodes of a PC cluster driving a tiled display wall. Aggressive pre-fetching schemes employed by LambdaRAM help to reduce latency involved in remote memory access. We compare LambdaRAM with a more traditional memory-mapped file approach for out-of-core visualization. ?? 2004 IEEE.

Quantum teleportation between remote atomic-ensemble quantum memories.

PubMed

Bao, Xiao-Hui; Xu, Xiao-Fan; Li, Che-Ming; Yuan, Zhen-Sheng; Lu, Chao-Yang; Pan, Jian-Wei

2012-12-11

Quantum teleportation and quantum memory are two crucial elements for large-scale quantum networks. With the help of prior distributed entanglement as a "quantum channel," quantum teleportation provides an intriguing means to faithfully transfer quantum states among distant locations without actual transmission of the physical carriers [Bennett CH, et al. (1993) Phys Rev Lett 70(13):1895-1899]. Quantum memory enables controlled storage and retrieval of fast-flying photonic quantum bits with stationary matter systems, which is essential to achieve the scalability required for large-scale quantum networks. Combining these two capabilities, here we realize quantum teleportation between two remote atomic-ensemble quantum memory nodes, each composed of ∼10(8) rubidium atoms and connected by a 150-m optical fiber. The spin wave state of one atomic ensemble is mapped to a propagating photon and subjected to Bell state measurements with another single photon that is entangled with the spin wave state of the other ensemble. Two-photon detection events herald the success of teleportation with an average fidelity of 88(7)%. Besides its fundamental interest as a teleportation between two remote macroscopic objects, our technique may be useful for quantum information transfer between different nodes in quantum networks and distributed quantum computing.

Depth of treatment sensitive noise resistant dynamic artificial neural networks model of recall in people with prosopagnosia.

PubMed

Morissette, Laurence; Chartier, Sylvain; Vandermeulen, Robyn; Watier, Nicholas

2012-08-01

The Fusiform Face Area (FFA) is the brain region considered to be responsible for face recognition. Prosopagnosia is a brain disorder causing the inability to a recognise faces that is said to mainly affect the FFA. We put forward a model that simulates the capacity to retrieve label associated with faces and objects depending on the depth of treatment of the information. Akin to prosopagnosia, various localised "lesions" were inserted into the network in order to evaluate the degradation of performance. The network is first composed of a Feature Extracting Bidirectional Associative Memory (FEBAM-SOM) to represent the topological maps allowing the categorisation of all faces. The second component of the network is a Bidirectional Heteroassociative Memory (BHM) that links those representations to their semantic label. For the latter, specific semantic labels were used as well as more general ones. The inputs were images representing faces and various objects. Just like in the visual perceptual system, the images were pre-processed using a low-pass filter. Results showed that the network is able to associate the extracted map with the correct label information. The network is able to generalise and is robust to noise. Moreover, results showed that the recall performance of names associated with faces decrease with the size of lesion without affecting the performance of the objects. Finally, results obtained with the network are also consistent with human ones in that higher level, more general labels are more robust to lesion compared to low level, specific labels. Copyright © 2012 Elsevier Ltd. All rights reserved.

Convergence dynamics and pseudo almost periodicity of a class of nonautonomous RFDEs with applications

NASA Astrophysics Data System (ADS)

Fan, Meng; Ye, Dan

2005-09-01

This paper studies the dynamics of a system of retarded functional differential equations (i.e., RF=Es), which generalize the Hopfield neural network models, the bidirectional associative memory neural networks, the hybrid network models of the cellular neural network type, and some population growth model. Sufficient criteria are established for the globally exponential stability and the existence and uniqueness of pseudo almost periodic solution. The approaches are based on constructing suitable Lyapunov functionals and the well-known Banach contraction mapping principle. The paper ends with some applications of the main results to some neural network models and population growth models and numerical simulations.

Fast, noise-free memory for photon synchronization at room temperature.

PubMed

Finkelstein, Ran; Poem, Eilon; Michel, Ohad; Lahad, Ohr; Firstenberg, Ofer

2018-01-01

Future quantum photonic networks require coherent optical memories for synchronizing quantum sources and gates of probabilistic nature. We demonstrate a fast ladder memory (FLAME) mapping the optical field onto the superposition between electronic orbitals of rubidium vapor. Using a ladder-level system of orbital transitions with nearly degenerate frequencies simultaneously enables high bandwidth, low noise, and long memory lifetime. We store and retrieve 1.7-ns-long pulses, containing 0.5 photons on average, and observe short-time external efficiency of 25%, memory lifetime (1/ e ) of 86 ns, and below 10 -4 added noise photons. Consequently, coupling this memory to a probabilistic source would enhance the on-demand photon generation probability by a factor of 12, the highest number yet reported for a noise-free, room temperature memory. This paves the way toward the controlled production of large quantum states of light from probabilistic photon sources.

Cognitive components of a mathematical processing network in 9-year-old children.

PubMed

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-07-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular 'number sense'. We suggest an 'executive memory function centric' model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors.

Cognitive components of a mathematical processing network in 9-year-old children

PubMed Central

Szűcs, Dénes; Devine, Amy; Soltesz, Fruzsina; Nobes, Alison; Gabriel, Florence

2014-01-01

We determined how various cognitive abilities, including several measures of a proposed domain-specific number sense, relate to mathematical competence in nearly 100 9-year-old children with normal reading skill. Results are consistent with an extended number processing network and suggest that important processing nodes of this network are phonological processing, verbal knowledge, visuo-spatial short-term and working memory, spatial ability and general executive functioning. The model was highly specific to predicting arithmetic performance. There were no strong relations between mathematical achievement and verbal short-term and working memory, sustained attention, response inhibition, finger knowledge and symbolic number comparison performance. Non-verbal intelligence measures were also non-significant predictors when added to our model. Number sense variables were non-significant predictors in the model and they were also non-significant predictors when entered into regression analysis with only a single visuo-spatial WM measure. Number sense variables were predicted by sustained attention. Results support a network theory of mathematical competence in primary school children and falsify the importance of a proposed modular ‘number sense’. We suggest an ‘executive memory function centric’ model of mathematical processing. Mapping a complex processing network requires that studies consider the complex predictor space of mathematics rather than just focusing on a single or a few explanatory factors. PMID:25089322

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network

PubMed Central

Zhang, Kechen

2016-01-01

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a “megamap,” or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world. PMID:27193320

Experimental demonstration of the influence of alcohol advertising on the activation of alcohol expectancies in memory among fourth- and fifth-grade children.

PubMed

Dunn, M E; Yniguez, R M

1999-11-01

Previous work has demonstrated that children's organization and activation of alcohol expectancies in memory vary as a function of alcohol use, even among children as young as in the 3rd grade. To advance the understanding of influences on the development of alcohol expectancies in children, 551 4th- and 5th-grade children were exposed to 5 beer commercials or 5 soft drink commercials. After viewing the advertisements, all children reported their 1st associate to an alcohol prompt and completed a memory model-based measure of children's alcohol expectancies. Multidimensional scaling was used to map expectancies into hypothetical memory network format, and preference mapping was used to derive possible paths of activation. Children who viewed beer commercials were more likely to activate positive and arousing alcohol expectancies. In view of previous findings demonstrating that this pattern of activation corresponded to higher drinking among 3rd, 6th, 9th, and 12th graders, the present findings suggested that antecedents to drinking like exposure to advertising may promote heavier drinking among children by influencing the activation of expectancies in memory.

Two distinct neural networks support the mapping of meaning to a novel word.

PubMed

Ye, Zheng; Mestres-Missé, Anna; Rodriguez-Fornells, Antoni; Münte, Thomas F

2011-07-01

Children can learn the meaning of a new word from context during normal reading or listening, without any explicit instruction. It is unclear how such meaning acquisition is supported and achieved in human brain. In this functional magnetic resonance imaging (fMRI) study we investigated neural networks supporting word learning with a functional connectivity approach. Participants were exposed to a new word presented in two successive sentences and needed to derive the meaning of the new word. We observed two neural networks involved in mapping the meaning to the new word. One network connected the left inferior frontal gyrus (LIFG) with the middle frontal gyrus (MFG), medial superior frontal gyrus, caudate nucleus, thalamus, and inferior parietal lobule. The other network connected the left middle temporal gyrus (LMTG) with the MFG, anterior and posterior cingulate cortex. The LIFG network showed stronger interregional interactions for new than real words, whereas the LMTG network showed similar connectivity patterns for new and real words. We proposed that these two networks support different functions during word learning. The LIFG network appears to select the most appropriate meaning from competing candidates and to map the selected meaning onto the new word. The LMTG network may be recruited to integrate the word into sentential context, regardless of whether the word is real or new. The LIFG and the LMTG networks share a common node, the MFG, suggesting that these two networks communicate in working memory. Copyright © 2010 Wiley-Liss, Inc.

Current progress in multiple-image blind demixing algorithms

NASA Astrophysics Data System (ADS)

Szu, Harold H.

2000-06-01

Imagery edges occur naturally in human visual systems as a consequence of redundancy reduction towards `sparse and orthogonality feature maps,' which have been recently derived from the maximum entropy information-theoretical first principle of artificial neural networks. After a brief match review of such an Independent Component Analysis or Blind Source Separation of edge maps, we explore the de- mixing condition for more than two imagery objects recognizable by an intelligent pair of cameras with memory in a time-multiplex fashion.

An Activation/Inhibition Network Cell.

DTIC Science & Technology

1982-01-01

memory is a powerful concept, no one has found a way of doing activation and inhibition on it, so I’ll stick to the simple mapping. --------- AAB -W...Rumongus Scale Integration.. G-9- Vorking Paper 31 Referens. Fahiman, S.. NL: A s=u LI= rpnng A" using rial-world kowledge . MIT Pess, Cambridge, 1979

Quantum teleportation between remote atomic-ensemble quantum memories

PubMed Central

Bao, Xiao-Hui; Xu, Xiao-Fan; Li, Che-Ming; Yuan, Zhen-Sheng; Lu, Chao-Yang; Pan, Jian-Wei

2012-01-01

Quantum teleportation and quantum memory are two crucial elements for large-scale quantum networks. With the help of prior distributed entanglement as a “quantum channel,” quantum teleportation provides an intriguing means to faithfully transfer quantum states among distant locations without actual transmission of the physical carriers [Bennett CH, et al. (1993) Phys Rev Lett 70(13):1895–1899]. Quantum memory enables controlled storage and retrieval of fast-flying photonic quantum bits with stationary matter systems, which is essential to achieve the scalability required for large-scale quantum networks. Combining these two capabilities, here we realize quantum teleportation between two remote atomic-ensemble quantum memory nodes, each composed of ∼108 rubidium atoms and connected by a 150-m optical fiber. The spin wave state of one atomic ensemble is mapped to a propagating photon and subjected to Bell state measurements with another single photon that is entangled with the spin wave state of the other ensemble. Two-photon detection events herald the success of teleportation with an average fidelity of 88(7)%. Besides its fundamental interest as a teleportation between two remote macroscopic objects, our technique may be useful for quantum information transfer between different nodes in quantum networks and distributed quantum computing. PMID:23144222

The increase in medial prefrontal glutamate/glutamine concentration during memory encoding is associated with better memory performance and stronger functional connectivity in the human medial prefrontal-thalamus-hippocampus network.

PubMed

Thielen, Jan-Willem; Hong, Donghyun; Rohani Rankouhi, Seyedmorteza; Wiltfang, Jens; Fernández, Guillén; Norris, David G; Tendolkar, Indira

2018-06-01

The classical model of the declarative memory system describes the hippocampus and its interactions with representational brain areas in posterior neocortex as being essential for the formation of long-term episodic memories. However, new evidence suggests an extension of this classical model by assigning the medial prefrontal cortex (mPFC) a specific, yet not fully defined role in episodic memory. In this study, we utilized 1H magnetic resonance spectroscopy (MRS) and psychophysiological interaction (PPI) analysis to lend further support for the idea of a mnemonic role of the mPFC in humans. By using MRS, we measured mPFC γ-aminobutyric acid (GABA) and glutamate/glutamine (GLx) concentrations before and after volunteers memorized face-name association. We demonstrate that mPFC GLx but not GABA levels increased during the memory task, which appeared to be related to memory performance. Regarding functional connectivity, we used the subsequent memory paradigm and found that the GLx increase was associated with stronger mPFC connectivity to thalamus and hippocampus for associations subsequently recognized with high confidence as opposed to subsequently recognized with low confidence/forgotten. Taken together, we provide new evidence for an mPFC involvement in episodic memory by showing a memory-related increase in mPFC excitatory neurotransmitter levels that was associated with better memory and stronger memory-related functional connectivity in a medial prefrontal-thalamus-hippocampus network. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Decreased Metabolism in the Posterior Medial Network with Concomitantly Increased Metabolism in the Anterior Temporal Network During Transient Global Amnesia.

PubMed

Yi, SangHak; Park, Young Ho; Jang, Jae-Won; Lim, Jae-Sung; Chun, In Kook; Kim, SangYun

2018-05-01

Perturbation of corticohippocampal circuits is a key step in the pathogenesis of transient global amnesia. We evaluated the spatial distribution of altered cerebral metabolism to determine the location of the corticohippocampal circuits perturbed during the acute stage of transient global amnesia. A consecutive series of 12 patients with transient global amnesia who underwent 18 F-fluorodeoxyglucose positron emission tomography within 3 days after symptom onset was identified. We used statistical parametric mapping with two contrasts to identify regions of decreased and increased brain metabolism in transient global amnesia patients compared with 25 age-matched controls. Transient global amnesia patients showed hypometabolic clusters in the left temporal and bilateral parieto-occipital regions that belong to the posterior medial network as well as, hypermetabolic clusters in the bilateral inferior frontal regions that belong to the anterior temporal network. The posterior medial and anterior temporal networks are the two main corticohippocampal circuits involved in memory-guided behavior. Decreased metabolism in the posterior medial network might explain the impairment of episodic memory observed during the acute stage of transient global amnesia. Concomitant increased metabolism within the anterior temporal network might occur as a compensatory mechanism.

Circuit mechanisms of hippocampal reactivation during sleep.

PubMed

Malerba, Paola; Bazhenov, Maxim

2018-05-01

The hippocampus is important for memory and learning, being a brain site where initial memories are formed and where sharp wave - ripples (SWR) are found, which are responsible for mapping recent memories to long-term storage during sleep-related memory replay. While this conceptual schema is well established, specific intrinsic and network-level mechanisms driving spatio-temporal patterns of hippocampal activity during sleep, and specifically controlling off-line memory reactivation are unknown. In this study, we discuss a model of hippocampal CA1-CA3 network generating spontaneous characteristic SWR activity. Our study predicts the properties of CA3 input which are necessary for successful CA1 ripple generation and the role of synaptic interactions and intrinsic excitability in spike sequence replay during SWRs. Specifically, we found that excitatory synaptic connections promote reactivation in both CA3 and CA1, but the different dynamics of sharp waves in CA3 and ripples in CA1 result in a differential role for synaptic inhibition in modulating replay: promoting spike sequence specificity in CA3 but not in CA1 areas. Finally, we describe how awake learning of spatial trajectories leads to synaptic changes sufficient to drive hippocampal cells' reactivation during sleep, as required for sleep-related memory consolidation. Copyright © 2018 Elsevier Inc. All rights reserved.

Merlin - Massively parallel heterogeneous computing

NASA Technical Reports Server (NTRS)

Wittie, Larry; Maples, Creve

1989-01-01

Hardware and software for Merlin, a new kind of massively parallel computing system, are described. Eight computers are linked as a 300-MIPS prototype to develop system software for a larger Merlin network with 16 to 64 nodes, totaling 600 to 3000 MIPS. These working prototypes help refine a mapped reflective memory technique that offers a new, very general way of linking many types of computer to form supercomputers. Processors share data selectively and rapidly on a word-by-word basis. Fast firmware virtual circuits are reconfigured to match topological needs of individual application programs. Merlin's low-latency memory-sharing interfaces solve many problems in the design of high-performance computing systems. The Merlin prototypes are intended to run parallel programs for scientific applications and to determine hardware and software needs for a future Teraflops Merlin network.

A symbolic/subsymbolic interface protocol for cognitive modeling

PubMed Central

Simen, Patrick; Polk, Thad

2009-01-01

Researchers studying complex cognition have grown increasingly interested in mapping symbolic cognitive architectures onto subsymbolic brain models. Such a mapping seems essential for understanding cognition under all but the most extreme viewpoints (namely, that cognition consists exclusively of digitally implemented rules; or instead, involves no rules whatsoever). Making this mapping reduces to specifying an interface between symbolic and subsymbolic descriptions of brain activity. To that end, we propose parameterization techniques for building cognitive models as programmable, structured, recurrent neural networks. Feedback strength in these models determines whether their components implement classically subsymbolic neural network functions (e.g., pattern recognition), or instead, logical rules and digital memory. These techniques support the implementation of limited production systems. Though inherently sequential and symbolic, these neural production systems can exploit principles of parallel, analog processing from decision-making models in psychology and neuroscience to explain the effects of brain damage on problem solving behavior. PMID:20711520

How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks

PubMed Central

Rombouts, Jaldert O.; Bohte, Sander M.; Roelfsema, Pieter R.

2015-01-01

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. We propose that the response selection stage sends attentional feedback signals to earlier processing levels, forming synaptic tags at those connections responsible for the stimulus-response mapping. Globally released neuromodulators then interact with tagged synapses to determine their plasticity. The resulting learning rule endows neural networks with the capacity to create new working memory representations of task relevant information as persistent activity. It is remarkably generic: it explains how association neurons learn to store task-relevant information for linear as well as non-linear stimulus-response mappings, how they become tuned to category boundaries or analog variables, depending on the task demands, and how they learn to integrate probabilistic evidence for perceptual decisions. PMID:25742003

Application of neural networks to group technology

NASA Astrophysics Data System (ADS)

Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

1991-08-01

Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

Short-Term Memory for Space and Time Flexibly Recruit Complementary Sensory-Biased Frontal Lobe Attention Networks.

PubMed

Michalka, Samantha W; Kong, Lingqiang; Rosen, Maya L; Shinn-Cunningham, Barbara G; Somers, David C

2015-08-19

The frontal lobes control wide-ranging cognitive functions; however, functional subdivisions of human frontal cortex are only coarsely mapped. Here, functional magnetic resonance imaging reveals two distinct visual-biased attention regions in lateral frontal cortex, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), anatomically interdigitated with two auditory-biased attention regions, transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic functional connectivity analysis demonstrates that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Interestingly, we observe that spatial and temporal short-term memory (STM), respectively, recruit visual and auditory attention networks in the frontal lobe, independent of sensory modality. These findings not only demonstrate that both sensory modality and information domain influence frontal lobe functional organization, they also demonstrate that spatial processing co-localizes with visual processing and that temporal processing co-localizes with auditory processing in lateral frontal cortex. Copyright © 2015 Elsevier Inc. All rights reserved.

Spatial-area selective retrieval of multiple object-place associations in a hierarchical cognitive map formed by theta phase coding.

PubMed

Sato, Naoyuki; Yamaguchi, Yoko

2009-06-01

The human cognitive map is known to be hierarchically organized consisting of a set of perceptually clustered landmarks. Patient studies have demonstrated that these cognitive maps are maintained by the hippocampus, while the neural dynamics are still poorly understood. The authors have shown that the neural dynamic "theta phase precession" observed in the rodent hippocampus may be capable of forming hierarchical cognitive maps in humans. In the model, a visual input sequence consisting of object and scene features in the central and peripheral visual fields, respectively, results in the formation of a hierarchical cognitive map for object-place associations. Surprisingly, it is possible for such a complex memory structure to be formed in a few seconds. In this paper, we evaluate the memory retrieval of object-place associations in the hierarchical network formed by theta phase precession. The results show that multiple object-place associations can be retrieved with the initial cue of a scene input. Importantly, according to the wide-to-narrow unidirectional connections among scene units, the spatial area for object-place retrieval can be controlled by the spatial area of the initial cue input. These results indicate that the hierarchical cognitive maps have computational advantages on a spatial-area selective retrieval of multiple object-place associations. Theta phase precession dynamics is suggested as a fundamental neural mechanism of the human cognitive map.

Network resiliency through memory health monitoring and proactive management

DOEpatents

Andrade Costa, Carlos H.; Cher, Chen-Yong; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

2017-11-21

A method for managing a network queue memory includes receiving sensor information about the network queue memory, predicting a memory failure in the network queue memory based on the sensor information, and outputting a notification through a plurality of nodes forming a network and using the network queue memory, the notification configuring communications between the nodes.

Temporal Multimode Storage of Entangled Photon Pairs

NASA Astrophysics Data System (ADS)

Tiranov, Alexey; Strassmann, Peter C.; Lavoie, Jonathan; Brunner, Nicolas; Huber, Marcus; Verma, Varun B.; Nam, Sae Woo; Mirin, Richard P.; Lita, Adriana E.; Marsili, Francesco; Afzelius, Mikael; Bussières, Félix; Gisin, Nicolas

2016-12-01

Multiplexed quantum memories capable of storing and processing entangled photons are essential for the development of quantum networks. In this context, we demonstrate and certify the simultaneous storage and retrieval of two entangled photons inside a solid-state quantum memory and measure a temporal multimode capacity of ten modes. This is achieved by producing two polarization-entangled pairs from parametric down-conversion and mapping one photon of each pair onto a rare-earth-ion-doped (REID) crystal using the atomic frequency comb (AFC) protocol. We develop a concept of indirect entanglement witnesses, which can be used as Schmidt number witnesses, and we use it to experimentally certify the presence of more than one entangled pair retrieved from the quantum memory. Our work puts forward REID-AFC as a platform compatible with temporal multiplexing of several entangled photon pairs along with a new entanglement certification method, useful for the characterization of multiplexed quantum memories.

A functional magnetic resonance imaging study mapping the episodic memory encoding network in temporal lobe epilepsy

PubMed Central

Sidhu, Meneka K.; Stretton, Jason; Winston, Gavin P.; Bonelli, Silvia; Centeno, Maria; Vollmar, Christian; Symms, Mark; Thompson, Pamela J.; Koepp, Matthias J.

2013-01-01

Functional magnetic resonance imaging has demonstrated reorganization of memory encoding networks within the temporal lobe in temporal lobe epilepsy, but little is known of the extra-temporal networks in these patients. We investigated the temporal and extra-temporal reorganization of memory encoding networks in refractory temporal lobe epilepsy and the neural correlates of successful subsequent memory formation. We studied 44 patients with unilateral temporal lobe epilepsy and hippocampal sclerosis (24 left) and 26 healthy control subjects. All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words with subsequent out-of-scanner recognition assessments. A blocked analysis was used to investigate activations during encoding and neural correlates of subsequent memory were investigated using an event-related analysis. Event-related activations were then correlated with out-of-scanner verbal and visual memory scores. During word encoding, control subjects activated the left prefrontal cortex and left hippocampus whereas patients with left hippocampal sclerosis showed significant additional right temporal and extra-temporal activations. Control subjects displayed subsequent verbal memory effects within left parahippocampal gyrus, left orbitofrontal cortex and fusiform gyrus whereas patients with left hippocampal sclerosis activated only right posterior hippocampus, parahippocampus and fusiform gyrus. Correlational analysis showed that patients with left hippocampal sclerosis with better verbal memory additionally activated left orbitofrontal cortex, anterior cingulate cortex and left posterior hippocampus. During face encoding, control subjects showed right lateralized prefrontal cortex and bilateral hippocampal activations. Patients with right hippocampal sclerosis showed increased temporal activations within the superior temporal gyri bilaterally and no increased extra-temporal areas of activation compared with control subjects. Control subjects showed subsequent visual memory effects within right amygdala, hippocampus, fusiform gyrus and orbitofrontal cortex. Patients with right hippocampal sclerosis showed subsequent visual memory effects within right posterior hippocampus, parahippocampal and fusiform gyri, and predominantly left hemisphere extra-temporal activations within the insula and orbitofrontal cortex. Correlational analysis showed that patients with right hippocampal sclerosis with better visual memory activated the amygdala bilaterally, right anterior parahippocampal gyrus and left insula. Right sided extra-temporal areas of reorganization observed in patients with left hippocampal sclerosis during word encoding and bilateral lateral temporal reorganization in patients with right hippocampal sclerosis during face encoding were not associated with subsequent memory formation. Reorganization within the medial temporal lobe, however, is an efficient process. The orbitofrontal cortex is critical to subsequent memory formation in control subjects and patients. Activations within anterior cingulum and insula correlated with better verbal and visual subsequent memory in patients with left and right hippocampal sclerosis, respectively, representing effective extra-temporal recruitment. PMID:23674488

Synaptic Mechanisms of Memory Consolidation during Sleep Slow Oscillations

PubMed Central

Wei, Yina; Krishnan, Giri P.

2016-01-01

Sleep is critical for regulation of synaptic efficacy, memories, and learning. However, the underlying mechanisms of how sleep rhythms contribute to consolidating memories acquired during wakefulness remain unclear. Here we studied the role of slow oscillations, 0.2–1 Hz rhythmic transitions between Up and Down states during stage 3/4 sleep, on dynamics of synaptic connectivity in the thalamocortical network model implementing spike-timing-dependent synaptic plasticity. We found that the spatiotemporal pattern of Up-state propagation determines the changes of synaptic strengths between neurons. Furthermore, an external input, mimicking hippocampal ripples, delivered to the cortical network results in input-specific changes of synaptic weights, which persisted after stimulation was removed. These synaptic changes promoted replay of specific firing sequences of the cortical neurons. Our study proposes a neuronal mechanism on how an interaction between hippocampal input, such as mediated by sharp wave-ripple events, cortical slow oscillations, and synaptic plasticity, may lead to consolidation of memories through preferential replay of cortical cell spike sequences during slow-wave sleep. SIGNIFICANCE STATEMENT Sleep is critical for memory and learning. Replay during sleep of temporally ordered spike sequences related to a recent experience was proposed to be a neuronal substrate of memory consolidation. However, specific mechanisms of replay or how spike sequence replay leads to synaptic changes that underlie memory consolidation are still poorly understood. Here we used a detailed computational model of the thalamocortical system to report that interaction between slow cortical oscillations and synaptic plasticity during deep sleep can underlie mapping hippocampal memory traces to persistent cortical representation. This study provided, for the first time, a mechanistic explanation of how slow-wave sleep may promote consolidation of recent memory events. PMID:27076422

On building a memory evolutive system for application to learning and cognition modeling.

PubMed

de Lima do Rego Monteiro, Julio; Kogler, Joao Eduardo; Ribeiro, Joao Henrique Ranhel; Netto, Marcio Lobo

2010-01-01

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich's formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.

Highly-efficient quantum memory for polarization qubits in a spatially-multiplexed cold atomic ensemble.

PubMed

Vernaz-Gris, Pierre; Huang, Kun; Cao, Mingtao; Sheremet, Alexandra S; Laurat, Julien

2018-01-25

Quantum memory for flying optical qubits is a key enabler for a wide range of applications in quantum information. A critical figure of merit is the overall storage and retrieval efficiency. So far, despite the recent achievements of efficient memories for light pulses, the storage of qubits has suffered from limited efficiency. Here we report on a quantum memory for polarization qubits that combines an average conditional fidelity above 99% and efficiency around 68%, thereby demonstrating a reversible qubit mapping where more information is retrieved than lost. The qubits are encoded with weak coherent states at the single-photon level and the memory is based on electromagnetically-induced transparency in an elongated laser-cooled ensemble of cesium atoms, spatially multiplexed for dual-rail storage. This implementation preserves high optical depth on both rails, without compromise between multiplexing and storage efficiency. Our work provides an efficient node for future tests of quantum network functionalities and advanced photonic circuits.

Working memory circuit as a function of increasing age in healthy adolescence: A systematic review and meta-analyses.

PubMed

Andre, Julia; Picchioni, Marco; Zhang, Ruibin; Toulopoulou, Timothea

2016-01-01

Working memory ability matures through puberty and early adulthood. Deficits in working memory are linked to the risk of onset of neurodevelopmental disorders such as schizophrenia, and there is a significant temporal overlap between the peak of first episode psychosis risk and working memory maturation. In order to characterize the normal working memory functional maturation process through this critical phase of cognitive development we conducted a systematic review and coordinate based meta-analyses of all the available primary functional magnetic resonance imaging studies (n = 382) that mapped WM function in healthy adolescents (10-17 years) and young adults (18-30 years). Activation Likelihood Estimation analyses across all WM tasks revealed increased activation with increasing subject age in the middle frontal gyrus (BA6) bilaterally, the left middle frontal gyrus (BA10), the left precuneus and left inferior parietal gyri (BA7; 40). Decreased activation with increasing age was found in the right superior frontal (BA8), left junction of postcentral and inferior parietal (BA3/40), and left limbic cingulate gyrus (BA31). These results suggest that brain activation during adolescence increased with age principally in higher order cortices, part of the core working memory network, while reductions were detected in more diffuse and potentially more immature neural networks. Understanding the process by which the brain and its cognitive functions mature through healthy adulthood may provide us with new clues to understanding the vulnerability to neurodevelopmental disorders.

A lexical semantic hub for heteromodal naming in middle fusiform gyrus.

PubMed

Forseth, Kiefer James; Kadipasaoglu, Cihan Mehmet; Conner, Christopher Richard; Hickok, Gregory; Knight, Robert Thomas; Tandon, Nitin

2018-07-01

Semantic memory underpins our understanding of objects, people, places, and ideas. Anomia, a disruption of semantic memory access, is the most common residual language disturbance and is seen in dementia and following injury to temporal cortex. While such anomia has been well characterized by lesion symptom mapping studies, its pathophysiology is not well understood. We hypothesize that inputs to the semantic memory system engage a specific heteromodal network hub that integrates lexical retrieval with the appropriate semantic content. Such a network hub has been proposed by others, but has thus far eluded precise spatiotemporal delineation. This limitation in our understanding of semantic memory has impeded progress in the treatment of anomia. We evaluated the cortical structure and dynamics of the lexical semantic network in driving speech production in a large cohort of patients with epilepsy using electrocorticography (n = 64), functional MRI (n = 36), and direct cortical stimulation (n = 30) during two generative language processes that rely on semantic knowledge: visual picture naming and auditory naming to definition. Each task also featured a non-semantic control condition: scrambled pictures and reversed speech, respectively. These large-scale data of the left, language-dominant hemisphere uniquely enable convergent, high-resolution analyses of neural mechanisms characterized by rapid, transient dynamics with strong interactions between distributed cortical substrates. We observed three stages of activity during both visual picture naming and auditory naming to definition that were serially organized: sensory processing, lexical semantic processing, and articulation. Critically, the second stage was absent in both the visual and auditory control conditions. Group activity maps from both electrocorticography and functional MRI identified heteromodal responses in middle fusiform gyrus, intraparietal sulcus, and inferior frontal gyrus; furthermore, the spectrotemporal profiles of these three regions revealed coincident activity preceding articulation. Only in the middle fusiform gyrus did direct cortical stimulation disrupt both naming tasks while still preserving the ability to repeat sentences. These convergent data strongly support a model in which a distinct neuroanatomical substrate in middle fusiform gyrus provides access to object semantic information. This under-appreciated locus of semantic processing is at risk in resections for temporal lobe epilepsy as well as in trauma and strokes that affect the inferior temporal cortex-it may explain the range of anomic states seen in these conditions. Further characterization of brain network behaviour engaging this region in both healthy and diseased states will expand our understanding of semantic memory and further development of therapies directed at anomia.

Context-sensitive autoassociative memories as expert systems in medical diagnosis

PubMed Central

Pomi, Andrés; Olivera, Fernando

2006-01-01

Background The complexity of our contemporary medical practice has impelled the development of different decision-support aids based on artificial intelligence and neural networks. Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences. Methods We present the context-dependent autoassociative memory model. The sets of diseases and symptoms are mapped onto a pair of basis of orthogonal vectors. A matrix memory stores the associations between the signs and symptoms, and their corresponding diseases. A minimal numerical example is presented to show how to instruct the memory and how the system works. In order to provide a quick appreciation of the validity of the model and its potential clinical relevance we implemented an application with real data. A memory was trained with published data of neonates with suspected late-onset sepsis in a neonatal intensive care unit (NICU). A set of personal clinical observations was used as a test set to evaluate the capacity of the model to discriminate between septic and non-septic neonates on the basis of clinical and laboratory findings. Results We show here that matrix memory models with associations modulated by context can perform automatic medical diagnosis. The sequential availability of new information over time makes the system progress in a narrowing process that reduces the range of diagnostic possibilities. At each step the system provides a probabilistic map of the different possible diagnoses to that moment. The system can incorporate the clinical experience, building in that way a representative database of historical data that captures geo-demographical differences between patient populations. The trained model succeeds in diagnosing late-onset sepsis within the test set of infants in the NICU: sensitivity 100%; specificity 80%; percentage of true positives 91%; percentage of true negatives 100%; accuracy (true positives plus true negatives over the totality of patients) 93,3%; and Cohen's kappa index 0,84. Conclusion Context-dependent associative memories can operate as medical expert systems. The model is presented in a simple and tutorial way to encourage straightforward implementations by medical groups. An application with real data, presented as a primary evaluation of the validity and potentiality of the model in medical diagnosis, shows that the model is a highly promising alternative in the development of accuracy diagnostic tools. PMID:17121675

Episodic memory in aspects of large-scale brain networks

PubMed Central

Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

2015-01-01

Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.

PubMed

Kepinska, Olga; de Rover, Mischa; Caspers, Johanneke; Schiller, Niels O

2017-03-01

In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities. Copyright © 2016 Elsevier B.V. All rights reserved.

Mapping the neuropsychological profile of temporal lobe epilepsy using cognitive network topology and graph theory.

PubMed

Kellermann, Tanja S; Bonilha, Leonardo; Eskandari, Ramin; Garcia-Ramos, Camille; Lin, Jack J; Hermann, Bruce P

2016-10-01

Normal cognitive function is defined by harmonious interaction among multiple neuropsychological domains. Epilepsy has a disruptive effect on cognition, but how diverse cognitive abilities differentially interact with one another compared with healthy controls (HC) is unclear. This study used graph theory to analyze the community structure of cognitive networks in adults with temporal lobe epilepsy (TLE) compared with that in HC. Neuropsychological assessment was performed in 100 patients with TLE and 82 HC. For each group, an adjacency matrix was constructed representing pair-wise correlation coefficients between raw scores obtained in each possible test combination. For each cognitive network, each node corresponded to a cognitive test; each link corresponded to the correlation coefficient between tests. Global network structure, community structure, and node-wise graph theory properties were qualitatively assessed. The community structure in patients with TLE was composed of fewer, larger, more mixed modules, characterizing three main modules representing close relationships between the following: 1) aspects of executive function (EF), verbal and visual memory, 2) speed and fluency, and 3) speed, EF, perception, language, intelligence, and nonverbal memory. Conversely, controls exhibited a relative division between cognitive functions, segregating into more numerous, smaller modules consisting of the following: 1) verbal memory, 2) language, perception, and intelligence, 3) speed and fluency, and 4) visual memory and EF. Overall node-wise clustering coefficient and efficiency were increased in TLE. Adults with TLE demonstrate a less clear and poorly structured segregation between multiple cognitive domains. This panorama suggests a higher degree of interdependency across multiple cognitive domains in TLE, possibly indicating compensatory mechanisms to overcome functional impairments. Copyright © 2016 Elsevier Inc. All rights reserved.

Experiments and Analyses of Data Transfers Over Wide-Area Dedicated Connections

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

Rao, Nageswara S.; Liu, Qiang; Sen, Satyabrata

Dedicated wide-area network connections are increasingly employed in high-performance computing and big data scenarios. One might expect the performance and dynamics of data transfers over such connections to be easy to analyze due to the lack of competing traffic. However, non-linear transport dynamics and end-system complexities (e.g., multi-core hosts and distributed filesystems) can in fact make analysis surprisingly challenging. We present extensive measurements of memory-to-memory and disk-to-disk file transfers over 10 Gbps physical and emulated connections with 0–366 ms round trip times (RTTs). For memory-to-memory transfers, profiles of both TCP and UDT throughput as a function of RTT show concavemore » and convex regions; large buffer sizes and more parallel flows lead to wider concave regions, which are highly desirable. TCP and UDT both also display complex throughput dynamics, as indicated by their Poincare maps and Lyapunov exponents. For disk-to-disk transfers, we determine that high throughput can be achieved via a combination of parallel I/O threads, parallel network threads, and direct I/O mode. Our measurements also show that Lustre filesystems can be mounted over long-haul connections using LNet routers, although challenges remain in jointly optimizing file I/O and transport method parameters to achieve peak throughput.« less

Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits.

PubMed

Boguslawski, Bartosz; Gripon, Vincent; Seguin, Fabrice; Heitzmann, Frédéric

2016-02-01

Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They, thus, behave similarly to the human brain's memory that is capable, for instance, of retrieving the end of a song, given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of the bits used). Recently, a new family of sparse associative memories achieving almost optimal efficiency has been proposed. Their structure, relying on binary connections and neurons, induces a direct mapping between input messages and stored patterns. Nevertheless, it is well known that nonuniformity of the stored messages can lead to a dramatic decrease in performance. In this paper, we show the impact of nonuniformity on the performance of this recent model, and we exploit the structure of the model to improve its performance in practical applications, where data are not necessarily uniform. In order to approach the performance of networks with uniformly distributed messages presented in theoretical studies, twin neurons are introduced. To assess the adapted model, twin neurons are used with the real-world data to optimize power consumption of electronic circuits in practical test cases.

Two Distinct Scene-Processing Networks Connecting Vision and Memory.

PubMed

Baldassano, Christopher; Esteva, Andre; Fei-Fei, Li; Beck, Diane M

2016-01-01

A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.

Memory-Efficient Analysis of Dense Functional Connectomes.

PubMed

Loewe, Kristian; Donohue, Sarah E; Schoenfeld, Mircea A; Kruse, Rudolf; Borgelt, Christian

2016-01-01

The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download.

Memory-Efficient Analysis of Dense Functional Connectomes

PubMed Central

Loewe, Kristian; Donohue, Sarah E.; Schoenfeld, Mircea A.; Kruse, Rudolf; Borgelt, Christian

2016-01-01

The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download. PMID:27965565

Prediction of Sea Surface Temperature Using Long Short-Term Memory

NASA Astrophysics Data System (ADS)

Zhang, Qin; Wang, Hui; Dong, Junyu; Zhong, Guoqiang; Sun, Xin

2017-10-01

This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.

Neural mechanisms of discourse comprehension: a human lesion study

PubMed Central

Colom, Roberto; Grafman, Jordan

2014-01-01

Discourse comprehension is a hallmark of human social behaviour and refers to the act of interpreting a written or spoken message by constructing mental representations that integrate incoming language with prior knowledge and experience. Here, we report a human lesion study (n = 145) that investigates the neural mechanisms underlying discourse comprehension (measured by the Discourse Comprehension Test) and systematically examine its relation to a broad range of psychological factors, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores obtained from these factors were submitted to voxel-based lesion-symptom mapping to elucidate their neural substrates. Stepwise regression analyses revealed that working memory and extraversion reliably predict individual differences in discourse comprehension: higher working memory scores and lower extraversion levels predict better discourse comprehension performance. Lesion mapping results indicated that these convergent variables depend on a shared network of frontal and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The observed findings motivate an integrative framework for understanding the neural foundations of discourse comprehension, suggesting that core elements of discourse processing emerge from a distributed network of brain regions that support specific competencies for executive and social function. PMID:24293267

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

PubMed

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model

PubMed Central

Gönner, Lorenz; Vitay, Julien; Hamker, Fred H.

2017-01-01

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions. PMID:29075187

Mobile and replicated alignment of arrays in data-parallel programs

NASA Technical Reports Server (NTRS)

Chatterjee, Siddhartha; Gilbert, John R.; Schreiber, Robert

1993-01-01

When a data-parallel language like FORTRAN 90 is compiled for a distributed-memory machine, aggregate data objects (such as arrays) are distributed across the processor memories. The mapping determines the amount of residual communication needed to bring operands of parallel operations into alignment with each other. A common approach is to break the mapping into two stages: first, an alignment that maps all the objects to an abstract template, and then a distribution that maps the template to the processors. We solve two facets of the problem of finding alignments that reduce residual communication: we determine alignments that vary in loops, and objects that should have replicated alignments. We show that loop-dependent mobile alignment is sometimes necessary for optimum performance, and we provide algorithms with which a compiler can determine good mobile alignments for objects within do loops. We also identify situations in which replicated alignment is either required by the program itself (via spread operations) or can be used to improve performance. We propose an algorithm based on network flow that determines which objects to replicate so as to minimize the total amount of broadcast communication in replication. This work on mobile and replicated alignment extends our earlier work on determining static alignment.

The default mode network and the working memory network are not anti-correlated during all phases of a working memory task.

PubMed

Piccoli, Tommaso; Valente, Giancarlo; Linden, David E J; Re, Marta; Esposito, Fabrizio; Sack, Alexander T; Di Salle, Francesco

2015-01-01

The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between "task-positive" and "task-negative" brain networks. Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network.

The Default Mode Network and the Working Memory Network Are Not Anti-Correlated during All Phases of a Working Memory Task

PubMed Central

Piccoli, Tommaso; Valente, Giancarlo; Linden, David E. J.; Re, Marta; Esposito, Fabrizio; Sack, Alexander T.; Salle, Francesco Di

2015-01-01

Introduction The default mode network and the working memory network are known to be anti-correlated during sustained cognitive processing, in a load-dependent manner. We hypothesized that functional connectivity among nodes of the two networks could be dynamically modulated by task phases across time. Methods To address the dynamic links between default mode network and the working memory network, we used a delayed visuo-spatial working memory paradigm, which allowed us to separate three different phases of working memory (encoding, maintenance, and retrieval), and analyzed the functional connectivity during each phase within and between the default mode network and the working memory network networks. Results We found that the two networks are anti-correlated only during the maintenance phase of working memory, i.e. when attention is focused on a memorized stimulus in the absence of external input. Conversely, during the encoding and retrieval phases, when the external stimulation is present, the default mode network is positively coupled with the working memory network, suggesting the existence of a dynamically switching of functional connectivity between “task-positive” and “task-negative” brain networks. Conclusions Our results demonstrate that the well-established dichotomy of the human brain (anti-correlated networks during rest and balanced activation-deactivation during cognition) has a more nuanced organization than previously thought and engages in different patterns of correlation and anti-correlation during specific sub-phases of a cognitive task. This nuanced organization reinforces the hypothesis of a direct involvement of the default mode network in cognitive functions, as represented by a dynamic rather than static interaction with specific task-positive networks, such as the working memory network. PMID:25848951

Place Cells, Grid Cells, and Memory

PubMed Central

Moser, May-Britt; Rowland, David C.; Moser, Edvard I.

2015-01-01

The hippocampal system is critical for storage and retrieval of declarative memories, including memories for locations and events that take place at those locations. Spatial memories place high demands on capacity. Memories must be distinct to be recalled without interference and encoding must be fast. Recent studies have indicated that hippocampal networks allow for fast storage of large quantities of uncorrelated spatial information. The aim of the this article is to review and discuss some of this work, taking as a starting point the discovery of multiple functionally specialized cell types of the hippocampal–entorhinal circuit, such as place, grid, and border cells. We will show that grid cells provide the hippocampus with a metric, as well as a putative mechanism for decorrelation of representations, that the formation of environment-specific place maps depends on mechanisms for long-term plasticity in the hippocampus, and that long-term spatiotemporal memory storage may depend on offline consolidation processes related to sharp-wave ripple activity in the hippocampus. The multitude of representations generated through interactions between a variety of functionally specialized cell types in the entorhinal–hippocampal circuit may be at the heart of the mechanism for declarative memory formation. PMID:25646382

Memristor-based neural networks: Synaptic versus neuronal stochasticity

NASA Astrophysics Data System (ADS)

Naous, Rawan; AlShedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled Nabil

2016-11-01

In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.

Scaling properties in time-varying networks with memory

NASA Astrophysics Data System (ADS)

Kim, Hyewon; Ha, Meesoon; Jeong, Hawoong

2015-12-01

The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.

Simulated mossy fiber associated feedforward circuit functioning as a highpass filter.

PubMed

Zalay, Osbert C; Bardakjian, Berj L

2006-01-01

Learning and memory rely on the strict regulation of communication between neurons in the hippocampus. The mossy fiber (MF) pathway connects the dentate gyrus to the auto-associative CA3 network, and the information it carries is controlled by a feedforward circuit combining disynaptic inhibition with monosynaptic excitation. Analysis of the MF associated circuit using a mapped clock oscillator (MCO) model reveals the circuit to be a highpass filter.

Functional connectivity of hippocampal and prefrontal networks during episodic and spatial memory based on real-world environments.

PubMed

Robin, Jessica; Hirshhorn, Marnie; Rosenbaum, R Shayna; Winocur, Gordon; Moscovitch, Morris; Grady, Cheryl L

2015-01-01

Several recent studies have compared episodic and spatial memory in neuroimaging paradigms in order to understand better the contribution of the hippocampus to each of these tasks. In the present study, we build on previous findings showing common neural activation in default network areas during episodic and spatial memory tasks based on familiar, real-world environments (Hirshhorn et al. (2012) Neuropsychologia 50:3094-3106). Following previous demonstrations of the presence of functionally connected sub-networks within the default network, we performed seed-based functional connectivity analyses to determine how, depending on the task, the hippocampus and prefrontal cortex differentially couple with one another and with distinct whole-brain networks. We found evidence for a medial prefrontal-parietal network and a medial temporal lobe network, which were functionally connected to the prefrontal and hippocampal seeds, respectively, regardless of the nature of the memory task. However, these two networks were functionally connected with one another during the episodic memory task, but not during spatial memory tasks. Replicating previous reports of fractionation of the default network into stable sub-networks, this study also shows how these sub-networks may flexibly couple and uncouple with one another based on task demands. These findings support the hypothesis that episodic memory and spatial memory share a common medial temporal lobe-based neural substrate, with episodic memory recruiting additional prefrontal sub-networks. © 2014 Wiley Periodicals, Inc.

Identification of serial number on bank card using recurrent neural network

NASA Astrophysics Data System (ADS)

Liu, Li; Huang, Linlin; Xue, Jian

2018-04-01

Identification of serial number on bank card has many applications. Due to the different number printing mode, complex background, distortion in shape, etc., it is quite challenging to achieve high identification accuracy. In this paper, we propose a method using Normalization-Cooperated Gradient Feature (NCGF) and Recurrent Neural Network (RNN) based on Long Short-Term Memory (LSTM) for serial number identification. The NCGF maps the gradient direction elements of original image to direction planes such that the RNN with direction planes as input can recognize numbers more accurately. Taking the advantages of NCGF and RNN, we get 90%digit string recognition accuracy.

Social Networks and Memory over 15 Years of Followup in a Cohort of Older Australians: Results from the Australian Longitudinal Study of Ageing

PubMed Central

Giles, Lynne C.; Anstey, Kaarin J.; Walker, Ruth B.; Luszcz, Mary A.

2012-01-01

The purpose was to examine the relationship between different types of social networks and memory over 15 years of followup in a large cohort of older Australians who were cognitively intact at study baseline. Our specific aims were to investigate whether social networks were associated with memory, determine if different types of social networks had different relationships with memory, and examine if changes in memory over time differed according to types of social networks. We used five waves of data from the Australian Longitudinal Study of Ageing, and followed 706 participants with an average age of 78.6 years (SD 5.7) at baseline. The relationships between five types of social networks and changes in memory were assessed. The results suggested a gradient of effect; participants in the upper tertile of friends or overall social networks had better memory scores than those in the mid tertile, who in turn had better memory scores than participants in the lower tertile. There was evidence of a linear, but not quadratic, effect of time on memory, and an interaction between friends' social networks and time was apparent. Findings are discussed with respect to mechanisms that might explain the observed relationships between social networks and memory. PMID:22988510

Electrical Stimulation Modulates High γ Activity and Human Memory Performance

PubMed Central

Berry, Brent M.; Miller, Laura R.; Khadjevand, Fatemeh; Ezzyat, Youssef; Wanda, Paul; Sperling, Michael R.; Lega, Bradley; Stead, S. Matt

2018-01-01

Direct electrical stimulation of the brain has emerged as a powerful treatment for multiple neurological diseases, and as a potential technique to enhance human cognition. Despite its application in a range of brain disorders, it remains unclear how stimulation of discrete brain areas affects memory performance and the underlying electrophysiological activities. Here, we investigated the effect of direct electrical stimulation in four brain regions known to support declarative memory: hippocampus (HP), parahippocampal region (PH) neocortex, prefrontal cortex (PF), and lateral temporal cortex (TC). Intracranial EEG recordings with stimulation were collected from 22 patients during performance of verbal memory tasks. We found that high γ (62–118 Hz) activity induced by word presentation was modulated by electrical stimulation. This modulatory effect was greatest for trials with “poor” memory encoding. The high γ modulation correlated with the behavioral effect of stimulation in a given brain region: it was negative, i.e., the induced high γ activity was decreased, in the regions where stimulation decreased memory performance, and positive in the lateral TC where memory enhancement was observed. Our results suggest that the effect of electrical stimulation on high γ activity induced by word presentation may be a useful biomarker for mapping memory networks and guiding therapeutic brain stimulation. PMID:29404403

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

PubMed

He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-04-17

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

PubMed Central

Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-01-01

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. PMID:29673171

Fast Mapping Across Time: Memory Processes Support Children's Retention of Learned Words.

PubMed

Vlach, Haley A; Sandhofer, Catherine M

2012-01-01

Children's remarkable ability to map linguistic labels to referents in the world is commonly called fast mapping. The current study examined children's (N = 216) and adults' (N = 54) retention of fast-mapped words over time (immediately, after a 1-week delay, and after a 1-month delay). The fast mapping literature often characterizes children's retention of words as consistently high across timescales. However, the current study demonstrates that learners forget word mappings at a rapid rate. Moreover, these patterns of forgetting parallel forgetting functions of domain-general memory processes. Memory processes are critical to children's word learning and the role of one such process, forgetting, is discussed in detail - forgetting supports extended mapping by promoting the memory and generalization of words and categories.

Iconic memory and parietofrontal network: fMRI study using temporal integration.

PubMed

Saneyoshi, Ayako; Niimi, Ryosuke; Suetsugu, Tomoko; Kaminaga, Tatsuro; Yokosawa, Kazuhiko

2011-08-03

We investigated the neural basis of iconic memory using functional magnetic resonance imaging. The parietofrontal network of selective attention is reportedly relevant to readout from iconic memory. We adopted a temporal integration task that requires iconic memory but not selective attention. The results showed that the task activated the parietofrontal network, confirming that the network is involved in readout from iconic memory. We further tested a condition in which temporal integration was performed by visual short-term memory but not by iconic memory. However, no brain region revealed higher activation for temporal integration by iconic memory than for temporal integration by visual short-term memory. This result suggested that there is no localized brain region specialized for iconic memory per se.

Plastic modulation of episodic memory networks in the aging brain with cognitive decline.

PubMed

Bai, Feng; Yuan, Yonggui; Yu, Hui; Zhang, Zhijun

2016-07-15

Social-cognitive processing has been posited to underlie general functions such as episodic memory. Episodic memory impairment is a recognized hallmark of amnestic mild cognitive impairment (aMCI) who is at a high risk for dementia. Three canonical networks, self-referential processing, executive control processing and salience processing, have distinct roles in episodic memory retrieval processing. It remains unclear whether and how these sub-networks of the episodic memory retrieval system would be affected in aMCI. This task-state fMRI study constructed systems-level episodic memory retrieval sub-networks in 28 aMCI and 23 controls using two computational approaches: a multiple region-of-interest based approach and a voxel-level functional connectivity-based approach, respectively. These approaches produced the remarkably similar findings that the self-referential processing network made critical contributions to episodic memory retrieval in aMCI. More conspicuous alterations in self-referential processing of the episodic memory retrieval network were identified in aMCI. In order to complete a given episodic memory retrieval task, increases in cooperation between the self-referential processing network and other sub-networks were mobilized in aMCI. Self-referential processing mediate the cooperation of the episodic memory retrieval sub-networks as it may help to achieve neural plasticity and may contribute to the prevention and treatment of dementia. Copyright © 2016 Elsevier B.V. All rights reserved.

The role of autobiographical memory networks in the experience of negative emotions: how our remembered past elicits our current feelings.

PubMed

Philippe, Frederick L; Koestner, Richard; Lecours, Serge; Beaulieu-Pelletier, Genevieve; Bois, Katy

2011-12-01

The present research examined the role of autobiographical memory networks on negative emotional experiences. Results from 2 studies found support for an active but also discriminant role of autobiographical memories and their related networked memories on negative emotions. In addition, in line with self-determination theory, thwarting of the psychological needs for competence, autonomy, and relatedness was found to be the critical component of autobiographical memory affecting negative emotional experiences. Study 1 revealed that need thwarting in a specific autobiographical memory network related to the theme of loss was positively associated with depressive negative emotions, but not with other negative emotions. Study 2 showed within a prospective design a differential predictive validity between 2 autobiographical memory networks (an anger-related vs. a guilt-related memory) on situational anger reactivity with respect to unfair treatment. All of these results held after controlling for neuroticism (Studies 1 and 2), self-control (Study 2), and for the valence (Study 1) and emotions (Study 2) found in the measured autobiographical memory network. These findings highlight the ongoing emotional significance of representations of need thwarting in autobiographical memory networks. (c) 2011 APA, all rights reserved.

Reliable, Memory Speed Storage for Cluster Computing Frameworks

DTIC Science & Technology

2014-06-16

specification API that can capture computations in many of today’s popular data -parallel computing models, e.g., MapReduce and SQL. We also ported the Hadoop ...today’s big data workloads: • Immutable data : Data is immutable once written, since dominant underlying storage systems, such as HDFS [3], only support...network transfers, so reads can be data -local. • Program size vs. data size: In big data processing, the same operation is repeatedly applied on massive

Multimodal Fusion with Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

PubMed Central

Qi, Shile; Calhoun, Vince D.; van Erp, Theo G. M.; Bustillo, Juan; Damaraju, Eswar; Turner, Jessica A.; Du, Yuhui; Chen, Jiayu; Yu, Qingbao; Mathalon, Daniel H.; Ford, Judith M.; Voyvodic, James; Mueller, Bryon A.; Belger, Aysenil; Ewen, Sarah Mc; Potkin, Steven G.; Preda, Adrian; Jiang, Tianzi

2017-01-01

Multimodal fusion is an effective approach to take advantage of cross-information among multiple imaging data to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. To date, there is increasing interest to uncover the neurocognitive mapping of specific behavioral measurement on enriched brain imaging data; hence, a supervised, goal-directed model that enables a priori information as a reference to guide multimodal data fusion is in need and a natural option. Here we proposed a fusion with reference model, called “multi-site canonical correlation analysis with reference plus joint independent component analysis” (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to reference information, such as cognitive scores. In a 3-way fusion simulation, the proposed method was compared with its alternatives on estimation accuracy of both target component decomposition and modality linkage detection. MCCAR+jICA outperforms others with higher precision. In human imaging data, working memory performance was utilized as a reference to investigate the covarying functional and structural brain patterns among 3 modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Interestingly, similar brain maps were identified between the two cohorts, with substantial overlap in the executive control networks in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports, while MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the potential of such results to identify potential neuromarkers for mental disorders. PMID:28708547

Topographical memory for newly-learned maps is differentially affected by route-based versus landmark-based learning: a functional MRI study.

PubMed

Beatty, Erin L; Muller-Gass, Alexandra; Wojtarowicz, Dorothy; Jobidon, Marie-Eve; Smith, Ingrid; Lam, Quan; Vartanian, Oshin

2018-04-11

Humans rely on topographical memory to encode information about spatial aspects of environments. However, even though people adopt different strategies when learning new maps, little is known about the impact of those strategies on topographical memory, and their neural correlates. To examine that issue, we presented participants with 40 unfamiliar maps, each of which displayed one major route and three landmarks. Half were instructed to memorize the maps by focusing on the route, whereas the other half memorized the maps by focusing on the landmarks. One day later, the participants were tested on their ability to distinguish previously studied 'old' maps from completely unfamiliar 'new' maps under conditions of high and low working memory load in the functional MRI scanner. Viewing old versus new maps was associated with relatively greater activation in a distributed set of regions including bilateral inferior temporal gyrus - an important region for recognizing visual objects. Critically, whereas the performance of participants who had followed a route-based strategy dropped to chance level under high working memory load, participants who had followed a landmark-based strategy performed at above chance levels under both high and low working memory load - reflected by relatively greater activation in the left inferior parietal lobule (i.e. rostral part of the supramarginal gyrus known as area PFt). Our findings suggest that landmark-based learning may buffer against the effects of working memory load during recognition, and that this effect is represented by the greater involvement of a brain region implicated in both topographical and working memory.

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

PubMed

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

PubMed

Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

2016-01-15

Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the derivation of these networks, we also observed some brain-behavioral correlations, notably for the fluid-reasoning network whose network score correlated with performance on the memory and fluid-reasoning tasks. While age did not influence the expression of this RANN, the slope of the association between network score and fluid-reasoning performance was negatively associated with higher ages. These results provide support for the hypothesis that a set of specific, age-invariant neural networks underlies these four RAs, and that these networks maintain their cognitive specificity and level of intensity across age. Activation common to all 12 tasks was identified as another activation pattern resulting from a mean-contrast Partial-Least-Squares technique. This common pattern did show associations with age and some subject demographics for some of the reference domains, lending support to the overall conclusion that aspects of neural processing that are specific to any cognitive reference ability stay constant across age, while aspects that are common to all reference abilities differ across age. Copyright © 2015 Elsevier Inc. All rights reserved.

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

PubMed Central

Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2009-01-01

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly’s halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support. PMID:20396612

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network.

PubMed

Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2009-06-01

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly's halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support.

How to Compress Sequential Memory Patterns into Periodic Oscillations: General Reduction Rules

PubMed Central

Zhang, Kechen

2017-01-01

A neural network with symmetric reciprocal connections always admits a Lyapunov function, whose minima correspond to the memory states stored in the network. Networks with suitable asymmetric connections can store and retrieve a sequence of memory patterns, but the dynamics of these networks cannot be characterized as readily as that of the symmetric networks due to the lack of established general methods. Here, a reduction method is developed for a class of asymmetric attractor networks that store sequences of activity patterns as associative memories, as in a Hopfield network. The method projects the original activity pattern of the network to a low-dimensional space such that sequential memory retrievals in the original network correspond to periodic oscillations in the reduced system. The reduced system is self-contained and provides quantitative information about the stability and speed of sequential memory retrievals in the original network. The time evolution of the overlaps between the network state and the stored memory patterns can also be determined from extended reduced systems. The reduction procedure can be summarized by a few reduction rules, which are applied to several network models, including coupled networks and networks with time-delayed connections, and the analytical solutions of the reduced systems are confirmed by numerical simulations of the original networks. Finally, a local learning rule that provides an approximation to the connection weights involving the pseudoinverse is also presented. PMID:24877729

The C. elegans Connectome Consists of Homogenous Circuits with Defined Functional Roles

PubMed Central

Azulay, Aharon; Zaslaver, Alon

2016-01-01

A major goal of systems neuroscience is to decipher the structure-function relationship in neural networks. Here we study network functionality in light of the common-neighbor-rule (CNR) in which a pair of neurons is more likely to be connected the more common neighbors it shares. Focusing on the fully-mapped neural network of C. elegans worms, we establish that the CNR is an emerging property in this connectome. Moreover, sets of common neighbors form homogenous structures that appear in defined layers of the network. Simulations of signal propagation reveal their potential functional roles: signal amplification and short-term memory at the sensory/inter-neuron layer, and synchronized activity at the motoneuron layer supporting coordinated movement. A coarse-grained view of the neural network based on homogenous connected sets alone reveals a simple modular network architecture that is intuitive to understand. These findings provide a novel framework for analyzing larger, more complex, connectomes once these become available. PMID:27606684

States of mind: Emotions, body feelings, and thoughts share distributed neural networks

PubMed Central

Oosterwijk, Suzanne; Lindquist, Kristen A.; Anderson, Eric; Dautoff, Rebecca; Moriguchi, Yoshiya; Barrett, Lisa Feldman

2012-01-01

Scientists have traditionally assumed that different kinds of mental states (e.g., fear, disgust, love, memory, planning, concentration, etc.) correspond to different psychological faculties that have domain-specific correlates in the brain. Yet, growing evidence points to the constructionist hypothesis that mental states emerge from the combination of domain-general psychological processes that map to large-scale distributed brain networks. In this paper, we report a novel study testing a constructionist model of the mind in which participants generated three kinds of mental states (emotions, body feelings, or thoughts) while we measured activity within large-scale distributed brain networks using fMRI. We examined the similarity and differences in the pattern of network activity across these three classes of mental states. Consistent with a constructionist hypothesis, a combination of large-scale distributed networks contributed to emotions, thoughts, and body feelings, although these mental states differed in the relative contribution of those networks. Implications for a constructionist functional architecture of diverse mental states are discussed. PMID:22677148

Mapping attractor fields in face space: the atypicality bias in face recognition.

PubMed

Tanaka, J; Giles, M; Kremen, S; Simon, V

1998-09-01

A familiar face can be recognized across many changes in the stimulus input. In this research, the many-to-one mapping of face stimuli to a single face memory is referred to as a face memory's 'attractor field'. According to the attractor field approach, a face memory will be activated by any stimuli falling within the boundaries of its attractor field. It was predicted that by virtue of its location in a multi-dimensional face space, the attractor field of an atypical face will be larger than the attractor field of a typical face. To test this prediction, subjects make likeness judgments to morphed faces that contained a 50/50 contribution from an atypical and a typical parent face. The main result of four experiments was that the morph face was judged to bear a stronger resemblance to the atypical face parent than the typical face parent. The computational basis of the atypicality bias was demonstrated in a neural network simulation where morph inputs of atypical and typical representations elicited stronger activation of atypical output units than of typical output units. Together, the behavioral and simulation evidence supports the view that the attractor fields of atypical faces span over a broader region of face space that the attractor fields of typical faces.

From network heterogeneities to familiarity detection and hippocampal memory management

PubMed Central

Wang, Jane X.; Poe, Gina; Zochowski, Michal

2009-01-01

Hippocampal-neocortical interactions are key to the rapid formation of novel associative memories in the hippocampus and consolidation to long term storage sites in the neocortex. We investigated the role of network correlates during information processing in hippocampal-cortical networks. We found that changes in the intrinsic network dynamics due to the formation of structural network heterogeneities alone act as a dynamical and regulatory mechanism for stimulus novelty and familiarity detection, thereby controlling memory management in the context of memory consolidation. This network dynamic, coupled with an anatomically established feedback between the hippocampus and the neocortex, recovered heretofore unexplained properties of neural activity patterns during memory management tasks which we observed during sleep in multiunit recordings from behaving animals. Our simple dynamical mechanism shows an experimentally matched progressive shift of memory activation from the hippocampus to the neocortex and thus provides the means to achieve an autonomous off-line progression of memory consolidation. PMID:18999453

Mnemonic training reshapes brain networks to support superior memory

PubMed Central

Dresler, Martin; Shirer, William R.; Konrad, Boris N.; Müller, Nils C.J.; Wagner, Isabella C.; Fernández, Guillén; Czisch, Michael; Greicius, Michael D.

2017-01-01

Summary Memory skills strongly differ across the general population, however little is known about the brain characteristics supporting superior memory performance. Here, we assess functional brain network organization of 23 of the world’s most successful memory athletes and matched controls by fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that in a group of naïve controls, functional connectivity changes induced by six weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain’s functional network organization to enable superior memory performance. PMID:28279356

Mapping parahippocampal systems for recognition and recency memory in the absence of the rat hippocampus

PubMed Central

Kinnavane, L; Amin, E; Horne, M; Aggleton, J P

2014-01-01

The present study examined immediate-early gene expression in the perirhinal cortex of rats with hippocampal lesions. The goal was to test those models of recognition memory which assume that the perirhinal cortex can function independently of the hippocampus. The c-fos gene was targeted, as its expression in the perirhinal cortex is strongly associated with recognition memory. Four groups of rats were examined. Rats with hippocampal lesions and their surgical controls were given either a recognition memory task (novel vs. familiar objects) or a relative recency task (objects with differing degrees of familiarity). Perirhinal Fos expression in the hippocampal-lesioned groups correlated with both recognition and recency performance. The hippocampal lesions, however, had no apparent effect on overall levels of perirhinal or entorhinal cortex c-fos expression in response to novel objects, with only restricted effects being seen in the recency condition. Network analyses showed that whereas the patterns of parahippocampal interactions were differentially affected by novel or familiar objects, these correlated networks were not altered by hippocampal lesions. Additional analyses in control rats revealed two modes of correlated medial temporal activation. Novel stimuli recruited the pathway from the lateral entorhinal cortex (cortical layer II or III) to hippocampal field CA3, and thence to CA1. Familiar stimuli recruited the direct pathway from the lateral entorhinal cortex (principally layer III) to CA1. The present findings not only reveal the independence from the hippocampus of some perirhinal systems associated with recognition memory, but also show how novel stimuli engage hippocampal subfields in qualitatively different ways from familiar stimuli. PMID:25264133

Seizure Control and Memory Impairment Are Related to Disrupted Brain Functional Integration in Temporal Lobe Epilepsy.

PubMed

Park, Chang-Hyun; Choi, Yun Seo; Jung, A-Reum; Chung, Hwa-Kyoung; Kim, Hyeon Jin; Yoo, Jeong Hyun; Lee, Hyang Woon

2017-01-01

Brain functional integration can be disrupted in patients with temporal lobe epilepsy (TLE), but the clinical relevance of this disruption is not completely understood. The authors hypothesized that disrupted functional integration over brain regions remote from, as well as adjacent to, the seizure focus could be related to clinical severity in terms of seizure control and memory impairment. Using resting-state functional MRI data acquired from 48 TLE patients and 45 healthy controls, the authors mapped functional brain networks and assessed changes in a network parameter of brain functional integration, efficiency, to examine the distribution of disrupted functional integration within and between brain regions. The authors assessed whether the extent of altered efficiency was influenced by seizure control status and whether the degree of altered efficiency was associated with the severity of memory impairment. Alterations in the efficiency were observed primarily near the subcortical region ipsilateral to the seizure focus in TLE patients. The extent of regional involvement was greater in patients with poor seizure control: it reached the frontal, temporal, occipital, and insular cortices in TLE patients with poor seizure control, whereas it was limited to the limbic and parietal cortices in TLE patients with good seizure control. Furthermore, TLE patients with poor seizure control experienced more severe memory impairment, and this was associated with lower efficiency in the brain regions with altered efficiency. These findings indicate that the distribution of disrupted brain functional integration is clinically relevant, as it is associated with seizure control status and comorbid memory impairment.

Route Descriptions by Visually Impaired and Sighted Children from Memory and from Maps.

ERIC Educational Resources Information Center

Edwards, Rachel; Ungar, Simon; Blades, Mark

1998-01-01

This study evaluated descriptions, either from memory or by using a map (print or tactile), of 12 visually impaired and 12 sighted elementary grade children of two routes around their schools. Descriptions from maps were generally poorer than those from memory. Qualitative differences were also found between descriptions of visually impaired and…

Youthful Brains in Older Adults: Preserved Neuroanatomy in the Default Mode and Salience Networks Contributes to Youthful Memory in Superaging.

PubMed

Sun, Felicia W; Stepanovic, Michael R; Andreano, Joseph; Barrett, Lisa Feldman; Touroutoglou, Alexandra; Dickerson, Bradford C

2016-09-14

Decline in cognitive skills, especially in memory, is often viewed as part of "normal" aging. Yet some individuals "age better" than others. Building on prior research showing that cortical thickness in one brain region, the anterior midcingulate cortex, is preserved in older adults with memory performance abilities equal to or better than those of people 20-30 years younger (i.e., "superagers"), we examined the structural integrity of two large-scale intrinsic brain networks in superaging: the default mode network, typically engaged during memory encoding and retrieval tasks, and the salience network, typically engaged during attention, motivation, and executive function tasks. We predicted that superagers would have preserved cortical thickness in critical nodes in these networks. We defined superagers (60-80 years old) based on their performance compared to young adults (18-32 years old) on the California Verbal Learning Test Long Delay Free Recall test. We found regions within the networks of interest where the cerebral cortex of superagers was thicker than that of typical older adults, and where superagers were anatomically indistinguishable from young adults; hippocampal volume was also preserved in superagers. Within the full group of older adults, thickness of a number of regions, including the anterior temporal cortex, rostral medial prefrontal cortex, and anterior midcingulate cortex, correlated with memory performance, as did the volume of the hippocampus. These results indicate older adults with youthful memory abilities have youthful brain regions in key paralimbic and limbic nodes of the default mode and salience networks that support attentional, executive, and mnemonic processes subserving memory function. Memory performance typically declines with age, as does cortical structural integrity, yet some older adults maintain youthful memory. We tested the hypothesis that superagers (older individuals with youthful memory performance) would exhibit preserved neuroanatomy in key brain networks subserving memory. We found that superagers not only perform similarly to young adults on memory testing, they also do not show the typical patterns of brain atrophy in certain regions. These regions are contained largely within two major intrinsic brain networks: the default mode network, implicated in memory encoding, storage, and retrieval, and the salience network, associated with attention and executive processes involved in encoding and retrieval. Preserved neuroanatomical integrity in these networks is associated with better memory performance among older adults. Copyright © 2016 Sun, Stepanovic et al.

Complex network structure influences processing in long-term and short-term memory.

PubMed

Vitevitch, Michael S; Chan, Kit Ying; Roodenrys, Steven

2012-07-01

Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological word-forms influenced retrieval from the mental lexicon (that portion of long-term memory dedicated to language) during the on-line recognition and production of spoken words. In the present study we examined how network structure influences other retrieval processes in long- and short-term memory. In a false-memory task-examining long-term memory-participants falsely recognized more words with low- than high-C. In a recognition memory task-examining veridical memories in long-term memory-participants correctly recognized more words with low- than high-C. However, participants in a serial recall task-examining redintegration in short-term memory-recalled lists comprised of high-C words more accurately than lists comprised of low-C words. These results demonstrate that network structure influences cognitive processes associated with several forms of memory including lexical, long-term, and short-term.

Optimizing Coverage of Three-Dimensional Wireless Sensor Networks by Means of Photon Mapping

DTIC Science & Technology

2013-12-01

information if it does not display a currently valid OMB control number. 1. REPORT DATE DEC 2013 2. REPORT TYPE 3. DATES COVERED 00-00-2013 to 00-00...information about the monitored space is sensed?” Solving this formulation of the AGP relies upon the creation of a model describing how a set of...simulated photons will propagate in a 3D virtual environment. Furthermore, the photon model requires an efficient data structure with small memory

State Space Modeling of Time-Varying Contemporaneous and Lagged Relations in Connectivity Maps

PubMed Central

Molenaar, Peter C. M.; Beltz, Adriene M.; Gates, Kathleen M.; Wilson, Stephen J.

2017-01-01

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. PMID:26546863

Increased functional connectivity within memory networks following memory rehabilitation in multiple sclerosis.

PubMed

Leavitt, Victoria M; Wylie, Glenn R; Girgis, Peter A; DeLuca, John; Chiaravalloti, Nancy D

2014-09-01

Identifying effective behavioral treatments to improve memory in persons with learning and memory impairment is a primary goal for neurorehabilitation researchers. Memory deficits are the most common cognitive symptom in multiple sclerosis (MS), and hold negative professional and personal consequences for people who are often in the prime of their lives when diagnosed. A 10-session behavioral treatment, the modified Story Memory Technique (mSMT), was studied in a randomized, placebo-controlled clinical trial. Behavioral improvements and increased fMRI activation were shown after treatment. Here, connectivity within the neural networks underlying memory function was examined with resting-state functional connectivity (RSFC) in a subset of participants from the clinical trial. We hypothesized that the treatment would result in increased integrity of connections within two primary memory networks of the brain, the hippocampal memory network, and the default network (DN). Seeds were placed in left and right hippocampus, and the posterior cingulate cortex. Increased connectivity was found between left hippocampus and cortical regions specifically involved in memory for visual imagery, as well as among critical hubs of the DN. These results represent the first evidence for efficacy of a behavioral intervention to impact the integrity of neural networks subserving memory functions in persons with MS.

Dopamine D1 signaling organizes network dynamics underlying working memory.

PubMed

Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

2016-06-01

Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.

Mapping of the Underlying Neural Mechanisms of Maintenance and Manipulation in Visuo-Spatial Working Memory Using An n-back Mental Rotation Task: A Functional Magnetic Resonance Imaging Study.

PubMed

Lamp, Gemma; Alexander, Bonnie; Laycock, Robin; Crewther, David P; Crewther, Sheila G

2016-01-01

Mapping of the underlying neural mechanisms of visuo-spatial working memory (WM) has been shown to consistently elicit activity in right hemisphere dominant fronto-parietal networks. However to date, the bulk of neuroimaging literature has focused largely on the maintenance aspect of visuo-spatial WM, with a scarcity of research into the aspects of WM involving manipulation of information. Thus, this study aimed to compare maintenance-only with maintenance and manipulation of visuo-spatial stimuli (3D cube shapes) utilizing a 1-back task while functional magnetic resonance imaging (fMRI) scans were acquired. Sixteen healthy participants (9 women, M = 23.94 years, SD = 2.49) were required to perform the 1-back task with or without mentally rotating the shapes 90° on a vertical axis. When no rotation was required (maintenance-only condition), a right hemispheric lateralization was revealed across fronto-parietal areas. However, when the task involved maintaining and manipulating the same stimuli through 90° rotation, activation was primarily seen in the bilateral parietal lobe and left fusiform gyrus. The findings confirm that the well-established right lateralized fronto-parietal networks are likely to underlie simple maintenance of visuo-spatial stimuli. The results also suggest that the added demand of manipulation of information maintained online appears to require further neural recruitment of functionally related areas. In particular mental rotation of visuospatial stimuli required bilateral parietal areas, and the left fusiform gyrus potentially to maintain a categorical or object representation. It can be concluded that WM is a complex neural process involving the interaction of an increasingly large network.

Mapping of the Underlying Neural Mechanisms of Maintenance and Manipulation in Visuo-Spatial Working Memory Using An n-back Mental Rotation Task: A Functional Magnetic Resonance Imaging Study

PubMed Central

Lamp, Gemma; Alexander, Bonnie; Laycock, Robin; Crewther, David P.; Crewther, Sheila G.

2016-01-01

Mapping of the underlying neural mechanisms of visuo-spatial working memory (WM) has been shown to consistently elicit activity in right hemisphere dominant fronto-parietal networks. However to date, the bulk of neuroimaging literature has focused largely on the maintenance aspect of visuo-spatial WM, with a scarcity of research into the aspects of WM involving manipulation of information. Thus, this study aimed to compare maintenance-only with maintenance and manipulation of visuo-spatial stimuli (3D cube shapes) utilizing a 1-back task while functional magnetic resonance imaging (fMRI) scans were acquired. Sixteen healthy participants (9 women, M = 23.94 years, SD = 2.49) were required to perform the 1-back task with or without mentally rotating the shapes 90° on a vertical axis. When no rotation was required (maintenance-only condition), a right hemispheric lateralization was revealed across fronto-parietal areas. However, when the task involved maintaining and manipulating the same stimuli through 90° rotation, activation was primarily seen in the bilateral parietal lobe and left fusiform gyrus. The findings confirm that the well-established right lateralized fronto-parietal networks are likely to underlie simple maintenance of visuo-spatial stimuli. The results also suggest that the added demand of manipulation of information maintained online appears to require further neural recruitment of functionally related areas. In particular mental rotation of visuospatial stimuli required bilateral parietal areas, and the left fusiform gyrus potentially to maintain a categorical or object representation. It can be concluded that WM is a complex neural process involving the interaction of an increasingly large network. PMID:27199694

Cooperation in memory-based prisoner's dilemma game on interdependent networks

NASA Astrophysics Data System (ADS)

Luo, Chao; Zhang, Xiaolin; Liu, Hong; Shao, Rui

2016-05-01

Memory or so-called experience normally plays the important role to guide the human behaviors in real world, that is essential for rational decisions made by individuals. Hence, when the evolutionary behaviors of players with bounded rationality are investigated, it is reasonable to make an assumption that players in system are with limited memory. Besides, in order to unravel the intricate variability of complex systems in real world and make a highly integrative understanding of their dynamics, in recent years, interdependent networks as a comprehensive network structure have obtained more attention in this community. In this article, the evolution of cooperation in memory-based prisoner's dilemma game (PDG) on interdependent networks composed by two coupled square lattices is studied. Herein, all or part of players are endowed with finite memory ability, and we focus on the mutual influence of memory effect and interdependent network reciprocity on cooperation of spatial PDG. We show that the density of cooperation can be significantly promoted within an optimal region of memory length and interdependent strength. Furthermore, distinguished by whether having memory ability/external links or not, each kind of players on networks would have distinct evolutionary behaviors. Our work could be helpful to understand the emergence and maintenance of cooperation under the evolution of memory-based players on interdependent networks.

Dynamic Neural Networks Supporting Memory Retrieval

PubMed Central

St. Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

2011-01-01

How do separate neural networks interact to support complex cognitive processes such as remembrance of the personal past? Autobiographical memory (AM) retrieval recruits a consistent pattern of activation that potentially comprises multiple neural networks. However, it is unclear how such large-scale neural networks interact and are modulated by properties of the memory retrieval process. In the present functional MRI (fMRI) study, we combined independent component analysis (ICA) and dynamic causal modeling (DCM) to understand the neural networks supporting AM retrieval. ICA revealed four task-related components consistent with the previous literature: 1) Medial Prefrontal Cortex (PFC) Network, associated with self-referential processes, 2) Medial Temporal Lobe (MTL) Network, associated with memory, 3) Frontoparietal Network, associated with strategic search, and 4) Cingulooperculum Network, associated with goal maintenance. DCM analysis revealed that the medial PFC network drove activation within the system, consistent with the importance of this network to AM retrieval. Additionally, memory accessibility and recollection uniquely altered connectivity between these neural networks. Recollection modulated the influence of the medial PFC on the MTL network during elaboration, suggesting that greater connectivity among subsystems of the default network supports greater re-experience. In contrast, memory accessibility modulated the influence of frontoparietal and MTL networks on the medial PFC network, suggesting that ease of retrieval involves greater fluency among the multiple networks contributing to AM. These results show the integration between neural networks supporting AM retrieval and the modulation of network connectivity by behavior. PMID:21550407

Neural correlates of recognition memory of social information in people with schizophrenia

PubMed Central

Harvey, Philippe-Olivier; Lepage, Martin

2014-01-01

Background Social dysfunction is a hallmark characteristic of schizophrenia. Part of it may stem from an inability to efficiently encode social information into memory and retrieve it later. This study focused on whether patients with schizophrenia show a memory boost for socially relevant information and engage the same neural network as controls when processing social stimuli that were previously encoded into memory. Methods Patients with schizophrenia and healthy controls performed a social and nonsocial picture recognition memory task while being scanned. We calculated memory performance using d′. Our main analysis focused on brain activity associated with recognition memory of social and nonsocial pictures. Results Our study included 28 patients with schizophrenia and 26 controls. Healthy controls demonstrated a memory boost for socially relevant information. In contrast, patients with schizophrenia failed to show enhanced recognition sensitivity for social pictures. At the neural level, patients did not engage the dorsomedial prefrontal cortex (DMPFC) as much as controls while recognizing social pictures. Limitations Our study did not include direct measures of self-referential processing. All but 3 patients were taking antipsychotic medications, which may have altered both the behavioural performance during the picture recognition memory task and brain activity. Conclusion Impaired social memory in patients with schizophrenia may be associated with altered DMPFC activity. A reduction of DMPFC activity may reflect less involvement of self-referential processes during memory retrieval. Our functional MRI results contribute to a better mapping of the neural disturbances associated with social memory impairment in patients with schizophrenia and may facilitate the development of innovative treatments, such as transcranial magnetic stimulation. PMID:24119792

Neural correlates of recognition memory of social information in people with schizophrenia.

PubMed

Harvey, Philippe-Olivier; Lepage, Martin

2014-03-01

Social dysfunction is a hallmark characteristic of schizophrenia. Part of it may stem from an inability to efficiently encode social information into memory and retrieve it later. This study focused on whether patients with schizophrenia show a memory boost for socially relevant information and engage the same neural network as controls when processing social stimuli that were previously encoded into memory. Patients with schizophrenia and healthy controls performed a social and nonsocial picture recognition memory task while being scanned. We calculated memory performance using d'. Our main analysis focused on brain activity associated with recognition memory of social and nonsocial pictures. Our study included 28 patients with schizophrenia and 26 controls. Healthy controls demonstrated a memory boost for socially relevant information. In contrast, patients with schizophrenia failed to show enhanced recognition sensitivity for social pictures. At the neural level, patients did not engage the dorsomedial prefrontal cortex (DMPFC) as much as controls while recognizing social pictures. Our study did not include direct measures of self-referential processing. All but 3 patients were taking antipsychotic medications, which may have altered both the behavioural performance during the picture recognition memory task and brain activity. Impaired social memory in patients with schizophrenia may be associated with altered DMPFC activity. A reduction of DMPFC activity may reflect less involvement of self-referential processes during memory retrieval. Our functional MRI results contribute to a better mapping of the neural disturbances associated with social memory impairment in patients with schizophrenia and may facilitate the development of innovative treatments, such as transcranial magnetic stimulation.

Hippocampal functional connectivity and episodic memory in early childhood

PubMed Central

Riggins, Tracy; Geng, Fengji; Blankenship, Sarah L.; Redcay, Elizabeth

2016-01-01

Episodic memory relies on a distributed network of brain regions, with the hippocampus playing a critical and irreplaceable role. Few studies have examined how changes in this network contribute to episodic memory development early in life. The present addressed this gap by examining relations between hippocampal functional connectivity and episodic memory in 4-and 6-year-old children (n=40). Results revealed similar hippocampal functional connectivity between age groups, which included lateral temporal regions, precuneus, and multiple parietal and prefrontal regions, and functional specialization along the longitudinal axis. Despite these similarities, developmental differences were also observed. Specifically, 3 (of 4) regions within the hippocampal memory network were positively associated with episodic memory in 6-year-old children, but negatively associated with episodic memory in 4-year-old children. In contrast, all 3 regions outside the hippocampal memory network were negatively associated with episodic memory in older children, but positively associated with episodic memory in younger children. These interactions are interpreted within an interactive specialization framework and suggest the hippocampus becomes functionally integrated with cortical regions that are part of the hippocampal memory network in adults and functionally segregated from regions unrelated to memory in adults, both of which are associated with age-related improvements in episodic memory ability. PMID:26900967

Hippocampal functional connectivity and episodic memory in early childhood.

PubMed

Riggins, Tracy; Geng, Fengji; Blankenship, Sarah L; Redcay, Elizabeth

2016-06-01

Episodic memory relies on a distributed network of brain regions, with the hippocampus playing a critical and irreplaceable role. Few studies have examined how changes in this network contribute to episodic memory development early in life. The present addressed this gap by examining relations between hippocampal functional connectivity and episodic memory in 4- and 6-year-old children (n=40). Results revealed similar hippocampal functional connectivity between age groups, which included lateral temporal regions, precuneus, and multiple parietal and prefrontal regions, and functional specialization along the longitudinal axis. Despite these similarities, developmental differences were also observed. Specifically, 3 (of 4) regions within the hippocampal memory network were positively associated with episodic memory in 6-year-old children, but negatively associated with episodic memory in 4-year-old children. In contrast, all 3 regions outside the hippocampal memory network were negatively associated with episodic memory in older children, but positively associated with episodic memory in younger children. These interactions are interpreted within an interactive specialization framework and suggest the hippocampus becomes functionally integrated with cortical regions that are part of the hippocampal memory network in adults and functionally segregated from regions unrelated to memory in adults, both of which are associated with age-related improvements in episodic memory ability. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Memory and betweenness preference in temporal networks induced from time series

NASA Astrophysics Data System (ADS)

Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

2017-02-01

We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

Targeted Memory Reactivation during Sleep Adaptively Promotes the Strengthening or Weakening of Overlapping Memories.

PubMed

Oyarzún, Javiera P; Morís, Joaquín; Luque, David; de Diego-Balaguer, Ruth; Fuentemilla, Lluís

2017-08-09

System memory consolidation is conceptualized as an active process whereby newly encoded memory representations are strengthened through selective memory reactivation during sleep. However, our learning experience is highly overlapping in content (i.e., shares common elements), and memories of these events are organized in an intricate network of overlapping associated events. It remains to be explored whether and how selective memory reactivation during sleep has an impact on these overlapping memories acquired during awake time. Here, we test in a group of adult women and men the prediction that selective memory reactivation during sleep entails the reactivation of associated events and that this may lead the brain to adaptively regulate whether these associated memories are strengthened or pruned from memory networks on the basis of their relative associative strength with the shared element. Our findings demonstrate the existence of efficient regulatory neural mechanisms governing how complex memory networks are shaped during sleep as a function of their associative memory strength. SIGNIFICANCE STATEMENT Numerous studies have demonstrated that system memory consolidation is an active, selective, and sleep-dependent process in which only subsets of new memories become stabilized through their reactivation. However, the learning experience is highly overlapping in content and thus events are encoded in an intricate network of related memories. It remains to be explored whether and how memory reactivation has an impact on overlapping memories acquired during awake time. Here, we show that sleep memory reactivation promotes strengthening and weakening of overlapping memories based on their associative memory strength. These results suggest the existence of an efficient regulatory neural mechanism that avoids the formation of cluttered memory representation of multiple events and promotes stabilization of complex memory networks. Copyright © 2017 the authors 0270-6474/17/377748-11$15.00/0.

Establishing and storing of deterministic quantum entanglement among three distant atomic ensembles.

PubMed

Yan, Zhihui; Wu, Liang; Jia, Xiaojun; Liu, Yanhong; Deng, Ruijie; Li, Shujing; Wang, Hai; Xie, Changde; Peng, Kunchi

2017-09-28

It is crucial for the physical realization of quantum information networks to first establish entanglement among multiple space-separated quantum memories and then, at a user-controlled moment, to transfer the stored entanglement to quantum channels for distribution and conveyance of information. Here we present an experimental demonstration on generation, storage, and transfer of deterministic quantum entanglement among three spatially separated atomic ensembles. The off-line prepared multipartite entanglement of optical modes is mapped into three distant atomic ensembles to establish entanglement of atomic spin waves via electromagnetically induced transparency light-matter interaction. Then the stored atomic entanglement is transferred into a tripartite quadrature entangled state of light, which is space-separated and can be dynamically allocated to three quantum channels for conveying quantum information. The existence of entanglement among three released optical modes verifies that the system has the capacity to preserve multipartite entanglement. The presented protocol can be directly extended to larger quantum networks with more nodes.Continuous-variable encoding is a promising approach for quantum information and communication networks. Here, the authors show how to map entanglement from three spatial optical modes to three separated atomic samples via electromagnetically induced transparency, releasing it later on demand.

Hippocampal Network Modularity Is Associated With Relational Memory Dysfunction in Schizophrenia.

PubMed

Avery, Suzanne N; Rogers, Baxter P; Heckers, Stephan

2018-05-01

Functional dysconnectivity has been proposed as a major pathophysiological mechanism for cognitive dysfunction in schizophrenia. The hippocampus is a focal point of dysconnectivity in schizophrenia, with decreased hippocampal functional connectivity contributing to the marked memory deficits observed in patients. Normal memory function relies on the interaction of complex corticohippocampal networks. However, only recent technological advances have enabled the large-scale exploration of functional networks with accuracy and precision. We investigated the modularity of hippocampal resting-state functional networks in a sample of 45 patients with schizophrenia spectrum disorders and 38 healthy control subjects. Modularity was calculated for two distinct functional networks: a core hippocampal-medial temporal lobe cortex network and an extended hippocampal-cortical network. As hippocampal function differs along its longitudinal axis, follow-up analyses examined anterior and posterior networks separately. To explore effects of resting network function on behavior, we tested associations between modularity and relational memory ability. Age, sex, handedness, and parental education were similar between groups. Network modularity was lower in schizophrenia patients, especially in the posterior hippocampal network. Schizophrenia patients also showed markedly lower relational memory ability compared with control subjects. We found a distinct brain-behavior relationship in schizophrenia that differed from control subjects by network and anterior/posterior division-while relational memory in control subjects was associated with anterior hippocampal-cortical modularity, schizophrenia patients showed an association with posterior hippocampal-medial temporal lobe cortex network modularity. Our findings support a model of abnormal resting-state corticohippocampal network coherence in schizophrenia, which may contribute to relational memory deficits. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

The relationship between phonological memory, receptive vocabulary, and fast mapping in young children with specific language impairment.

PubMed

Gray, Shelley

2006-10-01

This study assessed the fast mapping performance of children with specific language impairment (SLI) across the preschool to kindergarten age span in relation to their phonological memory and vocabulary development. Fifty-three children diagnosed with SLI and 53 children with normal language (NL) matched for age and gender (30 three-year-olds, 18 four-year-olds, 28 five-year-olds, and 30 six-year-olds) participated. Children's phonological memory was assessed using nonword repetition and digit span tasks. Receptive vocabulary was assessed using the Peabody Picture Vocabulary Test-III. Children learned the names for 8 objects during 2 fast mapping tasks. Overall, the NL group demonstrated significantly better performance on phonological memory and vocabulary measures across the age span; however, performance on the fast mapping task differed significantly only at age 5. Phonological memory and existing receptive vocabulary did not predict fast mapping ability. The phonological memory skills of preschoolers with NL and SLI followed a similar developmental pattern, but the SLI group consistently scored significantly lower than the NL group. Overall, the NL group showed significantly better receptive vocabulary, with evidence that between-group differences increased at age 6. Neither short-term phonological memory nor receptive vocabulary predicted fast mapping comprehension or production performance, even though both have been shown to correlate with later stages of word learning.

Implementation of digital equality comparator circuit on memristive memory crossbar array using material implication logic

NASA Astrophysics Data System (ADS)

Haron, Adib; Mahdzair, Fazren; Luqman, Anas; Osman, Nazmie; Junid, Syed Abdul Mutalib Al

2018-03-01

One of the most significant constraints of Von Neumann architecture is the limited bandwidth between memory and processor. The cost to move data back and forth between memory and processor is considerably higher than the computation in the processor itself. This architecture significantly impacts the Big Data and data-intensive application such as DNA analysis comparison which spend most of the processing time to move data. Recently, the in-memory processing concept was proposed, which is based on the capability to perform the logic operation on the physical memory structure using a crossbar topology and non-volatile resistive-switching memristor technology. This paper proposes a scheme to map digital equality comparator circuit on memristive memory crossbar array. The 2-bit, 4-bit, 8-bit, 16-bit, 32-bit, and 64-bit of equality comparator circuit are mapped on memristive memory crossbar array by using material implication logic in a sequential and parallel method. The simulation results show that, for the 64-bit word size, the parallel mapping exhibits 2.8× better performance in total execution time than sequential mapping but has a trade-off in terms of energy consumption and area utilization. Meanwhile, the total crossbar area can be reduced by 1.2× for sequential mapping and 1.5× for parallel mapping both by using the overlapping technique.

Long-Term Memory Stabilized by Noise-Induced Rehearsal

PubMed Central

Wei, Yi

2014-01-01

Cortical networks can maintain memories for decades despite the short lifetime of synaptic strengths. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of ongoing spike-timing-dependent plasticity (STDP) on the stability of memory patterns stored in synapses of an attractor neural network. We show that certain classes of STDP rules can stabilize all stored memory patterns despite a short lifetime of synapses. In our model, unstructured neural noise, after passing through the recurrent network connections, carries the imprint of all memory patterns in temporal correlations. STDP, combined with these correlations, leads to reinforcement of all stored patterns, even those that are never explicitly visited. Our findings may provide the functional reason for irregular spiking displayed by cortical neurons and justify models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections. PMID:25411507

On the usefulness of 'what' and 'where' pathways in vision.

PubMed

de Haan, Edward H F; Cowey, Alan

2011-10-01

The primate visual brain is classically portrayed as a large number of separate 'maps', each dedicated to the processing of specific visual cues, such as colour, motion or faces and their many features. In order to understand this fractionated architecture, the concept of cortical 'pathways' or 'streams' was introduced. In the currently prevailing view, the different maps are organised hierarchically into two major pathways, one involved in recognition and memory (the ventral stream or 'what' pathway) and the other in the programming of action (the dorsal stream or 'where' pathway). In this review, we question this heuristically influential but potentially misleading linear hierarchical pathway model and argue instead for a 'patchwork' or network model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Black Holes as Brains: Neural Networks with Area Law Entropy

NASA Astrophysics Data System (ADS)

Dvali, Gia

2018-04-01

Motivated by the potential similarities between the underlying mechanisms of the enhanced memory storage capacity in black holes and in brain networks, we construct an artificial quantum neural network based on gravity-like synaptic connections and a symmetry structure that allows to describe the network in terms of geometry of a d-dimensional space. We show that the network possesses a critical state in which the gapless neurons emerge that appear to inhabit a (d-1)-dimensional surface, with their number given by the surface area. In the excitations of these neurons, the network can store and retrieve an exponentially large number of patterns within an arbitrarily narrow energy gap. The corresponding micro-state entropy of the brain network exhibits an area law. The neural network can be described in terms of a quantum field, via identifying the different neurons with the different momentum modes of the field, while identifying the synaptic connections among the neurons with the interactions among the corresponding momentum modes. Such a mapping allows to attribute a well-defined sense of geometry to an intrinsically non-local system, such as the neural network, and vice versa, it allows to represent the quantum field model as a neural network.

Short-Term Memory in Orthogonal Neural Networks

NASA Astrophysics Data System (ADS)

White, Olivia L.; Lee, Daniel D.; Sompolinsky, Haim

2004-04-01

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.

CLOCS (Computer with Low Context-Switching Time) Architecture Reference Documents

DTIC Science & Technology

1988-05-06

Peculiarities The only state inside the central processing unit(CPU) is a program status word. All data operations are memory to memory. One result of this... to the challenge "if I whore to design RISC, this is how I would do it." The architecture was designed by Mark Davis and Bill Gallmeister. 1.2...are memory to memory. Any special devices added should be memory mapped. The program counter is even memory mapped. 1.3.1 Working storage There is no

Motor transfer from map ocular exploration to locomotion during spatial navigation from memory.

PubMed

Demichelis, Alixia; Olivier, Gérard; Berthoz, Alain

2013-02-01

Spatial navigation from memory can rely on two different strategies: a mental simulation of a kinesthetic spatial navigation (egocentric route strategy) or visual-spatial memory using a mental map (allocentric survey strategy). We hypothesized that a previously performed "oculomotor navigation" on a map could be used by the brain to perform a locomotor memory task. Participants were instructed to (1) learn a path on a map through a sequence of vertical and horizontal eyes movements and (2) walk on the slabs of a "magic carpet" to recall this path. The main results showed that the anisotropy of ocular movements (horizontal ones being more efficient than vertical ones) influenced performances of participants when they change direction on the central slab of the magic carpet. These data suggest that, to find their way through locomotor space, subjects mentally repeated their past ocular exploration of the map, and this visuo-motor memory was used as a template for the locomotor performance.

Social Transmission of False Memory in Small Groups and Large Networks.

PubMed

Maswood, Raeya; Rajaram, Suparna

2018-05-21

Sharing information and memories is a key feature of social interactions, making social contexts important for developing and transmitting accurate memories and also false memories. False memory transmission can have wide-ranging effects, including shaping personal memories of individuals as well as collective memories of a network of people. This paper reviews a collection of key findings and explanations in cognitive research on the transmission of false memories in small groups. It also reviews the emerging experimental work on larger networks and collective false memories. Given the reconstructive nature of memory, the abundance of misinformation in everyday life, and the variety of social structures in which people interact, an understanding of transmission of false memories has both scientific and societal implications. © 2018 Cognitive Science Society, Inc.

A balanced memory network.

PubMed

Roudi, Yasser; Latham, Peter E

2007-09-01

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

Spreading activation in nonverbal memory networks.

PubMed

Foster, Paul S; Wakefield, Candias; Pryjmak, Scott; Roosa, Katelyn M; Branch, Kaylei K; Drago, Valeria; Harrison, David W; Ruff, Ronald

2017-09-01

Theories of spreading activation primarily involve semantic memory networks. However, the existence of separate verbal and visuospatial memory networks suggests that spreading activation may also occur in visuospatial memory networks. The purpose of the present investigation was to explore this possibility. Specifically, this study sought to create and describe the design frequency corpus and to determine whether this measure of visuospatial spreading activation was related to right hemisphere functioning and spreading activation in verbal memory networks. We used word frequencies taken from the Controlled Oral Word Association Test and design frequencies taken from the Ruff Figural Fluency Test as measures of verbal and visuospatial spreading activation, respectively. Average word and design frequencies were then correlated with measures of left and right cerebral functioning. The results indicated that a significant relationship exists between performance on a test of right posterior functioning (Block Design) and design frequency. A significant negative relationship also exists between spreading activation in semantic memory networks and design frequency. Based on our findings, the hypotheses were supported. Further research will need to be conducted to examine whether spreading activation exists in visuospatial memory networks as well as the parameters that might modulate this spreading activation, such as the influence of neurotransmitters.

Activation of the occipital cortex and deactivation of the default mode network during working memory in the early blind.

PubMed

Park, Hae-Jeong; Chun, Ji-Won; Park, Bumhee; Park, Haeil; Kim, Joong Il; Lee, Jong Doo; Kim, Jae-Jin

2011-05-01

Although blind people heavily depend on working memory to manage daily life without visual information, it is not clear yet whether their working memory processing involves functional reorganization of the memory-related cortical network. To explore functional reorganization of the cortical network that supports various types of working memory processes in the early blind, we investigated activation differences between 2-back tasks and 0-back tasks using fMRI in 10 congenitally blind subjects and 10 sighted subjects. We used three types of stimulus sequences: words for a verbal task, pitches for a non-verbal task, and sound locations for a spatial task. When compared to the sighted, the blind showed additional activations in the occipital lobe for all types of stimulus sequences for working memory and more significant deactivation in the posterior cingulate cortex of the default mode network. The blind had increased effective connectivity from the default mode network to the left parieto-frontal network and from the occipital cortex to the right parieto-frontal network during the 2-back tasks than the 0-back tasks. These findings suggest not only cortical plasticity of the occipital cortex but also reorganization of the cortical network for the executive control of working memory.

Neural-activity mapping of memory-based dominance in the crow: neural networks integrating individual discrimination and social behaviour control.

PubMed

Nishizawa, K; Izawa, E-I; Watanabe, S

2011-12-01

Large-billed crows (Corvus macrorhynchos), highly social birds, form stable dominance relationships based on the memory of win/loss outcomes of first encounters and on individual discrimination. This socio-cognitive behaviour predicts the existence of neural mechanisms for integration of social behaviour control and individual discrimination. This study aimed to elucidate the neural substrates of memory-based dominance in crows. First, the formation of dominance relationships was confirmed between males in a dyadic encounter paradigm. Next, we examined whether neural activities in 22 focal nuclei of pallium and subpallium were correlated with social behaviour and stimulus familiarity after exposure to dominant/subordinate familiar individuals and unfamiliar conspecifics. Neural activity was determined by measuring expression level of the immediate-early-gene (IEG) protein Zenk. Crows displayed aggressive and/or submissive behaviour to opponents less frequently but more discriminatively in subsequent encounters, suggesting stable dominance based on memory, including win/loss outcomes of the first encounters and individual discrimination. Neural correlates of aggressive and submissive behaviour were found in limbic subpallium including septum, bed nucleus of the striae terminalis (BST), and nucleus taeniae of amygdala (TnA), but also those to familiarity factor in BST and TnA. Contrastingly, correlates of social behaviour were little in pallium and those of familiarity with exposed individuals were identified in hippocampus, medial meso-/nidopallium, and ventro-caudal nidopallium. Given the anatomical connection and neural response patterns of the focal nuclei, neural networks connecting pallium and limbic subpallium via hippocampus could be involved in the integration of individual discrimination and social behaviour control in memory-based dominance in the crow. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Memory Dynamics in Cross-linked Actin Networks

NASA Astrophysics Data System (ADS)

Scheff, Danielle; Majumdar, Sayantan; Gardel, Margaret

Cells demonstrate the remarkable ability to adapt to mechanical stimuli through rearrangement of the actin cytoskeleton, a cross-linked network of actin filaments. In addition to its importance in cell biology, understanding this mechanical response provides strategies for creation of novel materials. A recent study has demonstrated that applied stress can encode mechanical memory in these networks through changes in network geometry, which gives rise to anisotropic shear response. Under later shear, the network is stiffer in the direction of the previously applied stress. However, the dynamics behind the encoding of this memory are unknown. To address this question, we explore the effect of varying either the rigidity of the cross-linkers or the length of actin filament on the time scales required for both memory encoding and over which it later decays. While previous experiments saw only a long-lived memory, initial results suggest another mechanism where memories relax relatively quickly. Overall, our study is crucial for understanding the process by which an external stress can impact network arrangement and thus the dynamics of memory formation.

Properties of a memory network in psychology

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

Wedemann, Roseli S.; Donangelo, Raul; Carvalho, Luis A. V. de

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory.

Properties of a memory network in psychology

NASA Astrophysics Data System (ADS)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2007-12-01

We have previously described neurotic psychopathology and psychoanalytic working-through by an associative memory mechanism, based on a neural network model, where memory was modelled by a Boltzmann machine (BM). Since brain neural topology is selectively structured, we simulated known microscopic mechanisms that control synaptic properties, showing that the network self-organizes to a hierarchical, clustered structure. Here, we show some statistical mechanical properties of the complex networks which result from this self-organization. They indicate that a generalization of the BM may be necessary to model memory.

System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

PubMed

Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

2014-05-01

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

Memory-induced nonlinear dynamics of excitation in cardiac diseases.

PubMed

Landaw, Julian; Qu, Zhilin

2018-04-01

Excitable cells, such as cardiac myocytes, exhibit short-term memory, i.e., the state of the cell depends on its history of excitation. Memory can originate from slow recovery of membrane ion channels or from accumulation of intracellular ion concentrations, such as calcium ion or sodium ion concentration accumulation. Here we examine the effects of memory on excitation dynamics in cardiac myocytes under two diseased conditions, early repolarization and reduced repolarization reserve, each with memory from two different sources: slow recovery of a potassium ion channel and slow accumulation of the intracellular calcium ion concentration. We first carry out computer simulations of action potential models described by differential equations to demonstrate complex excitation dynamics, such as chaos. We then develop iterated map models that incorporate memory, which accurately capture the complex excitation dynamics and bifurcations of the action potential models. Finally, we carry out theoretical analyses of the iterated map models to reveal the underlying mechanisms of memory-induced nonlinear dynamics. Our study demonstrates that the memory effect can be unmasked or greatly exacerbated under certain diseased conditions, which promotes complex excitation dynamics, such as chaos. The iterated map models reveal that memory converts a monotonic iterated map function into a nonmonotonic one to promote the bifurcations leading to high periodicity and chaos.

Memory-induced nonlinear dynamics of excitation in cardiac diseases

NASA Astrophysics Data System (ADS)

Landaw, Julian; Qu, Zhilin

2018-04-01

Excitable cells, such as cardiac myocytes, exhibit short-term memory, i.e., the state of the cell depends on its history of excitation. Memory can originate from slow recovery of membrane ion channels or from accumulation of intracellular ion concentrations, such as calcium ion or sodium ion concentration accumulation. Here we examine the effects of memory on excitation dynamics in cardiac myocytes under two diseased conditions, early repolarization and reduced repolarization reserve, each with memory from two different sources: slow recovery of a potassium ion channel and slow accumulation of the intracellular calcium ion concentration. We first carry out computer simulations of action potential models described by differential equations to demonstrate complex excitation dynamics, such as chaos. We then develop iterated map models that incorporate memory, which accurately capture the complex excitation dynamics and bifurcations of the action potential models. Finally, we carry out theoretical analyses of the iterated map models to reveal the underlying mechanisms of memory-induced nonlinear dynamics. Our study demonstrates that the memory effect can be unmasked or greatly exacerbated under certain diseased conditions, which promotes complex excitation dynamics, such as chaos. The iterated map models reveal that memory converts a monotonic iterated map function into a nonmonotonic one to promote the bifurcations leading to high periodicity and chaos.

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

PubMed Central

Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

2012-01-01

Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise. PMID:22276205

States of mind: emotions, body feelings, and thoughts share distributed neural networks.

PubMed

Oosterwijk, Suzanne; Lindquist, Kristen A; Anderson, Eric; Dautoff, Rebecca; Moriguchi, Yoshiya; Barrett, Lisa Feldman

2012-09-01

Scientists have traditionally assumed that different kinds of mental states (e.g., fear, disgust, love, memory, planning, concentration, etc.) correspond to different psychological faculties that have domain-specific correlates in the brain. Yet, growing evidence points to the constructionist hypothesis that mental states emerge from the combination of domain-general psychological processes that map to large-scale distributed brain networks. In this paper, we report a novel study testing a constructionist model of the mind in which participants generated three kinds of mental states (emotions, body feelings, or thoughts) while we measured activity within large-scale distributed brain networks using fMRI. We examined the similarity and differences in the pattern of network activity across these three classes of mental states. Consistent with a constructionist hypothesis, a combination of large-scale distributed networks contributed to emotions, thoughts, and body feelings, although these mental states differed in the relative contribution of those networks. Implications for a constructionist functional architecture of diverse mental states are discussed. Copyright © 2012 Elsevier Inc. All rights reserved.

Double-Labeled Metabolic Maps of Memory.

ERIC Educational Resources Information Center

John, E. R.; And Others

1986-01-01

Reviews a study which sought to obtain a quantitative metabolic map of the neurons mediating a specific memory. Research results support notions of cooperative processes in which nonrandom behavior of high ensembles of neural elements mediates the integration and processing of information and the retrieval of memory. (ML)

To search or to like: Mapping fixations to differentiate two forms of incidental scene memory.

PubMed

Choe, Kyoung Whan; Kardan, Omid; Kotabe, Hiroki P; Henderson, John M; Berman, Marc G

2017-10-01

We employed eye-tracking to investigate how performing different tasks on scenes (e.g., intentionally memorizing them, searching for an object, evaluating aesthetic preference) can affect eye movements during encoding and subsequent scene memory. We found that scene memorability decreased after visual search (one incidental encoding task) compared to intentional memorization, and that preference evaluation (another incidental encoding task) produced better memory, similar to the incidental memory boost previously observed for words and faces. By analyzing fixation maps, we found that although fixation map similarity could explain how eye movements during visual search impairs incidental scene memory, it could not explain the incidental memory boost from aesthetic preference evaluation, implying that implicit mechanisms were at play. We conclude that not all incidental encoding tasks should be taken to be similar, as different mechanisms (e.g., explicit or implicit) lead to memory enhancements or decrements for different incidental encoding tasks.

Electronic implementation of associative memory based on neural network models

NASA Technical Reports Server (NTRS)

Moopenn, A.; Lambe, John; Thakoor, A. P.

1987-01-01

An electronic embodiment of a neural network based associative memory in the form of a binary connection matrix is described. The nature of false memory errors, their effect on the information storage capacity of binary connection matrix memories, and a novel technique to eliminate such errors with the help of asymmetrical extra connections are discussed. The stability of the matrix memory system incorporating a unique local inhibition scheme is analyzed in terms of local minimization of an energy function. The memory's stability, dynamic behavior, and recall capability are investigated using a 32-'neuron' electronic neural network memory with a 1024-programmable binary connection matrix.

Long-term memory stabilized by noise-induced rehearsal.

PubMed

Wei, Yi; Koulakov, Alexei A

2014-11-19

Cortical networks can maintain memories for decades despite the short lifetime of synaptic strengths. Can a neural network store long-lasting memories in unstable synapses? Here, we study the effects of ongoing spike-timing-dependent plasticity (STDP) on the stability of memory patterns stored in synapses of an attractor neural network. We show that certain classes of STDP rules can stabilize all stored memory patterns despite a short lifetime of synapses. In our model, unstructured neural noise, after passing through the recurrent network connections, carries the imprint of all memory patterns in temporal correlations. STDP, combined with these correlations, leads to reinforcement of all stored patterns, even those that are never explicitly visited. Our findings may provide the functional reason for irregular spiking displayed by cortical neurons and justify models of system memory consolidation. Therefore, we propose that irregular neural activity is the feature that helps cortical networks maintain stable connections. Copyright © 2014 the authors 0270-6474/14/3415804-12$15.00/0.

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks

PubMed Central

Ruiz-Rizzo, Adriana L.; Neitzel, Julia; Müller, Hermann J.; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity. PMID:29662444

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks.

PubMed

Ruiz-Rizzo, Adriana L; Neitzel, Julia; Müller, Hermann J; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's "theory of visual attention" (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity.

Optical interconnection network for parallel access to multi-rank memory in future computing systems.

PubMed

Wang, Kang; Gu, Huaxi; Yang, Yintang; Wang, Kun

2015-08-10

With the number of cores increasing, there is an emerging need for a high-bandwidth low-latency interconnection network, serving core-to-memory communication. In this paper, aiming at the goal of simultaneous access to multi-rank memory, we propose an optical interconnection network for core-to-memory communication. In the proposed network, the wavelength usage is delicately arranged so that cores can communicate with different ranks at the same time and broadcast for flow control can be achieved. A distributed memory controller architecture that works in a pipeline mode is also designed for efficient optical communication and transaction address processes. The scaling method and wavelength assignment for the proposed network are investigated. Compared with traditional electronic bus-based core-to-memory communication, the simulation results based on the PARSEC benchmark show that the bandwidth enhancement and latency reduction are apparent.

Associative memory in phasing neuron networks

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

Nair, Niketh S; Bochove, Erik J.; Braiman, Yehuda

2014-01-01

We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

Spreading activation in emotional memory networks and the cumulative effects of somatic markers.

PubMed

Foster, Paul S; Hubbard, Tyler; Campbell, Ransom W; Poole, Jonathan; Pridmore, Michael; Bell, Chris; Harrison, David W

2017-06-01

The theory of spreading activation proposes that the activation of a semantic memory node may spread along bidirectional associative links to other related nodes. Although this theory was originally proposed to explain semantic memory networks, a similar process may be said to exist with episodic or emotional memory networks. The Somatic Marker hypothesis proposes that remembering an emotional memory activates the somatic sensations associated with the memory. An integration of these two models suggests that as spreading activation in emotional memory networks increases, a greater number of associated somatic markers would become activated. This process would then result in greater changes in physiological functioning. We sought to investigate this possibility by having subjects recall words associated with sad and happy memories, in addition to a neutral condition. The average ages of the memories and the number of word memories recalled were then correlated with measures of heart rate and skin conductance. The results indicated significant positive correlations between the number of happy word memories and heart rate (r = .384, p = .022) and between the average ages of the sad memories and skin conductance (r = .556, p = .001). Unexpectedly, a significant negative relationship was found between the number of happy word memories and skin conductance (r = -.373, p = .025). The results provide partial support for our hypothesis, indicating that increasing spreading activation in emotional memory networks activates an increasing number of somatic markers and this is then reflected in greater physiological activity at the time of recalling the memories.

Visuospatial working memory in very preterm and term born children--impact of age and performance.

PubMed

Mürner-Lavanchy, I; Ritter, B C; Spencer-Smith, M M; Perrig, W J; Schroth, G; Steinlin, M; Everts, R

2014-07-01

Working memory is crucial for meeting the challenges of daily life and performing academic tasks, such as reading or arithmetic. Very preterm born children are at risk of low working memory capacity. The aim of this study was to examine the visuospatial working memory network of school-aged preterm children and to determine the effect of age and performance on the neural working memory network. Working memory was assessed in 41 very preterm born children and 36 term born controls (aged 7-12 years) using functional magnetic resonance imaging (fMRI) and neuropsychological assessment. While preterm children and controls showed equal working memory performance, preterm children showed less involvement of the right middle frontal gyrus, but higher fMRI activation in superior frontal regions than controls. The younger and low-performing preterm children presented an atypical working memory network whereas the older high-performing preterm children recruited a working memory network similar to the controls. Results suggest that younger and low-performing preterm children show signs of less neural efficiency in frontal brain areas. With increasing age and performance, compensational mechanisms seem to occur, so that in preterm children, the typical visuospatial working memory network is established by the age of 12 years. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Semantic networks for odors and colors in Alzheimer's disease.

PubMed

Razani, Jill; Chan, Agnes; Nordin, Steven; Murphy, Claire

2010-05-01

Impairment in odor-naming ability and in verbal and visual semantic networks raised the hypothesis of a breakdown in the semantic network for odors in Alzheimer's disease (AD). The current study addressed this hypothesis. Twenty-four individuals, half patients with probable AD and half control participants, performed triadic-similarity judgments for odors and colors, separately, which, utilizing the multidimensional scaling (MDS) technique of individual difference scaling analysis (INDSCAL), generated two-dimensional configurations of similarity. The abilities to match odors and colors with written name labels were assessed to investigate disease-related differences in ability to identify and conceptualize the stimuli. In addition, responses on attribute-sorting tasks, requiring the odor and color perceptions to be categorized as one polarity of a certain dimension, were obtained to allow for objective interpretation of the MDS spatial maps. Whereas comparison subjects generated spatial maps based predominantly on relatively abstract characteristics, patients with AD classified odors on perceptual characteristics. The maps for patients with AD also showed disorganized groupings and loose associations between odors. Their normal configurations for colors imply that the patients were able to comprehend the task per se. The data for label matching and for attribute sorting provide further evidence for a disturbance in semantic odor memory in AD. The patients performed poorer than controls on both these odor tasks, implying that the ability to identify and/or conceptualize odors is impaired in AD. The results provide clear evidence for deterioration of the structure of semantic knowledge for odors in AD.

Lesion mapping of social problem solving

PubMed Central

Colom, Roberto; Paul, Erick J.; Chau, Aileen; Solomon, Jeffrey; Grafman, Jordan H.

2014-01-01

Accumulating neuroscience evidence indicates that human intelligence is supported by a distributed network of frontal and parietal regions that enable complex, goal-directed behaviour. However, the contributions of this network to social aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 144) that investigates the neural bases of social problem solving (measured by the Everyday Problem Solving Inventory) and examine the degree to which individual differences in performance are predicted by a broad spectrum of psychological variables, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores for each variable were obtained, followed by voxel-based lesion–symptom mapping. Stepwise regression analyses revealed that working memory, processing speed, and emotional intelligence predict individual differences in everyday problem solving. A targeted analysis of specific everyday problem solving domains (involving friends, home management, consumerism, work, information management, and family) revealed psychological variables that selectively contribute to each. Lesion mapping results indicated that social problem solving, psychometric intelligence, and emotional intelligence are supported by a shared network of frontal, temporal, and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The results support an integrative framework for understanding social intelligence and make specific recommendations for the application of the Everyday Problem Solving Inventory to the study of social problem solving in health and disease. PMID:25070511

`Unlearning' has a stabilizing effect in collective memories

NASA Astrophysics Data System (ADS)

Hopfield, J. J.; Feinstein, D. I.; Palmer, R. G.

1983-07-01

Crick and Mitchison1 have presented a hypothesis for the functional role of dream sleep involving an `unlearning' process. We have independently carried out mathematical and computer modelling of learning and `unlearning' in a collective neural network of 30-1,000 neurones. The model network has a content-addressable memory or `associative memory' which allows it to learn and store many memories. A particular memory can be evoked in its entirety when the network is stimulated by any adequate-sized subpart of the information of that memory2. But different memories of the same size are not equally easy to recall. Also, when memories are learned, spurious memories are also created and can also be evoked. Applying an `unlearning' process, similar to the learning processes but with a reversed sign and starting from a noise input, enhances the performance of the network in accessing real memories and in minimizing spurious ones. Although our model was not motivated by higher nervous function, our system displays behaviours which are strikingly parallel to those needed for the hypothesized role of `unlearning' in rapid eye movement (REM) sleep.

Dopamine D1 signaling organizes network dynamics underlying working memory

PubMed Central

Roffman, Joshua L.; Tanner, Alexandra S.; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J.; Ho, New Fei; Nitenson, Adam Z.; Chonde, Daniel B.; Greve, Douglas N.; Abi-Dargham, Anissa; Buckner, Randy L.; Manoach, Dara S.; Rosen, Bruce R.; Hooker, Jacob M.; Catana, Ciprian

2016-01-01

Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography–magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory–emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits. PMID:27386561

A revised limbic system model for memory, emotion and behaviour.

PubMed

Catani, Marco; Dell'acqua, Flavio; Thiebaut de Schotten, Michel

2013-09-01

Emotion, memories and behaviour emerge from the coordinated activities of regions connected by the limbic system. Here, we propose an update of the limbic model based on the seminal work of Papez, Yakovlev and MacLean. In the revised model we identify three distinct but partially overlapping networks: (i) the Hippocampal-diencephalic and parahippocampal-retrosplenial network dedicated to memory and spatial orientation; (ii) The temporo-amygdala-orbitofrontal network for the integration of visceral sensation and emotion with semantic memory and behaviour; (iii) the default-mode network involved in autobiographical memories and introspective self-directed thinking. The three networks share cortical nodes that are emerging as principal hubs in connectomic analysis. This revised network model of the limbic system reconciles recent functional imaging findings with anatomical accounts of clinical disorders commonly associated with limbic pathology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Recurrent Network models of sequence generation and memory

PubMed Central

Rajan, Kanaka; Harvey, Christopher D; Tank, David W

2016-01-01

SUMMARY Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here, we demonstrate that starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network training (PINning), to model and match cellular-resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced choice task [Harvey, Coen and Tank, 2012]. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures. PMID:26971945

Invetigation of Travelling Ionospheric Disturbances during the Memorial Day Weekend Geomagnetic Storm of 27 - 28 OF may, 2017 Over North America.

NASA Astrophysics Data System (ADS)

Jonah, O. F.; Coster, A. J.; Zhang, S.; Goncharenko, L. P.; Bhatt, A.; Kendall, E. A.

2017-12-01

Using the large GNSS network over North America (70 - 125oW and 25 - 50oN) with a spatial resolution of 0.1 x 0.1o in latitude and longitude and temporal resolution of 1min, we investigate the ionospheric perturbations associated with Traveling Ionospheric Disturbances (TIDs) on 2017 Memorial Day weekend's geomagnetic storm that occurred on the 27 and 28 of May, 2017. Our results were compared with images from the Mid-latitude All-Sky-imager Network for GeoSpace Observation (MANGO) and radio occultation satellite data. The storm was intense, featuring a southward interplanetary field (Bz) below 20 nT for about 4 hr, with a strong increase in the AE from 200 to 1300 nT and the Dst and kp indies were below 120 nT, and above 7 units, respectively. Both medium and large scale TIDs were observed, and their velocity, wavelength, wavefront and period throughout the storm are analyzed. Results show the presence of both poleward and equator-ward propagation of the TIDs. The features of TIDs obtained from keograms and 2-D TEC maps from GNSS-TEC are compared with those obtained from the all-sky imaging system during storm period. Finally, we compared the behavior of TIDs during Memorial Day weekend geomagnetic storm with the TIDs during the May quiet periods.

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.

PubMed

Molenaar, Peter C M; Beltz, Adriene M; Gates, Kathleen M; Wilson, Stephen J

2016-01-15

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. Published by Elsevier Inc.

Places, Spaces and Memory Traces: Showing Students with Learning Disabilities Ways to Remember Locations and Events on Maps.

ERIC Educational Resources Information Center

Brigham, Frederick J.

This study examined the memory-enhancing effects of elaborative and mnemonic encoding of information presented with maps, compared to more traditional, non-mnemonic maps, on recall of locations of events and information associated with those events by 72 middle school students with learning disabilities. Subjects were presented with map-like…

The Future of Memory: Remembering, Imagining, and the Brain

PubMed Central

Schacter, Daniel L.; Addis, Donna Rose; Hassabis, Demis; Martin, Victoria C.; Spreng, R. Nathan; Szpunar, Karl K.

2013-01-01

During the past few years, there has been a dramatic increase in research examining the role of memory in imagination and future thinking. This work has revealed striking similarities between remembering the past and imagining or simulating the future, including the finding that a common brain network underlies both memory and imagination. Here we discuss a number of key points that have emerged during recent years, focusing in particular on the importance of distinguishing between temporal and non-temporal factors in analyses of memory and imagination, the nature of differences between remembering the past and imagining the future, the identification of component processes that comprise the default network supporting memory-based simulations, and the finding that this network can couple flexibly with other networks to support complex goal-directed simulations. This growing area of research has broadened our conception of memory by highlighting the many ways in which memory supports adaptive functioning. PMID:23177955

Distortions in memory for visual displays

NASA Technical Reports Server (NTRS)

Tversky, Barbara

1989-01-01

Systematic errors in perception and memory present a challenge to theories of perception and memory and to applied psychologists interested in overcoming them as well. A number of systematic errors in memory for maps and graphs are reviewed, and they are accounted for by an analysis of the perceptual processing presumed to occur in comprehension of maps and graphs. Visual stimuli, like verbal stimuli, are organized in comprehension and memory. For visual stimuli, the organization is a consequence of perceptual processing, which is bottom-up or data-driven in its earlier stages, but top-down and affected by conceptual knowledge later on. Segregation of figure from ground is an early process, and figure recognition later; for both, symmetry is a rapidly detected and ecologically valid cue. Once isolated, figures are organized relative to one another and relative to a frame of reference. Both perceptual (e.g., salience) and conceptual factors (e.g., significance) seem likely to affect selection of a reference frame. Consistent with the analysis, subjects perceived and remembered curves in graphs and rivers in maps as more symmetric than they actually were. Symmetry, useful for detecting and recognizing figures, distorts map and graph figures alike. Top-down processes also seem to operate in that calling attention to the symmetry vs. asymmetry of a slightly asymmetric curve yielded memory errors in the direction of the description. Conceptual frame of reference effects were demonstrated in memory for lines embedded in graphs. In earlier work, the orientation of map figures was distorted in memory toward horizontal or vertical. In recent work, graph lines, but not map lines, were remembered as closer to an imaginary 45 deg line than they had been. Reference frames are determined by both perceptual and conceptual factors, leading to selection of the canonical axes as a reference frame in maps, but selection of the imaginary 45 deg as a reference frame in graphs.

Default Mode Network Interference in Mild Traumatic Brain Injury – A Pilot Resting State Study

PubMed Central

Sours, Chandler; Zhuo, Jiachen; Janowich, Jacqueline; Aarabi, Bizhan; Shanmuganathan, Kathirkamanthan; Gullapalli, Rao P

2013-01-01

In this study we investigated the functional connectivity in 23 Mild TBI (mTBI) patients with and without memory complaints using resting state fMRI in the sub-acute stage of injury as well as a group of control participants. Results indicate that mTBI patients with memory complaints performed significantly worse than patients without memory complaints on tests assessing memory from the Automated Neuropsychological Assessment Metrics (ANAM). Altered functional connectivity was observed between the three groups between the default mode network (DMN) and the nodes of the task positive network (TPN). Altered functional connectivity was also observed between both the TPN and DMN and nodes associated with the Salience Network (SN). Following mTBI there is a reduction in anti-correlated networks for both those with and without memory complaints for the DMN, but only a reduction in the anti-correlated network in mTBI patients with memory complaints for the TPN. Furthermore, an increased functional connectivity between the TPN and SN appears to be associated with reduced performance on memory assessments. Overall the results suggest that a disruption in the segregation of the DMN and the TPN at rest may be mediated through both a direct pathway of increased FC between various nodes of the TPN and DMN, and through an indirect pathway that links the TPN and DMN through nodes of the SN. This disruption between networks may cause a detrimental impact on memory functioning following mTBI, supporting the Default Mode Interference Hypothesis in the context of mTBI related memory deficits. PMID:23994210

Default mode network interference in mild traumatic brain injury - a pilot resting state study.

PubMed

Sours, Chandler; Zhuo, Jiachen; Janowich, Jacqueline; Aarabi, Bizhan; Shanmuganathan, Kathirkamanthan; Gullapalli, Rao P

2013-11-06

In this study we investigated the functional connectivity in 23 Mild TBI (mTBI) patients with and without memory complaints using resting state fMRI in the sub-acute stage of injury as well as a group of control participants. Results indicate that mTBI patients with memory complaints performed significantly worse than patients without memory complaints on tests assessing memory from the Automated Neuropsychological Assessment Metrics (ANAM). Altered functional connectivity was observed between the three groups between the default mode network (DMN) and the nodes of the task positive network (TPN). Altered functional connectivity was also observed between both the TPN and DMN and nodes associated with the Salience Network (SN). Following mTBI there is a reduction in anti-correlated networks for both those with and without memory complaints for the DMN, but only a reduction in the anti-correlated network in mTBI patients with memory complaints for the TPN. Furthermore, an increased functional connectivity between the TPN and SN appears to be associated with reduced performance on memory assessments. Overall the results suggest that a disruption in the segregation of the DMN and the TPN at rest may be mediated through both a direct pathway of increased FC between various nodes of the TPN and DMN, and through an indirect pathway that links the TPN and DMN through nodes of the SN. This disruption between networks may cause a detrimental impact on memory functioning following mTBI, supporting the Default Mode Interference Hypothesis in the context of mTBI related memory deficits. © 2013 Elsevier B.V. All rights reserved.

Networks of Memories

DTIC Science & Technology

2013-03-01

2000). The construction of  autobiographical   memories in the self­memory system. Psychological Review, 107(2), 261­288. Dennis, S., & Chapman, A. (2010...AFRL-OSR-VA-TR-2013-0131 Networks of Memories Simon Dennis, Mikhail Belkin Ohio State University March 2013 Final...Back (Rev. 8/98) 1 Networks of  Memories FA9550­09­1­0614 Professor Jay Myung PI: Simon Dennis Ohio State University February 15, 2013 2 Introduction

Self-organization and solution of shortest-path optimization problems with memristive networks

NASA Astrophysics Data System (ADS)

Pershin, Yuriy V.; Di Ventra, Massimiliano

2013-07-01

We show that memristive networks, namely networks of resistors with memory, can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time nonlocality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.

Resonator memories and optical novelty filters

NASA Astrophysics Data System (ADS)

Anderson, Dana Z.; Erle, Marie C.

Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive materials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydreaming" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.

Resonator Memories And Optical Novelty Filters

NASA Astrophysics Data System (ADS)

Anderson, Dana Z.; Erie, Marie C.

1987-05-01

Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content-addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive ma-terials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydream-ing" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.

Spiking neural network simulation: memory-optimal synaptic event scheduling.

PubMed

Stewart, Robert D; Gurney, Kevin N

2011-06-01

Spiking neural network simulations incorporating variable transmission delays require synaptic events to be scheduled prior to delivery. Conventional methods have memory requirements that scale with the total number of synapses in a network. We introduce novel scheduling algorithms for both discrete and continuous event delivery, where the memory requirement scales instead with the number of neurons. Superior algorithmic performance is demonstrated using large-scale, benchmarking network simulations.

A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

PubMed

Zhou, Caigen; Zeng, Xiaoqin; Luo, Chaomin; Zhang, Huaguang

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity. The effectiveness, robustness, and fault-tolerant capability are validated by simulated experiments.

Variation across individuals and items determine learning outcomes from fast mapping.

PubMed

Coutanche, Marc N; Koch, Griffin E

2017-11-01

An approach to learning words known as "fast mapping" has been linked to unique neurobiological and behavioral markers in adult humans, including rapid lexical integration. However, the mechanisms supporting fast mapping are still not known. In this study, we sought to help change this by examining factors that modulate learning outcomes. In 90 subjects, we systematically manipulated the typicality of the items used to support fast mapping (foils), and quantified learners' inclination to employ semantic, episodic, and spatial memory through the Survey of Autobiographical Memory (SAM). We asked how these factors affect lexical competition and recognition performance, and then asked how foil typicality and lexical competition are related in an independent dataset. We find that both the typicality of fast mapping foils, and individual differences in how different memory systems are employed, influence lexical competition effects after fast mapping, but not after other learning approaches. Specifically, learning a word through fast mapping with an atypical foil led to lexical competition, while a typical foil led to lexical facilitation. This effect was particularly evident in individuals with a strong tendency to employ semantic memory. We further replicated the relationship between continuous foil atypicality and lexical competition in an independent dataset. These findings suggest that semantic properties of the foils that support fast mapping can influence the degree and nature of subsequent lexical integration. Further, the effects of foils differ based on an individual's tendency to draw-on the semantic memory system. Copyright © 2017 Elsevier Ltd. All rights reserved.

Brain and effort: brain activation and effort-related working memory in healthy participants and patients with working memory deficits.

PubMed

Engström, Maria; Landtblom, Anne-Marie; Karlsson, Thomas

2013-01-01

Despite the interest in the neuroimaging of working memory, little is still known about the neurobiology of complex working memory in tasks that require simultaneous manipulation and storage of information. In addition to the central executive network, we assumed that the recently described salience network [involving the anterior insular cortex (AIC) and the anterior cingulate cortex (ACC)] might be of particular importance to working memory tasks that require complex, effortful processing. Healthy participants (n = 26) and participants suffering from working memory problems related to the Kleine-Levin syndrome (KLS) (a specific form of periodic idiopathic hypersomnia; n = 18) participated in the study. Participants were further divided into a high- and low-capacity group, according to performance on a working memory task (listening span). In a functional magnetic resonance imaging (fMRI) study, participants were administered the reading span complex working memory task tapping cognitive effort. The fMRI-derived blood oxygen level dependent (BOLD) signal was modulated by (1) effort in both the central executive and the salience network and (2) capacity in the salience network in that high performers evidenced a weaker BOLD signal than low performers. In the salience network there was a dichotomy between the left and the right hemisphere; the right hemisphere elicited a steeper increase of the BOLD signal as a function of increasing effort. There was also a stronger functional connectivity within the central executive network because of increased task difficulty. The ability to allocate cognitive effort in complex working memory is contingent upon focused resources in the executive and in particular the salience network. Individual capacity during the complex working memory task is related to activity in the salience (but not the executive) network so that high-capacity participants evidence a lower signal and possibly hence a larger dynamic response.

Brain and effort: brain activation and effort-related working memory in healthy participants and patients with working memory deficits

PubMed Central

Engström, Maria; Landtblom, Anne-Marie; Karlsson, Thomas

2013-01-01

Despite the interest in the neuroimaging of working memory, little is still known about the neurobiology of complex working memory in tasks that require simultaneous manipulation and storage of information. In addition to the central executive network, we assumed that the recently described salience network [involving the anterior insular cortex (AIC) and the anterior cingulate cortex (ACC)] might be of particular importance to working memory tasks that require complex, effortful processing. Method: Healthy participants (n = 26) and participants suffering from working memory problems related to the Kleine–Levin syndrome (KLS) (a specific form of periodic idiopathic hypersomnia; n = 18) participated in the study. Participants were further divided into a high- and low-capacity group, according to performance on a working memory task (listening span). In a functional magnetic resonance imaging (fMRI) study, participants were administered the reading span complex working memory task tapping cognitive effort. Principal findings: The fMRI-derived blood oxygen level dependent (BOLD) signal was modulated by (1) effort in both the central executive and the salience network and (2) capacity in the salience network in that high performers evidenced a weaker BOLD signal than low performers. In the salience network there was a dichotomy between the left and the right hemisphere; the right hemisphere elicited a steeper increase of the BOLD signal as a function of increasing effort. There was also a stronger functional connectivity within the central executive network because of increased task difficulty. Conclusion: The ability to allocate cognitive effort in complex working memory is contingent upon focused resources in the executive and in particular the salience network. Individual capacity during the complex working memory task is related to activity in the salience (but not the executive) network so that high-capacity participants evidence a lower signal and possibly hence a larger dynamic response. PMID:23616756

Modeling of cortical signals using echo state networks

NASA Astrophysics Data System (ADS)

Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

2009-10-01

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

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level.

PubMed

Coman, Alin; Momennejad, Ida; Drach, Rae D; Geana, Andra

2016-07-19

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members' memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.

A deep learning framework for causal shape transformation.

PubMed

Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

2018-02-01

Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structural covariance mapping delineates medial and medio-lateral temporal networks in déjà vu.

PubMed

Shaw, Daniel Joel; Mareček, Radek; Brázdil, Milan

2016-12-01

Déjà vu (DV) is an eerie phenomenon experienced frequently as an aura of temporal lobe epilepsy, but also reported commonly by healthy individuals. The former pathological manifestation appears to result from aberrant neural activity among brain structures within the medial temporal lobes. Recent studies also implicate medial temporal brain structures in the non-pathological experience of DV, but as one element of a diffuse neuroanatomical correlate; it remains to be seen if neural activity among the medial temporal lobes also underlies this benign manifestation. The present study set out to investigate this. Due to its unpredictable and infrequent occurrence, however, non-pathological DV does not lend itself easily to functional neuroimaging. Instead, we draw on research showing that brain structure covaries among regions that interact frequently as nodes of functional networks. Specifically, we assessed whether grey-matter covariance among structures implicated in non-pathological DV differs according to the frequency with which the phenomenon is experienced. This revealed two diverging patterns of structural covariation: Among the first, comprised primarily of medial temporal structures and the caudate, grey-matter volume becomes more positively correlated with higher frequency of DV experience. The second pattern encompasses medial and lateral temporal structures, among which greater DV frequency is associated with more negatively correlated grey matter. Using a meta-analytic method of co-activation mapping, we demonstrate a higher probability of functional interactions among brain structures constituting the former pattern, particularly during memory-related processes. Our findings suggest that altered neural signalling within memory-related medial temporal brain structures underlies both pathological and non-pathological DV.

Algorithms for Automatic Alignment of Arrays

NASA Technical Reports Server (NTRS)

Chatterjee, Siddhartha; Gilbert, John R.; Oliker, Leonid; Schreiber, Robert; Sheffler, Thomas J.

1996-01-01

Aggregate data objects (such as arrays) are distributed across the processor memories when compiling a data-parallel language for a distributed-memory machine. The mapping determines the amount of communication needed to bring operands of parallel operations into alignment with each other. A common approach is to break the mapping into two stages: an alignment that maps all the objects to an abstract template, followed by a distribution that maps the template to the processors. This paper describes algorithms for solving the various facets of the alignment problem: axis and stride alignment, static and mobile offset alignment, and replication labeling. We show that optimal axis and stride alignment is NP-complete for general program graphs, and give a heuristic method that can explore the space of possible solutions in a number of ways. We show that some of these strategies can give better solutions than a simple greedy approach proposed earlier. We also show how local graph contractions can reduce the size of the problem significantly without changing the best solution. This allows more complex and effective heuristics to be used. We show how to model the static offset alignment problem using linear programming, and we show that loop-dependent mobile offset alignment is sometimes necessary for optimum performance. We describe an algorithm with for determining mobile alignments for objects within do loops. We also identify situations in which replicated alignment is either required by the program itself or can be used to improve performance. We describe an algorithm based on network flow that replicates objects so as to minimize the total amount of broadcast communication in replication.

Visual Fast Mapping in School-Aged Children with Specific Language Impairment

ERIC Educational Resources Information Center

Alt, Mary

2013-01-01

Purpose: To determine whether children with specific language impairment (SLI) demonstrate impaired visual fast mapping skills compared with unimpaired peers and to test components of visual working memory that may contribute to a visual working memory deficit. Methods: Fifty children (25 SLI) played 2 computer-based visual fast mapping games…

An Examination of the Effects of Argument Mapping on Students' Memory and Comprehension Performance

ERIC Educational Resources Information Center

Dwyer, Christopher P.; Hogan, Michael J.; Stewart, Ian

2013-01-01

Argument mapping (AM) is a method of visually diagramming arguments to allow for easy comprehension of core statements and relations. A series of three experiments compared argument map reading and construction with hierarchical outlining, text summarisation, and text reading as learning methods by examining subsequent memory and comprehension…

Extended write combining using a write continuation hint flag

DOEpatents

Chen, Dong; Gara, Alan; Heidelberger, Philip; Ohmacht, Martin; Vranas, Pavlos

2013-06-04

A computing apparatus for reducing the amount of processing in a network computing system which includes a network system device of a receiving node for receiving electronic messages comprising data. The electronic messages are transmitted from a sending node. The network system device determines when more data of a specific electronic message is being transmitted. A memory device stores the electronic message data and communicating with the network system device. A memory subsystem communicates with the memory device. The memory subsystem stores a portion of the electronic message when more data of the specific message will be received, and the buffer combines the portion with later received data and moves the data to the memory device for accessible storage.

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization

PubMed Central

2017-01-01

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity—a measure of network segregation—is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control network and default mode network strengthen their interaction with one another during episodic retrieval. Such across-network communication likely facilitates effective access to internally generated representations of past event knowledge. PMID:28242796

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization.

PubMed

Westphal, Andrew J; Wang, Siliang; Rissman, Jesse

2017-03-29

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control network and default mode network strengthen their interaction with one another during episodic retrieval. Such across-network communication likely facilitates effective access to internally generated representations of past event knowledge. Copyright © 2017 the authors 0270-6474/17/373523-09$15.00/0.

Functional Connectivity of Multiple Brain Regions Required for the Consolidation of Social Recognition Memory.

PubMed

Tanimizu, Toshiyuki; Kenney, Justin W; Okano, Emiko; Kadoma, Kazune; Frankland, Paul W; Kida, Satoshi

2017-04-12

Social recognition memory is an essential and basic component of social behavior that is used to discriminate familiar and novel animals/humans. Previous studies have shown the importance of several brain regions for social recognition memories; however, the mechanisms underlying the consolidation of social recognition memory at the molecular and anatomic levels remain unknown. Here, we show a brain network necessary for the generation of social recognition memory in mice. A mouse genetic study showed that cAMP-responsive element-binding protein (CREB)-mediated transcription is required for the formation of social recognition memory. Importantly, significant inductions of the CREB target immediate-early genes c-fos and Arc were observed in the hippocampus (CA1 and CA3 regions), medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and amygdala (basolateral region) when social recognition memory was generated. Pharmacological experiments using a microinfusion of the protein synthesis inhibitor anisomycin showed that protein synthesis in these brain regions is required for the consolidation of social recognition memory. These findings suggested that social recognition memory is consolidated through the activation of CREB-mediated gene expression in the hippocampus/mPFC/ACC/amygdala. Network analyses suggested that these four brain regions show functional connectivity with other brain regions and, more importantly, that the hippocampus functions as a hub to integrate brain networks and generate social recognition memory, whereas the ACC and amygdala are important for coordinating brain activity when social interaction is initiated by connecting with other brain regions. We have found that a brain network composed of the hippocampus/mPFC/ACC/amygdala is required for the consolidation of social recognition memory. SIGNIFICANCE STATEMENT Here, we identify brain networks composed of multiple brain regions for the consolidation of social recognition memory. We found that social recognition memory is consolidated through CREB-meditated gene expression in the hippocampus, medial prefrontal cortex, anterior cingulate cortex (ACC), and amygdala. Importantly, network analyses based on c-fos expression suggest that functional connectivity of these four brain regions with other brain regions is increased with time spent in social investigation toward the generation of brain networks to consolidate social recognition memory. Furthermore, our findings suggest that hippocampus functions as a hub to integrate brain networks and generate social recognition memory, whereas ACC and amygdala are important for coordinating brain activity when social interaction is initiated by connecting with other brain regions. Copyright © 2017 the authors 0270-6474/17/374103-14$15.00/0.

Neuroanatomy of episodic and semantic memory in humans: a brief review of neuroimaging studies.

PubMed

García-Lázaro, Haydée G; Ramirez-Carmona, Rocio; Lara-Romero, Ruben; Roldan-Valadez, Ernesto

2012-01-01

One of the most basic functions in every individual and species is memory. Memory is the process by which information is saved as knowledge and retained for further use as needed. Learning is a neurobiological phenomenon by which we acquire certain information from the outside world and is a precursor to memory. Memory consists of the capacity to encode, store, consolidate, and retrieve information. Recently, memory has been defined as a network of connections whose function is primarily to facilitate the long-lasting persistence of learned environmental cues. In this review, we present a brief description of the current classifications of memory networks with a focus on episodic memory and its anatomical substrate. We also present a brief review of the anatomical basis of memory systems and the most commonly used neuroimaging methods to assess memory, illustrated with magnetic resonance imaging images depicting the hippocampus, temporal lobe, and hippocampal formation, which are the main brain structures participating in memory networks.

Spatial Memory Impairment is Associated with Intraneural Amyloid-β Immunoreactivity and Dysfunctional Arc Expression in the Hippocampal-CA3 Region of a Transgenic Mouse Model of Alzheimer's Disease.

PubMed

Morin, Jean-Pascal; Cerón-Solano, Giovanni; Velázquez-Campos, Giovanna; Pacheco-López, Gustavo; Bermúdez-Rattoni, Federico; Díaz-Cintra, Sofía

2016-01-01

Dysfunction of synaptic communication in cortical and hippocampal networks has been suggested as one of the neuropathological hallmarks of the early stages of Alzheimer's disease (AD). Also, several lines of evidence have linked disrupted levels of activity-regulated cytoskeletal associated protein (Arc), an immediate early gene product that plays a central role in synaptic plasticity, with AD "synaptopathy". The mapping of Arc expression patterns in brain networks has been extensively used as a marker of memory-relevant neuronal activity history. Here we evaluated basal and behavior-induced Arc expression in hippocampal networks of the 3xTg-AD mouse model of AD. The basal percentage of Arc-expressing cells in 10-month-old 3xTg-AD mice was higher than wild type in CA3 (4.88% versus 1.77% , respectively) but similar in CA1 (1.75% versus 2.75% ). Noteworthy, this difference was not observed at 3 months of age. Furthermore, although a Morris water maze test probe induced a steep (∼4-fold) increment in the percentage of Arc+ cells in the CA3 region of the 10-month-old wild-type group, no such increment was observed in age-matched 3xTg-AD, whereas the amount of Arc+ cells in CA1 increased in both groups. Further, we detected that CA3 neurons with amyloid-β were much more likely to express Arc protein under basal conditions. We propose that in 3xTg-AD mice, intraneuronal amyloid-β expression in CA3 could increase unspecific neuronal activation and subsequent Arc protein expression, which might impair further memory-stabilizing processes.

Changes in Brain Network Efficiency and Working Memory Performance in Aging

PubMed Central

Stanley, Matthew L.; Simpson, Sean L.; Dagenbach, Dale; Lyday, Robert G.; Burdette, Jonathan H.; Laurienti, Paul J.

2015-01-01

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory. PMID:25875001

Changes in brain network efficiency and working memory performance in aging.

PubMed

Stanley, Matthew L; Simpson, Sean L; Dagenbach, Dale; Lyday, Robert G; Burdette, Jonathan H; Laurienti, Paul J

2015-01-01

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory.

Still searching for the engram.

PubMed

Eichenbaum, Howard

2016-09-01

For nearly a century, neurobiologists have searched for the engram-the neural representation of a memory. Early studies showed that the engram is widely distributed both within and across brain areas and is supported by interactions among large networks of neurons. Subsequent research has identified engrams that support memory within dedicated functional systems for habit learning and emotional memory, but the engram for declarative memories has been elusive. Nevertheless, recent years have brought progress from molecular biological approaches that identify neurons and networks that are necessary and sufficient to support memory, and from recording approaches and population analyses that characterize the information coded by large neural networks. These new directions offer the promise of revealing the engrams for episodic and semantic memories.

REMODELING SENSORY CORTICAL MAPS IMPLANTS SPECIFIC BEHAVIORAL MEMORY

PubMed Central

Bieszczad, Kasia M.; Miasnikov, Alexandre A.; Weinberger, Norman M.

2013-01-01

Neural mechanisms underlying the capacity of memory to be rich with sensory detail are largely unknown. A candidate mechanism is learning-induced plasticity that remodels adult sensory cortex. Here, expansion in the primary auditory cortical (A1) tonotopic map of rats was induced by pairing a 3.66 kHz tone with activation of the nucleus basalis, mimicking the effects of natural associative learning. Remodeling of A1 produced de novo specific behavioral memory, but neither memory nor plasticity were consistently at the frequency of the paired tone, which typically decreased in A1 representation. Rather, there was a specific match between individual subjects’ area of expansion and the tone that was strongest in each animal’s memory, as determined by post-training frequency generalization gradients. These findings provide the first demonstration of a match between the artificial induction of specific neural representational plasticity and artificial induction of behavioral memory. As such, together with prior and present findings for detection, correlation and mimicry of plasticity with the acquisition of memory, they satisfy a key criterion for neural substrates of memory. This demonstrates that directly remodeling sensory cortical maps is sufficient for the specificity of memory formation. PMID:23639876

Mapping Common Aphasia Assessments to Underlying Cognitive Processes and Their Neural Substrates.

PubMed

Lacey, Elizabeth H; Skipper-Kallal, Laura M; Xing, Shihui; Fama, Mackenzie E; Turkeltaub, Peter E

2017-05-01

Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual's specific pattern of deficits and preserved abilities.

Regional Specific Evidence for Memory-Load Dependent Activity in the Dorsal Subiculum and the Lateral Entorhinal Cortex

PubMed Central

Ku, Shih-pi; Nakamura, Nozomu H.; Maingret, Nicolas; Mahnke, Liv; Yoshida, Motoharu; Sauvage, Magdalena M.

2017-01-01

The subiculum and the lateral entorhinal cortex (LEC) are the main output areas of the hippocampus which contribute to spatial and non-spatial memory. The proximal part of the subiculum (bordering CA1) receives heavy projections from the perirhinal cortex and the distal part of CA1 (bordering the subiculum), both known for their ties to object recognition memory. However, the extent to which the proximal subiculum contributes to non-spatial memory is still unclear. Comparatively, the involvement of the LEC in non-spatial information processing is quite well known. However, very few studies have investigated its role within the frame of memory function. Thus, it is not known whether its contribution depends on memory load. In addition, the deep layers of the EC have been shown to be predictive of subsequent memory performance, but not its superficial layers. Hence, here we tested the extent to which the proximal part of the subiculum and the superficial and deep layers of the LEC contribute to non-spatial memory, and whether this contribution depends on the memory load of the task. To do so, we imaged brain activity at cellular resolution in these areas in rats performing a delayed nonmatch to sample task based on odors with two different memory loads (5 or 10 odors). This imaging technique is based on the detection of the RNA of the immediate-early gene Arc, which is especially tied to synaptic plasticity and behavioral demands, and is commonly used to map activity in the medial temporal lobe. We report for the first time that the proximal part of the subiculum is recruited in a memory-load dependent manner and the deep layers of the LEC engaged under high memory load conditions during the retrieval of non-spatial memory, thus shedding light on the specific networks contributing to non-spatial memory retrieval. PMID:28790897

Regional Specific Evidence for Memory-Load Dependent Activity in the Dorsal Subiculum and the Lateral Entorhinal Cortex.

PubMed

Ku, Shih-Pi; Nakamura, Nozomu H; Maingret, Nicolas; Mahnke, Liv; Yoshida, Motoharu; Sauvage, Magdalena M

2017-01-01

The subiculum and the lateral entorhinal cortex (LEC) are the main output areas of the hippocampus which contribute to spatial and non-spatial memory. The proximal part of the subiculum (bordering CA1) receives heavy projections from the perirhinal cortex and the distal part of CA1 (bordering the subiculum), both known for their ties to object recognition memory. However, the extent to which the proximal subiculum contributes to non-spatial memory is still unclear. Comparatively, the involvement of the LEC in non-spatial information processing is quite well known. However, very few studies have investigated its role within the frame of memory function. Thus, it is not known whether its contribution depends on memory load. In addition, the deep layers of the EC have been shown to be predictive of subsequent memory performance, but not its superficial layers. Hence, here we tested the extent to which the proximal part of the subiculum and the superficial and deep layers of the LEC contribute to non-spatial memory, and whether this contribution depends on the memory load of the task. To do so, we imaged brain activity at cellular resolution in these areas in rats performing a delayed nonmatch to sample task based on odors with two different memory loads (5 or 10 odors). This imaging technique is based on the detection of the RNA of the immediate-early gene Arc , which is especially tied to synaptic plasticity and behavioral demands, and is commonly used to map activity in the medial temporal lobe. We report for the first time that the proximal part of the subiculum is recruited in a memory-load dependent manner and the deep layers of the LEC engaged under high memory load conditions during the retrieval of non-spatial memory, thus shedding light on the specific networks contributing to non-spatial memory retrieval.

Altered Intrinsic Hippocmapus Declarative Memory Network and Its Association with Impulsivity in Abstinent Heroin Dependent Subjects

PubMed Central

Zhai, Tian-Ye; Shao, Yong-Cong; Xie, Chun-Ming; Ye, En-Mao; Zou, Feng; Fu, Li-Ping; Li, Wen-Jun; Chen, Gang; Chen, Guang-Yu; Zhang, Zheng-Guo; Li, Shi-Jiang; Yang, Zheng

2014-01-01

Converging evidence suggests that addiction can be considered a disease of aberrant learning and memory with impulsive decision-making. In the past decades, numerous studies have demonstrated that drug addiction is involved in multiple memory systems such as classical conditioned drug memory, instrumental learning memory and the habitual learning memory. However, most of these studies have focused on the contributions of non-declarative memory, and declarative memory has largely been neglected in the research of addiction. Based on a recent finding that hippocampus, as a core functioning region of declarative memory, was proved biased the decision-making process based on past experiences by spreading associated reward values throughout memory. Our present study focused on the hippocampus. By utilizing seed-based network analysis on the resting-state functional MRI datasets with the seed hippocampus we tested how the intrinsic hippocampal memory network altered towards drug addiction, and examined how the functional connectivity strength within the altered hippocampal network correlated with behavioral index ‘impulsivity’. Our results demonstrated that HD group showed enhanced coherence between hippocampus which represents declarative memory system and non-declarative rewardguided learning memory system, and also showed attenuated intrinsic functional link between hippocampus and top-down control system, compared to the CN group. This alteration was furthered found to have behavioral significance over the behavioral index ‘impulsivity’ measured with Barratt Impulsiveness Scale (BIS). These results provide insights into the mechanism of declarative memory underlying the impulsive behavior in drug addiction. PMID:25008351

Electronic device aspects of neural network memories

NASA Technical Reports Server (NTRS)

Lambe, J.; Moopenn, A.; Thakoor, A. P.

1985-01-01

The basic issues related to the electronic implementation of the neural network model (NNM) for content addressable memories are examined. A brief introduction to the principles of the NNM is followed by an analysis of the information storage of the neural network in the form of a binary connection matrix and the recall capability of such matrix memories based on a hardware simulation study. In addition, materials and device architecture issues involved in the future realization of such networks in VLSI-compatible ultrahigh-density memories are considered. A possible space application of such devices would be in the area of large-scale information storage without mechanical devices.

Memory Network For Distributed Data Processors

NASA Technical Reports Server (NTRS)

Bolen, David; Jensen, Dean; Millard, ED; Robinson, Dave; Scanlon, George

1992-01-01

Universal Memory Network (UMN) is modular, digital data-communication system enabling computers with differing bus architectures to share 32-bit-wide data between locations up to 3 km apart with less than one millisecond of latency. Makes it possible to design sophisticated real-time and near-real-time data-processing systems without data-transfer "bottlenecks". This enterprise network permits transmission of volume of data equivalent to an encyclopedia each second. Facilities benefiting from Universal Memory Network include telemetry stations, simulation facilities, power-plants, and large laboratories or any facility sharing very large volumes of data. Main hub of UMN is reflection center including smaller hubs called Shared Memory Interfaces.

Identifying major depressive disorder using Hurst exponent of resting-state brain networks.

PubMed

Wei, Maobin; Qin, Jiaolong; Yan, Rui; Li, Haoran; Yao, Zhijian; Lu, Qing

2013-12-30

Resting-state functional magnetic resonance imaging (fMRI) studies of major depressive disorder (MDD) have revealed abnormalities of functional connectivity within or among the resting-state networks. They provide valuable insight into the pathological mechanisms of depression. However, few reports were involved in the "long-term memory" of fMRI signals. This study was to investigate the "long-term memory" of resting-state networks by calculating their Hurst exponents for identifying depressed patients from healthy controls. Resting-state networks were extracted from fMRI data of 20 MDD and 20 matched healthy control subjects. The Hurst exponent of each network was estimated by Range Scale analysis for further discriminant analysis. 95% of depressed patients and 85% of healthy controls were correctly classified by Support Vector Machine with an accuracy of 90%. The right fronto-parietal and default mode network constructed a deficit network (lower memory and more irregularity in MDD), while the left fronto-parietal, ventromedial prefrontal and salience network belonged to an excess network (longer memory in MDD), suggesting these dysfunctional networks may be related to a portion of the complex of emotional and cognitive disturbances. The abnormal "long-term memory" of resting-state networks associated with depression may provide a new possibility towards the exploration of the pathophysiological mechanisms of MDD. © 2013 Elsevier Ireland Ltd. All rights reserved.

Conjoint Influence of Maps and Auded Prose on Children's Retrieval of Instruction.

ERIC Educational Resources Information Center

Webb, James M.; And Others

1994-01-01

Ninety-six fifth-grade students studied a map of a fictitious island while twice listening to a related narrative with target feature and nonfeature items, cued by varying iconic and verbal stimuli in four map cue conditions. Memory for feature information and pictorial retrieval cues appeared to activate memory for nonfeature information. (SLD)

Altered Effective Connectivity of Hippocampus-Dependent Episodic Memory Network in mTBI Survivors

PubMed Central

2016-01-01

Traumatic brain injuries (TBIs) are generally recognized to affect episodic memory. However, less is known regarding how external force altered the way functionally connected brain structures of the episodic memory system interact. To address this issue, we adopted an effective connectivity based analysis, namely, multivariate Granger causality approach, to explore causal interactions within the brain network of interest. Results presented that TBI induced increased bilateral and decreased ipsilateral effective connectivity in the episodic memory network in comparison with that of normal controls. Moreover, the left anterior superior temporal gyrus (aSTG, the concept forming hub), left hippocampus (the personal experience binding hub), and left parahippocampal gyrus (the contextual association hub) were no longer network hubs in TBI survivors, who compensated for hippocampal deficits by relying more on the right hippocampus (underlying perceptual memory) and the right medial frontal gyrus (MeFG) in the anterior prefrontal cortex (PFC). We postulated that the overrecruitment of the right anterior PFC caused dysfunction of the strategic component of episodic memory, which caused deteriorating episodic memory in mTBI survivors. Our findings also suggested that the pattern of brain network changes in TBI survivors presented similar functional consequences to normal aging. PMID:28074162

Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data.

PubMed

Vecchio, F; Miraglia, F; Quaranta, D; Granata, G; Romanello, R; Marra, C; Bramanti, P; Rossini, P M

2016-03-01

Functional brain abnormalities including memory loss are found to be associated with pathological changes in connectivity and network neural structures. Alzheimer's disease (AD) interferes with memory formation from the molecular level, to synaptic functions and neural networks organization. Here, we determined whether brain connectivity of resting-state networks correlate with memory in patients affected by AD and in subjects with mild cognitive impairment (MCI). One hundred and forty-four subjects were recruited: 70 AD (MMSE Mini Mental State Evaluation 21.4), 50 MCI (MMSE 25.2) and 24 healthy subjects (MMSE 29.8). Undirected and weighted cortical brain network was built to evaluate graph core measures to obtain Small World parameters. eLORETA lagged linear connectivity as extracted by electroencephalogram (EEG) signals was used to weight the network. A high statistical correlation between Small World and memory performance was found. Namely, higher Small World characteristic in EEG gamma frequency band during the resting state, better performance in short-term memory as evaluated by the digit span tests. Such Small World pattern might represent a biomarker of working memory impairment in older people both in physiological and pathological conditions. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Encoding mechano-memories in filamentous-actin networks

NASA Astrophysics Data System (ADS)

Majumdar, Sayantan; Foucard, Louis; Levine, Alex; Gardel, Margaret L.

History-dependent adaptation is a central feature of learning and memory. Incorporating such features into `adaptable materials' that can modify their mechanical properties in response to external cues, remains an outstanding challenge in materials science. Here, we study a novel mechanism of mechano-memory in cross-linked F-actin networks, the essential determinants of the mechanical behavior of eukaryotic cells. We find that the non-linear mechanical response of such networks can be reversibly programmed through induction of mechano-memories. In particular, the direction, magnitude, and duration of previously applied shear stresses can be encoded into the network architecture. The `memory' of the forcing history is long-lived, but it can be erased by force applied in the opposite direction. These results demonstrate that F-actin networks can encode analog read-write mechano-memories which can be used for adaptation to mechanical stimuli. We further show that the mechano-memory arises from changes in the nematic order of the constituent filaments. Our results suggest a new mechanism of mechanical sensing in eukaryotic cells and provide a strategy for designing a novel class of materials. S.M. acknowledges U. Chicago MRSEC for support through a Kadanoff-Rice fellowship.

Dissecting psychiatric spectrum disorders by generative embedding☆☆☆

PubMed Central

Brodersen, Kay H.; Deserno, Lorenz; Schlagenhauf, Florian; Lin, Zhihao; Penny, Will D.; Buhmann, Joachim M.; Stephan, Klaas E.

2013-01-01

This proof-of-concept study examines the feasibility of defining subgroups in psychiatric spectrum disorders by generative embedding, using dynamical system models which infer neuronal circuit mechanisms from neuroimaging data. To this end, we re-analysed an fMRI dataset of 41 patients diagnosed with schizophrenia and 42 healthy controls performing a numerical n-back working-memory task. In our generative-embedding approach, we used parameter estimates from a dynamic causal model (DCM) of a visual–parietal–prefrontal network to define a model-based feature space for the subsequent application of supervised and unsupervised learning techniques. First, using a linear support vector machine for classification, we were able to predict individual diagnostic labels significantly more accurately (78%) from DCM-based effective connectivity estimates than from functional connectivity between (62%) or local activity within the same regions (55%). Second, an unsupervised approach based on variational Bayesian Gaussian mixture modelling provided evidence for two clusters which mapped onto patients and controls with nearly the same accuracy (71%) as the supervised approach. Finally, when restricting the analysis only to the patients, Gaussian mixture modelling suggested the existence of three patient subgroups, each of which was characterised by a different architecture of the visual–parietal–prefrontal working-memory network. Critically, even though this analysis did not have access to information about the patients' clinical symptoms, the three neurophysiologically defined subgroups mapped onto three clinically distinct subgroups, distinguished by significant differences in negative symptom severity, as assessed on the Positive and Negative Syndrome Scale (PANSS). In summary, this study provides a concrete example of how psychiatric spectrum diseases may be split into subgroups that are defined in terms of neurophysiological mechanisms specified by a generative model of network dynamics such as DCM. The results corroborate our previous findings in stroke patients that generative embedding, compared to analyses of more conventional measures such as functional connectivity or regional activity, can significantly enhance both the interpretability and performance of computational approaches to clinical classification. PMID:24363992

A Balanced Memory Network

PubMed Central

Roudi, Yasser; Latham, Peter E

2007-01-01

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

Information Processing Capacity of Dynamical Systems

NASA Astrophysics Data System (ADS)

Dambre, Joni; Verstraeten, David; Schrauwen, Benjamin; Massar, Serge

2012-07-01

Many dynamical systems, both natural and artificial, are stimulated by time dependent external signals, somehow processing the information contained therein. We demonstrate how to quantify the different modes in which information can be processed by such systems and combine them to define the computational capacity of a dynamical system. This is bounded by the number of linearly independent state variables of the dynamical system, equaling it if the system obeys the fading memory condition. It can be interpreted as the total number of linearly independent functions of its stimuli the system can compute. Our theory combines concepts from machine learning (reservoir computing), system modeling, stochastic processes, and functional analysis. We illustrate our theory by numerical simulations for the logistic map, a recurrent neural network, and a two-dimensional reaction diffusion system, uncovering universal trade-offs between the non-linearity of the computation and the system's short-term memory.

Information Processing Capacity of Dynamical Systems

PubMed Central

Dambre, Joni; Verstraeten, David; Schrauwen, Benjamin; Massar, Serge

2012-01-01

Many dynamical systems, both natural and artificial, are stimulated by time dependent external signals, somehow processing the information contained therein. We demonstrate how to quantify the different modes in which information can be processed by such systems and combine them to define the computational capacity of a dynamical system. This is bounded by the number of linearly independent state variables of the dynamical system, equaling it if the system obeys the fading memory condition. It can be interpreted as the total number of linearly independent functions of its stimuli the system can compute. Our theory combines concepts from machine learning (reservoir computing), system modeling, stochastic processes, and functional analysis. We illustrate our theory by numerical simulations for the logistic map, a recurrent neural network, and a two-dimensional reaction diffusion system, uncovering universal trade-offs between the non-linearity of the computation and the system's short-term memory. PMID:22816038

Noise Tolerance of Attractor and Feedforward Memory Models

PubMed Central

Lim, Sukbin; Goldman, Mark S.

2017-01-01

In short-term memory networks, transient stimuli are represented by patterns of neural activity that persist long after stimulus offset. Here, we compare the performance of two prominent classes of memory networks, feedback-based attractor networks and feedforward networks, in conveying information about the amplitude of a briefly presented stimulus in the presence of gaussian noise. Using Fisher information as a metric of memory performance, we find that the optimal form of network architecture depends strongly on assumptions about the forms of nonlinearities in the network. For purely linear networks, we find that feedforward networks outperform attractor networks because noise is continually removed from feedforward networks when signals exit the network; as a result, feedforward networks can amplify signals they receive faster than noise accumulates over time. By contrast, attractor networks must operate in a signal-attenuating regime to avoid the buildup of noise. However, if the amplification of signals is limited by a finite dynamic range of neuronal responses or if noise is reset at the time of signal arrival, as suggested by recent experiments, we find that attractor networks can out-perform feedforward ones. Under a simple model in which neurons have a finite dynamic range, we find that the optimal attractor networks are forgetful if there is no mechanism for noise reduction with signal arrival but nonforgetful (perfect integrators) in the presence of a strong reset mechanism. Furthermore, we find that the maximal Fisher information for the feedforward and attractor networks exhibits power law decay as a function of time and scales linearly with the number of neurons. These results highlight prominent factors that lead to trade-offs in the memory performance of networks with different architectures and constraints, and suggest conditions under which attractor or feedforward networks may be best suited to storing information about previous stimuli. PMID:22091664

Flexible Kernel Memory

PubMed Central

Nowicki, Dimitri; Siegelmann, Hava

2010-01-01

This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces. PMID:20552013

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

PubMed Central

Coman, Alin; Momennejad, Ida; Drach, Rae D.; Geana, Andra

2016-01-01

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals. PMID:27357678

Generalized memory associativity in a network model for the neuroses

NASA Astrophysics Data System (ADS)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2009-03-01

We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.

Nanophotonic rare-earth quantum memory with optically controlled retrieval

NASA Astrophysics Data System (ADS)

Zhong, Tian; Kindem, Jonathan M.; Bartholomew, John G.; Rochman, Jake; Craiciu, Ioana; Miyazono, Evan; Bettinelli, Marco; Cavalli, Enrico; Verma, Varun; Nam, Sae Woo; Marsili, Francesco; Shaw, Matthew D.; Beyer, Andrew D.; Faraon, Andrei

2017-09-01

Optical quantum memories are essential elements in quantum networks for long-distance distribution of quantum entanglement. Scalable development of quantum network nodes requires on-chip qubit storage functionality with control of the readout time. We demonstrate a high-fidelity nanophotonic quantum memory based on a mesoscopic neodymium ensemble coupled to a photonic crystal cavity. The nanocavity enables >95% spin polarization for efficient initialization of the atomic frequency comb memory and time bin-selective readout through an enhanced optical Stark shift of the comb frequencies. Our solid-state memory is integrable with other chip-scale photon source and detector devices for multiplexed quantum and classical information processing at the network nodes.

Cortex and Memory: Emergence of a New Paradigm

ERIC Educational Resources Information Center

Fuster, Joaquin M.

2009-01-01

Converging evidence from humans and nonhuman primates is obliging us to abandon conventional models in favor of a radically different, distributed-network paradigm of cortical memory. Central to the new paradigm is the concept of memory network or cognit--that is, a memory or an item of knowledge defined by a pattern of connections between neuron…

Still searching for the engram

PubMed Central

Eichenbaum, Howard

2016-01-01

For nearly a century neurobiologists have searched for the engram - the neural representation of a memory. Early studies showed that the engram is widely distributed both within and across brain areas and is supported by interactions among large networks of neurons. Subsequent research has identified engrams that support memory within dedicated functional systems for habit learning and emotional memory, but the engram for declarative memories has been elusive. Nevertheless, recent years have brought progress from molecular biological approaches that identify neurons and networks that are necessary and sufficient to support memory, and from recording approaches and population analyses that characterize the information coded by large neural networks. These new directions offer the promise of revealing the engrams for episodic and semantic memories. PMID:26944423

Poly(Capro-Lactone) Networks as Actively Moving Polymers

NASA Astrophysics Data System (ADS)

Meng, Yuan

Shape-memory polymers (SMPs), as a subset of actively moving polymers, form an exciting class of materials that can store and recover elastic deformation energy upon application of an external stimulus. Although engineering of SMPs nowadays has lead to robust materials that can memorize multiple temporary shapes, and can be triggered by various stimuli such as heat, light, moisture, or applied magnetic fields, further commercialization of SMPs is still constrained by the material's incapability to store large elastic energy, as well as its inherent one-way shape-change nature. This thesis develops a series of model semi-crystalline shape-memory networks that exhibit ultra-high energy storage capacity, with accurately tunable triggering temperature; by introducing a second competing network, or reconfiguring the existing network under strained state, configurational chain bias can be effectively locked-in, and give rise to two-way shape-actuators that, in the absence of an external load, elongates upon cooling and reversibly contracts upon heating. We found that well-defined network architecture plays essential role on strain-induced crystallization and on the performance of cold-drawn shape-memory polymers. Model networks with uniform molecular weight between crosslinks, and specified functionality of each net-point, results in tougher, more elastic materials with a high degree of crystallinity and outstanding shape-memory properties. The thermal behavior of the model networks can be finely modified by introducing non-crystalline small molecule linkers that effectively frustrates the crystallization of the network strands. This resulted in shape-memory networks that are ultra-sensitive to heat, as deformed materials can be efficiently triggered to revert to its permanent state upon only exposure to body temperature. We also coupled the same reaction adopted to create the model network with conventional free-radical polymerization to prepare a dual-cure "double network" that behaves as a real thermal "actuator". This approach places sub-chains under different degrees of configurational bias within the network to utilize the material's propensity to undergo stress-induced crystallization. Reconfiguration of model shape-memory networks containing photo-sensitive linkages can also be employed to program two-way actuator. Chain reshuffling of a partially reconfigurable network is initiated upon exposure to light under specific strains. Interesting photo-induced creep and stress relaxation behaviors were demonstrated and understood based on a novel transient network model we derived. In summary, delicate manipulation of shape-memory network architectures addressed critical issues constraining the application of this type of functional polymer material. Strategies developed in this thesis may provide new opportunity to the field of shape-memory polymers.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

PubMed

Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa; Horga, Guillermo

2016-04-13

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. It is unclear how communication between brain networks responds to changing environmental demands during complex cognitive processes. Also, unknown in regard to these network dynamics is the role of neuromodulators, such as dopamine, and whether their dysregulation could underlie cognitive deficits in neuropsychiatric illness. We found that connectivity between brain networks changes with working-memory load and greater increases predict better working memory performance; however, it was not related to capacity for dopamine release in the cortex. Patients with schizophrenia did show dynamic internetwork connectivity; however, this was more weakly associated with successful performance in patients compared with healthy individuals. Our findings indicate that dynamic interactions between brain networks may support the type of flexible adaptations essential to goal-directed behavior. Copyright © 2016 the authors 0270-6474/16/364378-12$15.00/0.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

PubMed Central

Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

2016-01-01

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE STATEMENT It is unclear how communication between brain networks responds to changing environmental demands during complex cognitive processes. Also, unknown in regard to these network dynamics is the role of neuromodulators, such as dopamine, and whether their dysregulation could underlie cognitive deficits in neuropsychiatric illness. We found that connectivity between brain networks changes with working-memory load and greater increases predict better working memory performance; however, it was not related to capacity for dopamine release in the cortex. Patients with schizophrenia did show dynamic internetwork connectivity; however, this was more weakly associated with successful performance in patients compared with healthy individuals. Our findings indicate that dynamic interactions between brain networks may support the type of flexible adaptations essential to goal-directed behavior. PMID:27076432

A Quantum Network with Atoms and Photons

DTIC Science & Technology

2016-09-01

The long - term goal is to entangle distant atomic memories between ARL and JQI, and explore the possibility of entangling hybrid quantum memories . 2...ARL) environment. The long - term goal is to achieve a quantum repeater network capability for the US Army. Initially, a quantum channel between ARL and...SUBJECT  TERMS Quantum, atoms, photons, entanglement, teleportation, communications, network, memory 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

A Quantum Network with Atoms and Photons

DTIC Science & Technology

2016-09-30

The long - term goal is to entangle distant atomic memories between ARL and JQI, and explore the possibility of entangling hybrid quantum memories . 2...ARL) environment. The long - term goal is to achieve a quantum repeater network capability for the US Army. Initially, a quantum channel between ARL and...SUBJECT  TERMS Quantum, atoms, photons, entanglement, teleportation, communications, network, memory 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

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

PubMed

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

2013-01-01

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

How does creating a concept map affect item-specific encoding?

PubMed

Grimaldi, Phillip J; Poston, Laurel; Karpicke, Jeffrey D

2015-07-01

Concept mapping has become a popular learning tool. However, the processes underlying the task are poorly understood. In the present study, we examined the effect of creating a concept map on the processing of item-specific information. In 2 experiments, subjects learned categorized or ad hoc word lists by making pleasantness ratings, sorting words into categories, or creating a concept map. Memory was tested using a free recall test and a recognition memory test, which is considered to be especially sensitive to item-specific processing. Typically, tasks that promote item-specific processing enhance free recall of categorized lists, relative to category sorting. Concept mapping resulted in lower recall performance than both the pleasantness rating and category sorting condition for categorized words. Moreover, concept mapping resulted in lower recognition memory performance than the other 2 tasks. These results converge on the conclusion that creating a concept map disrupts the processing of item-specific information. (c) 2015 APA, all rights reserved.

Differential proteome profiling in the hippocampus of amnesic mice.

PubMed

Baghel, Meghraj Singh; Thakur, Mahendra Kumar

2017-08-01

Amnesia or memory loss is associated with brain aging and several neurodegenerative pathologies including Alzheimer's disease (AD). This can be induced by a cholinergic antagonist scopolamine but the underlying molecular mechanism is poorly understood. This study of proteome profiling in the hippocampus could provide conceptual insights into the molecular mechanisms involved in amnesia. To reveal this, mice were administered scopolamine to induce amnesia and memory impairment was validated by novel object recognition test. Using two-dimensional gel electrophoresis coupled with MALDI-MS/MS, we have analyzed the hippocampal proteome and identified 18 proteins which were differentially expressed. Out of these proteins, 11 were downregulated and 7 were upregulated in scopolamine-treated mice as compared to control. In silico analysis showed that the majority of identified proteins are involved in metabolism, catalytic activity, and cytoskeleton architectural functions. STRING interaction network analysis revealed that majority of identified proteins exhibit common association with Actg1 cytoskeleton and Vdac1 energy transporter protein. Furthermore, interaction map analysis showed that Fascin1 and Coronin 1b individually interact with Actg1 and regulate the actin filament dynamics. Vdac1 was significantly downregulated in amnesic mice and showed interaction with other proteins in interaction network. Therefore, we silenced Vdac1 in the hippocampus of normal young mice and found similar impairment in recognition memory of Vdac1 silenced and scopolamine-treated mice. Thus, these findings suggest that Vdac1-mediated disruption of energy metabolism and cytoskeleton architecture might be involved in scopolamine-induced amnesia. © 2017 Wiley Periodicals, Inc.

Associative memory and its cerebral correlates in Alzheimer's disease: Evidence for distinct deficits of relational and conjunctive memory

PubMed Central

Bastin, Christine; Bahri, Mohamed Ali; Miévis, Frédéric; Lemaire, Christian; Collette, Fabienne; Genon, Sarah; Simon, Jessica; Guillaume, Bénédicte; Diana, Rachel A.; Yonelinas, Andrew P.; Salmon, Eric

2014-01-01

This study investigated the impact of Alzheimer's disease (AD) on conjunctive and relational binding in episodic memory. Mild AD patients and controls had to remember item-color associations by imagining color either as a contextual association (relational memory) or as a feature of the item to be encoded (conjunctive memory). Patients' performance in each condition was correlated with cerebral metabolism measured by FDG-PET. The results showed that AD patients had an impaired capacity to remember item-color associations, with deficits in both relational and conjunctive memory. However, performance in the two kinds of associative memory varied independently across patients. Partial least square analyses revealed that poor conjunctive memory was related to hypometabolism in an anterior temporal-posterior fusiform brain network, whereas relational memory correlated with metabolism in regions of the default mode network. These findings support the hypothesis of distinct neural systems specialized in different types of associative memory and point to heterogeneous profiles of memory alteration in Alzheimer's disease as a function of damage to the respective neural networks. PMID:25172390

Does Sleep Improve Your Grammar? Preferential Consolidation of Arbitrary Components of New Linguistic Knowledge

PubMed Central

Mirković, Jelena; Gaskell, M. Gareth

2016-01-01

We examined the role of sleep-related memory consolidation processes in learning new form-meaning mappings. Specifically, we examined a Complementary Learning Systems account, which implies that sleep-related consolidation should be more beneficial for new hippocampally dependent arbitrary mappings (e.g. new vocabulary items) relative to new systematic mappings (e.g. grammatical regularities), which can be better encoded neocortically. The hypothesis was tested using a novel language with an artificial grammatical gender system. Stem-referent mappings implemented arbitrary aspects of the new language, and determiner/suffix+natural gender mappings implemented systematic aspects (e.g. tib scoiffesh + ballerina, tib mofeem + bride; ked jorool + cowboy, ked heefaff + priest). Importantly, the determiner-gender and the suffix-gender mappings varied in complexity and salience, thus providing a range of opportunities to detect beneficial effects of sleep for this type of mapping. Participants were trained on the new language using a word-picture matching task, and were tested after a 2-hour delay which included sleep or wakefulness. Participants in the sleep group outperformed participants in the wake group on tests assessing memory for the arbitrary aspects of the new mappings (individual vocabulary items), whereas we saw no evidence of a sleep benefit in any of the tests assessing memory for the systematic aspects of the new mappings: Participants in both groups extracted the salient determiner-natural gender mapping, but not the more complex suffix-natural gender mapping. The data support the predictions of the complementary systems account and highlight the importance of the arbitrariness/systematicity dimension in the consolidation process for declarative memories. PMID:27046022

Set selection dynamical system neural networks with partial memories, with applications to Sudoku and KenKen puzzles.

PubMed

Boreland, B; Clement, G; Kunze, H

2015-08-01

After reviewing set selection and memory model dynamical system neural networks, we introduce a neural network model that combines set selection with partial memories (stored memories on subsets of states in the network). We establish that feasible equilibria with all states equal to ± 1 correspond to answers to a particular set theoretic problem. We show that KenKen puzzles can be formulated as a particular case of this set theoretic problem and use the neural network model to solve them; in addition, we use a similar approach to solve Sudoku. We illustrate the approach in examples. As a heuristic experiment, we use online or print resources to identify the difficulty of the puzzles and compare these difficulties to the number of iterations used by the appropriate neural network solver, finding a strong relationship. Copyright © 2015 Elsevier Ltd. All rights reserved.

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

PubMed Central

2014-01-01

Background Network-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in principle respond to these issues, but raises further algorithmic problems and require resources not satisfiable with simple off-the-shelf computers. Results We propose a novel framework for scalable network-based learning of multi-species protein functions based on both a local implementation of existing algorithms and the adoption of innovative technologies: we solve “locally” the AFP problem, by designing “vertex-centric” implementations of network-based algorithms, but we do not give up thinking “globally” by exploiting the overall topology of the network. This is made possible by the adoption of secondary memory-based technologies that allow the efficient use of the large memory available on disks, thus overcoming the main memory limitations of modern off-the-shelf computers. This approach has been applied to the analysis of a large multi-species network including more than 300 species of bacteria and to a network with more than 200,000 proteins belonging to 13 Eukaryotic species. To our knowledge this is the first work where secondary-memory based network analysis has been applied to multi-species function prediction using biological networks with hundreds of thousands of proteins. Conclusions The combination of these algorithmic and technological approaches makes feasible the analysis of large multi-species networks using ordinary computers with limited speed and primary memory, and in perspective could enable the analysis of huge networks (e.g. the whole proteomes available in SwissProt), using well-equipped stand-alone machines. PMID:24843788

Abnormal Functional Activation and Connectivity in the Working Memory Network in Early-Onset Schizophrenia

ERIC Educational Resources Information Center

Kyriakopoulos, Marinos; Dima, Danai; Roiser, Jonathan P.; Corrigall, Richard; Barker, Gareth J.; Frangou, Sophia

2012-01-01

Objective: Disruption within the working memory (WM) neural network is considered an integral feature of schizophrenia. The WM network, and the dorsolateral prefrontal cortex (DLPFC) in particular, undergo significant remodeling in late adolescence. Potential interactions between developmental changes in the WM network and disease-related…

Offline memory reprocessing: involvement of the brain's default network in spontaneous thought processes.

PubMed

Wang, Kun; Yu, Chunshui; Xu, Lijuan; Qin, Wen; Li, Kuncheng; Xu, Lin; Jiang, Tianzi

2009-01-01

Spontaneous thought processes (STPs), also called daydreaming or mind-wandering, occur ubiquitously in daily life. However, the functional significance of STPs remains largely unknown. Using functional magnetic resonance imaging (fMRI), we first identified an STPs-network whose activity was positively correlated with the subjects' tendency of having STPs during a task-free state. The STPs-network was then found to be strongly associated with the default network, which has previously been established as being active during the task-free state. Interestingly, we found that offline reprocessing of previously memorized information further increased the activity of the STPs-network regions, although during a state with less STPs. In addition, we found that the STPs-network kept a dynamic balance between functional integration and functional separation among its component regions to execute offline memory reprocessing in STPs. These findings strengthen a view that offline memory reprocessing and STPs share the brain's default network, and thus implicate that offline memory reprocessing may be a predetermined function of STPs. This supports the perspective that memory can be consolidated and modified during STPs, and thus gives rise to a dynamic behavior dependent on both previous external and internal experiences.

A Facile and General Approach to Recoverable High-Strain Multishape Shape Memory Polymers.

PubMed

Li, Xingjian; Pan, Yi; Zheng, Zhaohui; Ding, Xiaobin

2018-03-01

Fabricating a single polymer network with no need to design complex structures to achieve an ideal combination of tunable high-strain multiple-shape memory effects and highly recoverable shape memory property is a great challenge for the real applications of advanced shape memory devices. Here, a facile and general approach to recoverable high-strain multishape shape memory polymers is presented via a random copolymerization of acrylate monomers and a chain-extended multiblock copolymer crosslinker. As-prepared shape memory networks show a large width at the half-peak height of the glass transition, far wider than current classical multishape shape memory polymers. A combination of tunable high-strain multishape memory effect and as high as 1000% recoverable strain in a single chemical-crosslinking network can be obtained. To the best of our knowledge, this is the first thermosetting material with a combination of highly recoverable strain and tunable high-strain multiple-shape memory effects. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Altered intrinsic hippocmapus declarative memory network and its association with impulsivity in abstinent heroin dependent subjects.

PubMed

Zhai, Tian-Ye; Shao, Yong-Cong; Xie, Chun-Ming; Ye, En-Mao; Zou, Feng; Fu, Li-Ping; Li, Wen-Jun; Chen, Gang; Chen, Guang-Yu; Zhang, Zheng-Guo; Li, Shi-Jiang; Yang, Zheng

2014-10-01

Converging evidence suggests that addiction can be considered a disease of aberrant learning and memory with impulsive decision-making. In the past decades, numerous studies have demonstrated that drug addiction is involved in multiple memory systems such as classical conditioned drug memory, instrumental learning memory and the habitual learning memory. However, most of these studies have focused on the contributions of non-declarative memory, and declarative memory has largely been neglected in the research of addiction. Based on a recent finding that hippocampus, as a core functioning region of declarative memory, was proved biased the decision-making process based on past experiences by spreading associated reward values throughout memory. Our present study focused on the hippocampus. By utilizing seed-based network analysis on the resting-state functional MRI datasets with the seed hippocampus we tested how the intrinsic hippocampal memory network altered toward drug addiction, and examined how the functional connectivity strength within the altered hippocampal network correlated with behavioral index 'impulsivity'. Our results demonstrated that HD group showed enhanced coherence between hippocampus which represents declarative memory system and non-declarative reward-guided learning memory system, and also showed attenuated intrinsic functional link between hippocampus and top-down control system, compared to the CN group. This alteration was furthered found to have behavioral significance over the behavioral index 'impulsivity' measured with Barratt Impulsiveness Scale (BIS). These results provide insights into the mechanism of declarative memory underlying the impulsive behavior in drug addiction. Copyright © 2014 Elsevier B.V. All rights reserved.

Elaboration versus suppression of cued memories: influence of memory recall instruction and success on parietal lobe, default network, and hippocampal activity.

PubMed

Gimbel, Sarah I; Brewer, James B

2014-01-01

Functional imaging studies of episodic memory retrieval consistently report task-evoked and memory-related activity in the medial temporal lobe, default network and parietal lobe subregions. Associated components of memory retrieval, such as attention-shifts, search, retrieval success, and post-retrieval processing also influence regional activity, but these influences remain ill-defined. To better understand how top-down control affects the neural bases of memory retrieval, we examined how regional activity responses were modulated by task goals during recall success or failure. Specifically, activity was examined during memory suppression, recall, and elaborative recall of paired-associates. Parietal lobe was subdivided into dorsal (BA 7), posterior ventral (BA 39), and anterior ventral (BA 40) regions, which were investigated separately to examine hypothesized distinctions in sub-regional functional responses related to differential attention-to-memory and memory strength. Top-down suppression of recall abolished memory strength effects in BA 39, which showed a task-negative response, and BA 40, which showed a task-positive response. The task-negative response in default network showed greater negatively-deflected signal for forgotten pairs when task goals required recall. Hippocampal activity was task-positive and was influenced by memory strength only when task goals required recall. As in previous studies, we show a memory strength effect in parietal lobe and hippocampus, but we show that this effect is top-down controlled and sensitive to whether the subject is trying to suppress or retrieve a memory. These regions are all implicated in memory recall, but their individual activity patterns show distinct memory-strength-related responses when task goals are varied. In parietal lobe, default network, and hippocampus, top-down control can override the commonly identified effects of memory strength.

Elaboration versus Suppression of Cued Memories: Influence of Memory Recall Instruction and Success on Parietal Lobe, Default Network, and Hippocampal Activity

PubMed Central

Gimbel, Sarah I.; Brewer, James B.

2014-01-01

Functional imaging studies of episodic memory retrieval consistently report task-evoked and memory-related activity in the medial temporal lobe, default network and parietal lobe subregions. Associated components of memory retrieval, such as attention-shifts, search, retrieval success, and post-retrieval processing also influence regional activity, but these influences remain ill-defined. To better understand how top-down control affects the neural bases of memory retrieval, we examined how regional activity responses were modulated by task goals during recall success or failure. Specifically, activity was examined during memory suppression, recall, and elaborative recall of paired-associates. Parietal lobe was subdivided into dorsal (BA 7), posterior ventral (BA 39), and anterior ventral (BA 40) regions, which were investigated separately to examine hypothesized distinctions in sub-regional functional responses related to differential attention-to-memory and memory strength. Top-down suppression of recall abolished memory strength effects in BA 39, which showed a task-negative response, and BA 40, which showed a task-positive response. The task-negative response in default network showed greater negatively-deflected signal for forgotten pairs when task goals required recall. Hippocampal activity was task-positive and was influenced by memory strength only when task goals required recall. As in previous studies, we show a memory strength effect in parietal lobe and hippocampus, but we show that this effect is top-down controlled and sensitive to whether the subject is trying to suppress or retrieve a memory. These regions are all implicated in memory recall, but their individual activity patterns show distinct memory-strength-related responses when task goals are varied. In parietal lobe, default network, and hippocampus, top-down control can override the commonly identified effects of memory strength. PMID:24586492

Towards a Quantum Memory assisted MDI-QKD node

NASA Astrophysics Data System (ADS)

Namazi, Mehdi; Vallone, Giuseppe; Jordaan, Bertus; Goham, Connor; Shahrokhshahi, Reihaneh; Villoresi, Paolo; Figueroa, Eden

2017-04-01

The creation of large quantum network that permits the communication of quantum states and the secure distribution of cryptographic keys requires multiple operational quantum memories. In this work we present our progress towards building a prototypical quantum network that performs the memory-assisted measurement device independent QKD protocol. Currently our network combines the quantum part of the BB84 protocol with room-temperature quantum memory operation, while still maintaining relevant quantum bit error rates for single-photon level operation. We will also discuss our efforts to use a network of two room temperature quantum memories, receiving, storing and transforming randomly polarized photons in order to realize Bell state measurements. The work was supported by the US-Navy Office of Naval Research, Grant Number N00141410801, the National Science Foundation, Grant Number PHY-1404398 and the Simons Foundation, Grant Number SBF241180.

THEORETICAL REVIEW The Hippocampus, Time, and Memory Across Scales

PubMed Central

Howard, Marc W.; Eichenbaum, Howard

2014-01-01

A wealth of experimental studies with animals have offered insights about how neural networks within the hippocampus support the temporal organization of memories. These studies have revealed the existence of “time cells” that encode moments in time, much as the well-known “place cells” map locations in space. Another line of work inspired by human behavioral studies suggests that episodic memories are mediated by a state of temporal context that changes gradually over long time scales, up to at least a few thousand seconds. In this view, the “mental time travel” hypothesized to support the experience of episodic memory corresponds to a “jump back in time” in which a previous state of temporal context is recovered. We suggest that these 2 sets of findings could be different facets of a representation of temporal history that maintains a record at the last few thousand seconds of experience. The ability to represent long time scales comes at the cost of discarding precise information about when a stimulus was experienced—this uncertainty becomes greater for events further in the past. We review recent computational work that describes a mechanism that could construct such a scale-invariant representation. Taken as a whole, this suggests the hippocampus plays its role in multiple aspects of cognition by representing events embedded in a general spatiotemporal context. The representation of internal time can be useful across nonhippocampal memory systems. PMID:23915126

A constraint optimization based virtual network mapping method

NASA Astrophysics Data System (ADS)

Li, Xiaoling; Guo, Changguo; Wang, Huaimin; Li, Zhendong; Yang, Zhiwen

2013-03-01

Virtual network mapping problem, maps different virtual networks onto the substrate network is an extremely challenging work. This paper proposes a constraint optimization based mapping method for solving virtual network mapping problem. This method divides the problem into two phases, node mapping phase and link mapping phase, which are all NP-hard problems. Node mapping algorithm and link mapping algorithm are proposed for solving node mapping phase and link mapping phase, respectively. Node mapping algorithm adopts the thinking of greedy algorithm, mainly considers two factors, available resources which are supplied by the nodes and distance between the nodes. Link mapping algorithm is based on the result of node mapping phase, adopts the thinking of distributed constraint optimization method, which can guarantee to obtain the optimal mapping with the minimum network cost. Finally, simulation experiments are used to validate the method, and results show that the method performs very well.

Short-term memory in networks of dissociated cortical neurons.

PubMed

Dranias, Mark R; Ju, Han; Rajaram, Ezhilarasan; VanDongen, Antonius M J

2013-01-30

Short-term memory refers to the ability to store small amounts of stimulus-specific information for a short period of time. It is supported by both fading and hidden memory processes. Fading memory relies on recurrent activity patterns in a neuronal network, whereas hidden memory is encoded using synaptic mechanisms, such as facilitation, which persist even when neurons fall silent. We have used a novel computational and optogenetic approach to investigate whether these same memory processes hypothesized to support pattern recognition and short-term memory in vivo, exist in vitro. Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays. Cultures were transfected with ChannelRhodopsin-2 and optically stimulated using random dot stimuli. The pattern of neuronal activity resulting from this stimulation was analyzed using classification algorithms that enabled the identification of stimulus-specific memories. Fading memories for different stimuli, encoded in ongoing neural activity, persisted and could be distinguished from each other for as long as 1 s after stimulation was terminated. Hidden memories were detected by altered responses of neurons to additional stimulation, and this effect persisted longer than 1 s. Interestingly, network bursts seem to eliminate hidden memories. These results are similar to those that have been reported from similar experiments in vivo and demonstrate that mechanisms of information processing and short-term memory can be studied using cultured neuronal networks, thereby setting the stage for therapeutic applications using this platform.

Parvalbumin-expressing interneurons coordinate hippocampal network dynamics required for memory consolidation

NASA Astrophysics Data System (ADS)

Ognjanovski, Nicolette; Schaeffer, Samantha; Wu, Jiaxing; Mofakham, Sima; Maruyama, Daniel; Zochowski, Michal; Aton, Sara J.

2017-04-01

Activity in hippocampal area CA1 is essential for consolidating episodic memories, but it is unclear how CA1 activity patterns drive memory formation. We find that in the hours following single-trial contextual fear conditioning (CFC), fast-spiking interneurons (which typically express parvalbumin (PV)) show greater firing coherence with CA1 network oscillations. Post-CFC inhibition of PV+ interneurons blocks fear memory consolidation. This effect is associated with loss of two network changes associated with normal consolidation: (1) augmented sleep-associated delta (0.5-4 Hz), theta (4-12 Hz) and ripple (150-250 Hz) oscillations; and (2) stabilization of CA1 neurons' functional connectivity patterns. Rhythmic activation of PV+ interneurons increases CA1 network coherence and leads to a sustained increase in the strength and stability of functional connections between neurons. Our results suggest that immediately following learning, PV+ interneurons drive CA1 oscillations and reactivation of CA1 ensembles, which directly promotes network plasticity and long-term memory formation.

Investigation of Higher Brain Functions in Music Composition Using Models of the Cortex Based on Physical System Analogies.

NASA Astrophysics Data System (ADS)

Leng, Xiaodan

The trion model was developed using the Mountcastle organizational principle for the column as the basic neuronal network in the cortex and the physical system analogy of Fisher's ANNNI spin model. An essential feature is that it is highly structured in time and in spatial connections. Simulations of a network of trions have shown that large numbers of quasi-stable, periodic spatial-temporal firing patterns can be excited. Characteristics of these patterns include the quality of being readily enhanced by only a small change in connection strengths, and that the patterns evolve in certain natural sequences from one to another. With only somewhat different parameters than used for studying memory and pattern recognition, much more flowing and intriguing patterns emerged from the simulations. The results were striking when these probabilistic evolutions were mapped onto pitches and instruments to produce music: For example different simple mappings of the same evolution give music having the "flavor" of a minuet, a waltz, folk music, or styles of specific periods. A theme can be learned so that evolutions have this theme and its variations reoccurring more often. That the trion model is a viable model for the coding of musical structure in human composition and perception is suggested. It is further proposed that model is relevant for examining creativity in the higher cognitive functions of mathematics and chess, which are similar to music. An even higher level of cortical organization was modeled by coupling together several trion networks. Further, one of the crucial features of higher brain function, especially in music composition or appreciation, is the role of emotion and mood as controlled by the many neuromodulators or neuropeptides. The MILA model whose underlying basis is zero-level representation of Kac-Moody algebra is used to modulate periodically the firing threshold of each network. Our preliminary results show that the introduction of "neuromodulation" into the dynamics of a few coupled trion networks greatly enhanced the richness of the music. Neuromodulation plays a very important role in cognitive processes. I discuss many aspects of cognitive processes such as, leaning and memory, innervation of cortical functions and coordination between music and emotions. The implications of my work are discussed.

Consolidation of visuomotor adaptation memory with consistent and noisy environments

PubMed Central

Maeda, Rodrigo S.; McGee, Steven E.

2016-01-01

Our understanding of how we learn and retain motor behaviors is still limited. For instance, there is conflicting evidence as to whether the memory of a learned visuomotor perturbation consolidates; i.e., the motor memory becomes resistant to interference from learning a competing perturbation over time. Here, we sought to determine the factors that influence consolidation during visually guided walking. Subjects learned a novel mapping relationship, created by prism lenses, between the perceived location of two targets and the motor commands necessary to direct the feet to their positions. Subjects relearned this mapping 1 wk later. Different groups experienced protocols with or without a competing mapping (and with and without washout trials), presented either on the same day as initial learning or before relearning on day 2. We tested identical protocols under constant and noisy mapping structures. In the latter, we varied, on a trial-by-trial basis, the strength of prism lenses around a non-zero mean. We found that a novel visuomotor mapping is retained at least 1 wk after initial learning. We also found reduced foot-placement error with relearning in constant and noisy mapping groups, despite learning a competing mapping beforehand, and with the exception of one protocol, with and without washout trials. Exposure to noisy mappings led to similar performance on relearning compared with the equivalent constant mapping groups for most protocols. Overall, our results support the idea of motor memory consolidation during visually guided walking and suggest that constant and noisy practices are effective for motor learning. NEW & NOTEWORTHY The adaptation of movement is essential for many daily activities. To interact with targets, this often requires learning the mapping to produce appropriate motor commands based on visual input. Here, we show that a novel visuomotor mapping is retained 1 wk after initial learning in a visually guided walking task. Furthermore, we find that this motor memory consolidates (i.e., becomes more resistant to interference from learning a competing mapping) when learning in constant and noisy mapping environments. PMID:27784800

Consolidation in older adults depends upon competition between resting-state networks

PubMed Central

Jacobs, Heidi I. L.; Dillen, Kim N. H.; Risius, Okka; Göreci, Yasemin; Onur, Oezguer A.; Fink, Gereon R.; Kukolja, Juraj

2015-01-01

Memory encoding and retrieval problems are inherent to aging. To date, however, the effect of aging upon the neural correlates of forming memory traces remains poorly understood. Resting-state fMRI connectivity can be used to investigate initial consolidation. We compared within and between network connectivity differences between healthy young and older participants before encoding, after encoding and before retrieval by means of resting-state fMRI. Alterations over time in the between-network connectivity analyses correlated with retrieval performance, whereas within-network connectivity did not: a higher level of negative coupling or competition between the default mode and the executive networks during the after encoding condition was associated with increased retrieval performance in the older adults, but not in the young group. Data suggest that the effective formation of memory traces depends on an age-dependent, dynamic reorganization of the interaction between multiple, large-scale functional networks. Our findings demonstrate that a cross-network based approach can further the understanding of the neural underpinnings of aging-associated memory decline. PMID:25620930

Drawing Road Networks with Mental Maps.

PubMed

Lin, Shih-Syun; Lin, Chao-Hung; Hu, Yan-Jhang; Lee, Tong-Yee

2014-09-01

Tourist and destination maps are thematic maps designed to represent specific themes in maps. The road network topologies in these maps are generally more important than the geometric accuracy of roads. A road network warping method is proposed to facilitate map generation and improve theme representation in maps. The basic idea is deforming a road network to meet a user-specified mental map while an optimization process is performed to propagate distortions originating from road network warping. To generate a map, the proposed method includes algorithms for estimating road significance and for deforming a road network according to various geometric and aesthetic constraints. The proposed method can produce an iconic mark of a theme from a road network and meet a user-specified mental map. Therefore, the resulting map can serve as a tourist or destination map that not only provides visual aids for route planning and navigation tasks, but also visually emphasizes the presentation of a theme in a map for the purpose of advertising. In the experiments, the demonstrations of map generations show that our method enables map generation systems to generate deformed tourist and destination maps efficiently.

Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation.

NASA Astrophysics Data System (ADS)

Jablonski, Piotr; Poe, Gina; Zochowski, Michal

2007-03-01

The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

Structural network heterogeneities and network dynamics: A possible dynamical mechanism for hippocampal memory reactivation

NASA Astrophysics Data System (ADS)

Jablonski, Piotr; Poe, Gina R.; Zochowski, Michal

2007-01-01

The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

An Adaptive Memory Interface Controller for Improving Bandwidth Utilization of Hybrid and Reconfigurable Systems

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

Castellana, Vito G.; Tumeo, Antonino; Ferrandi, Fabrizio

Emerging applications such as data mining, bioinformatics, knowledge discovery, social network analysis are irregular. They use data structures based on pointers or linked lists, such as graphs, unbalanced trees or unstructures grids, which generates unpredictable memory accesses. These data structures usually are large, but difficult to partition. These applications mostly are memory bandwidth bounded and have high synchronization intensity. However, they also have large amounts of inherent dynamic parallelism, because they potentially perform a task for each one of the element they are exploring. Several efforts are looking at accelerating these applications on hybrid architectures, which integrate general purpose processorsmore » with reconfigurable devices. Some solutions, which demonstrated significant speedups, include custom-hand tuned accelerators or even full processor architectures on the reconfigurable logic. In this paper we present an approach for the automatic synthesis of accelerators from C, targeted at irregular applications. In contrast to typical High Level Synthesis paradigms, which construct a centralized Finite State Machine, our approach generates dynamically scheduled hardware components. While parallelism exploitation in typical HLS-generated accelerators is usually bound within a single execution flow, our solution allows concurrently running multiple execution flow, thus also exploiting the coarser grain task parallelism of irregular applications. Our approach supports multiple, multi-ported and distributed memories, and atomic memory operations. Its main objective is parallelizing as many memory operations as possible, independently from their execution time, to maximize the memory bandwidth utilization. This significantly differs from current HLS flows, which usually consider a single memory port and require precise scheduling of memory operations. A key innovation of our approach is the generation of a memory interface controller, which dynamically maps concurrent memory accesses to multiple ports. We present a case study on a typical irregular kernel, Graph Breadth First search (BFS), exploring different tradeoffs in terms of parallelism and number of memories.« less

Star Mapping with Slime Mold Physarum Polycephalum

NASA Astrophysics Data System (ADS)

Mihklepp, M.; Domnitch, E.; Gelfand, D.; Foing, B. H.; van der Heide, E.

2014-04-01

Human curiosity and exploration towards outer space has led to many fantastic inventions and given way to alternative scenarios about the origins of life. In the Space Science in the Arts course together with ESTEC with support from ILEWG. I got interested about unicellular slime mold Physarum polycephalum. There has been and still is a lot of research on Physarum polycephalum. This brainless eucaryotic microbe has its smartness and external memory strategies. Physarum can navigate through a maze made of agar using the shortest route possible when two pieces of food are placed at two separate exits of the maze. It can build efficient networks - Physarum created network similar to the existing Tokyo train system. It is being used to control a robot, in USB-sensor and in sound synthesis. Right now there is a lot of research about using Physarum in bio-computing.

A biased competition account of attention and memory in Alzheimer's disease

PubMed Central

Finke, Kathrin; Myers, Nicholas; Bublak, Peter; Sorg, Christian

2013-01-01

The common view of Alzheimer's disease (AD) is that of an age-related memory disorder, i.e. declarative memory deficits are the first signs of the disease and associated with progressive brain changes in the medial temporal lobes and the default mode network. However, two findings challenge this view. First, new model-based tools of attention research have revealed that impaired selective attention accompanies memory deficits from early pre-dementia AD stages on. Second, very early distributed lesions of lateral parietal networks may cause these attention deficits by disrupting brain mechanisms underlying attentional biased competition. We suggest that memory and attention impairments might indicate disturbances of a common underlying neurocognitive mechanism. We propose a unifying account of impaired neural interactions within and across brain networks involved in attention and memory inspired by the biased competition principle. We specify this account at two levels of analysis: at the computational level, the selective competition of representations during both perception and memory is biased by AD-induced lesions; at the large-scale brain level, integration within and across intrinsic brain networks, which overlap in parietal and temporal lobes, is disrupted. This account integrates a large amount of previously unrelated findings of changed behaviour and brain networks and favours a brain mechanism-centred view on AD. PMID:24018724

A biased competition account of attention and memory in Alzheimer's disease.

PubMed

Finke, Kathrin; Myers, Nicholas; Bublak, Peter; Sorg, Christian

2013-10-19

The common view of Alzheimer's disease (AD) is that of an age-related memory disorder, i.e. declarative memory deficits are the first signs of the disease and associated with progressive brain changes in the medial temporal lobes and the default mode network. However, two findings challenge this view. First, new model-based tools of attention research have revealed that impaired selective attention accompanies memory deficits from early pre-dementia AD stages on. Second, very early distributed lesions of lateral parietal networks may cause these attention deficits by disrupting brain mechanisms underlying attentional biased competition. We suggest that memory and attention impairments might indicate disturbances of a common underlying neurocognitive mechanism. We propose a unifying account of impaired neural interactions within and across brain networks involved in attention and memory inspired by the biased competition principle. We specify this account at two levels of analysis: at the computational level, the selective competition of representations during both perception and memory is biased by AD-induced lesions; at the large-scale brain level, integration within and across intrinsic brain networks, which overlap in parietal and temporal lobes, is disrupted. This account integrates a large amount of previously unrelated findings of changed behaviour and brain networks and favours a brain mechanism-centred view on AD.

Salience Network and Parahippocampal Dopamine Dysfunction in Memory-Impaired Parkinson Disease

PubMed Central

Christopher, Leigh; Duff-Canning, Sarah; Koshimori, Yuko; Segura, Barbara; Boileau, Isabelle; Chen, Robert; Lang, Anthony E.; Houle, Sylvain; Rusjan, Pablo; Strafella, Antonio P.

2016-01-01

Objective Patients with Parkinson disease (PD) and mild cognitive impairment (MCI) are vulnerable to dementia and frequently experience memory deficits. This could be the result of dopamine dysfunction in corticostriatal networks (salience, central executive networks, and striatum) and/or the medial temporal lobe. Our aim was to investigate whether dopamine dysfunction in these regions contributes to memory impairment in PD. Methods We used positron emission tomography imaging to compare D2 receptor availability in the cortex and striatal (limbic and associative) dopamine neuron integrity in 4 groups: memory-impaired PD (amnestic MCI; n=9), PD with nonamnestic MCI (n=10), PD without MCI (n=11), and healthy controls (n=14). Subjects were administered a full neuropsychological test battery for cognitive performance. Results Memory-impaired patients demonstrated more significant reductions in D2 receptor binding in the salience network (insular cortex and anterior cingulate cortex [ACC] and the right parahippocampal gyrus [PHG]) compared to healthy controls and patients with no MCI. They also presented reductions in the right insula and right ACC compared to nonamnestic MCI patients. D2 levels were correlated with memory performance in the right PHG and left insula of amnestic patients and with executive performance in the bilateral insula and left ACC of all MCI patients. Associative striatal dopamine denervation was significant in all PD patients. Interpretation Dopaminergic differences in the salience network and the medial temporal lobe contribute to memory impairment in PD. Furthermore, these findings indicate the vulnerability of the salience network in PD and its potential role in memory and executive dysfunction. PMID:25448687

DMA shared byte counters in a parallel computer

DOEpatents

Chen, Dong; Gara, Alan G.; Heidelberger, Philip; Vranas, Pavlos

2010-04-06

A parallel computer system is constructed as a network of interconnected compute nodes. Each of the compute nodes includes at least one processor, a memory and a DMA engine. The DMA engine includes a processor interface for interfacing with the at least one processor, DMA logic, a memory interface for interfacing with the memory, a DMA network interface for interfacing with the network, injection and reception byte counters, injection and reception FIFO metadata, and status registers and control registers. The injection FIFOs maintain memory locations of the injection FIFO metadata memory locations including its current head and tail, and the reception FIFOs maintain the reception FIFO metadata memory locations including its current head and tail. The injection byte counters and reception byte counters may be shared between messages.

A network approach for modulating memory processes via direct and indirect brain stimulation: Toward a causal approach for the neural basis of memory.

PubMed

Kim, Kamin; Ekstrom, Arne D; Tandon, Nitin

2016-10-01

Electrical stimulation of the brain is a unique tool to perturb endogenous neural signals, allowing us to evaluate the necessity of given neural processes to cognitive processing. An important issue, gaining increasing interest in the literature, is whether and how stimulation can be employed to selectively improve or disrupt declarative memory processes. Here, we provide a comprehensive review of both invasive and non-invasive stimulation studies aimed at modulating memory performance. The majority of past studies suggest that invasive stimulation of the hippocampus impairs memory performance; similarly, most non-invasive studies show that disrupting frontal or parietal regions also impairs memory performance, suggesting that these regions also play necessary roles in declarative memory. On the other hand, a handful of both invasive and non-invasive studies have also suggested modest improvements in memory performance following stimulation. These studies typically target brain regions connected to the hippocampus or other memory "hubs," which may affect endogenous activity in connected areas like the hippocampus, suggesting that to augment declarative memory, altering the broader endogenous memory network activity is critical. Together, studies reporting memory improvements/impairments are consistent with the idea that a network of distinct brain "hubs" may be crucial for successful memory encoding and retrieval rather than a single primary hub such as the hippocampus. Thus, it is important to consider neurostimulation from the network perspective, rather than from a purely localizationalist viewpoint. We conclude by proposing a novel approach to neurostimulation for declarative memory modulation that aims to facilitate interactions between multiple brain "nodes" underlying memory rather than considering individual brain regions in isolation. Copyright © 2016. Published by Elsevier Inc.

Rethinking cognition and behavior in the new classification for childhood epilepsy: Examples from frontal lobe and temporal lobe epilepsies.

PubMed

Smith, Mary Lou

2016-11-01

The new approach to classification of the epilepsies emphasizes the role of dysfunction in networks in defining types of epilepsies. This paper reviews the structural and neuropsychological deficits in two types of childhood epilepsy: frontal lobe and temporal lobe epilepsy. The evidence for and against a pattern of specificity of deficits in executive function and memory associated with these two types of epilepsies is presented. The evidence varies with the methodologies used in the studies, but direct comparison of the two types of epilepsies does not suggest a clear-cut mapping of function onto structure. These findings are discussed in light of the concept of network dysfunction. The evidence supports the conceptualization of epilepsy as a network disease. Implications for future work in the neuropsychology of pediatric epilepsy are suggested. This article is part of a Special Issue entitled "The new approach to classification: Rethinking cognition and behavior in epilepsy". Copyright © 2016 Elsevier Inc. All rights reserved.

Training of Attentional Filtering, but Not of Memory Storage, Enhances Working Memory Efficiency by Strengthening the Neuronal Gatekeeper Network.

PubMed

Schmicker, Marlen; Schwefel, Melanie; Vellage, Anne-Katrin; Müller, Notger G

2016-04-01

Memory training (MT) in older adults with memory deficits often leads to frustration and, therefore, is usually not recommended. Here, we pursued an alternative approach and looked for transfer effects of 1-week attentional filter training (FT) on working memory performance and its neuronal correlates in young healthy humans. The FT effects were compared with pure MT, which lacked the necessity to filter out irrelevant information. Before and after training, all participants performed an fMRI experiment that included a combined task in which stimuli had to be both filtered based on color and stored in memory. We found that training induced processing changes by biasing either filtering or storage. FT induced larger transfer effects on the untrained cognitive function than MT. FT increased neuronal activity in frontal parts of the neuronal gatekeeper network, which is proposed to hinder irrelevant information from being unnecessarily stored in memory. MT decreased neuronal activity in the BG part of the gatekeeper network but enhanced activity in the parietal storage node. We take these findings as evidence that FT renders working memory more efficient by strengthening the BG-prefrontal gatekeeper network. MT, on the other hand, simply stimulates storage of any kind of information. These findings illustrate a tight connection between working memory and attention, and they may open up new avenues for ameliorating memory deficits in patients with cognitive impairments.

Age-related changes in parietal lobe activation during an episodic memory retrieval task.

PubMed

Oedekoven, Christiane S H; Jansen, Andreas; Kircher, Tilo T; Leube, Dirk T

2013-05-01

The crucial role of lateral parietal regions in episodic memory has been confirmed in previous studies. While aging has an influence on retrieval of episodic memory, it remains to be examined how the involvement of lateral parietal regions in episodic memory changes with age. We investigated episodic memory retrieval in two age groups, using faces as stimuli and retrieval success as a measure of episodic memory. Young and elderly participants showed activation within a similar network, including lateral and medial parietal as well as prefrontal regions, but elderly showed a higher level of brain activation regardless of condition. Furthermore, we examined functional connectivity in the two age groups and found a more extensive network in the young group, including correlations of parietal and prefrontal regions. In the elderly, the overall stronger activation related to memory performance may indicate a compensatory process for a less extensive functional network.

Cognitive Control Network Contributions to Memory-Guided Visual Attention

PubMed Central

Rosen, Maya L.; Stern, Chantal E.; Michalka, Samantha W.; Devaney, Kathryn J.; Somers, David C.

2016-01-01

Visual attentional capacity is severely limited, but humans excel in familiar visual contexts, in part because long-term memories guide efficient deployment of attention. To investigate the neural substrates that support memory-guided visual attention, we performed a set of functional MRI experiments that contrast long-term, memory-guided visuospatial attention with stimulus-guided visuospatial attention in a change detection task. Whereas the dorsal attention network was activated for both forms of attention, the cognitive control network (CCN) was preferentially activated during memory-guided attention. Three posterior nodes in the CCN, posterior precuneus, posterior callosal sulcus/mid-cingulate, and lateral intraparietal sulcus exhibited the greatest specificity for memory-guided attention. These 3 regions exhibit functional connectivity at rest, and we propose that they form a subnetwork within the broader CCN. Based on the task activation patterns, we conclude that the nodes of this subnetwork are preferentially recruited for long-term memory guidance of visuospatial attention. PMID:25750253

Constructing Neuronal Network Models in Massively Parallel Environments.

PubMed

Ippen, Tammo; Eppler, Jochen M; Plesser, Hans E; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

Constructing Neuronal Network Models in Massively Parallel Environments

PubMed Central

Ippen, Tammo; Eppler, Jochen M.; Plesser, Hans E.; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers. PMID:28559808

A processing architecture for associative short-term memory in electronic noses

NASA Astrophysics Data System (ADS)

Pioggia, G.; Ferro, M.; Di Francesco, F.; DeRossi, D.

2006-11-01

Electronic nose (e-nose) architectures usually consist of several modules that process various tasks such as control, data acquisition, data filtering, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, and fuzzy rules used to implement such tasks may lead to issues concerning module interconnection and cooperation. Moreover, a new learning phase is mandatory once new measurements have been added to the dataset, thus causing changes in the previously derived model. Consequently, if a loss in the previous learning occurs (catastrophic interference), real-time applications of e-noses are limited. To overcome these problems this paper presents an architecture for dynamic and efficient management of multi-transducer data processing techniques and for saving an associative short-term memory of the previously learned model. The architecture implements an artificial model of a hippocampus-based working memory, enabling the system to be ready for real-time applications. Starting from the base models available in the architecture core, dedicated models for neurons, maps and connections were tailored to an artificial olfactory system devoted to analysing olive oil. In order to verify the ability of the processing architecture in associative and short-term memory, a paired-associate learning test was applied. The avoidance of catastrophic interference was observed.

A functional MRI study of working memory task in euthymic bipolar disorder: evidence for task-specific dysfunction.

PubMed

Monks, Paul J; Thompson, Jill M; Bullmore, Edward T; Suckling, John; Brammer, Michael J; Williams, Steve C R; Simmons, Andrew; Giles, Nicola; Lloyd, Adrian J; Harrison, C Louise; Seal, Marc; Murray, Robin M; Ferrier, I Nicol; Young, Allan H; Curtis, Vivienne A

2004-12-01

Even when euthymic bipolar disorder patients can have persistent deficits in working memory, but the neural basis of this deficit remains unclear. We undertook an functional magnetic resonance imaging investigation of euthymic bipolar disorder patients performing two working memory paradigms; the two-back and Sternberg tasks, selected to examine the central executive and the phonological loop respectively. We hypothesized that neuronal dysfunction would be specific to the network underlying the executive rather than the phonological loop component of working memory. Twelve right-handed euthymic bipolar I males receiving lithium carbonate monotherapy were matched with 12 controls. The two-back task comprised a single working memory load contrasted with baseline vigilance condition. The Sternberg paradigm used a parametric design incorporating variable working memory load with fixed delay between presentation of an array of items to be remembered and a target item. Functional activation data were acquired during performance of the tasks and were analysed to produce brain activation maps representing significant group differences in activation (ANOVA). Load-response curves were derived from the Sternberg task data set. There were no significant between-group differences (t-test) in performance of the two-back task, or in 2 x 5 group by memory load ANOVA for the performance data from Sternberg task. In the two-back task, compared with controls bipolar disorder patients showed reductions in bilateral frontal, temporal and parietal activation, and increased activations with the left precentral, right medial frontal and left supramarginal gyri. No between-group differences were observed in the Sternberg task at any working memory load. Our findings support the notion that, in euthymic bipolar disorder, failure to engage fronto-executive function underpins the core neuropsychological deficits. Blackwell Munksgaard, 2004

Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance.

PubMed

Keerativittayayut, Ruedeerat; Aoki, Ryuta; Sarabi, Mitra Taghizadeh; Jimura, Koji; Nakahara, Kiyoshi

2018-06-18

Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30-40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding. © 2018, Keerativittayayut et al.

Comparable Rest-related Promotion of Spatial Memory Consolidation in Younger and Older Adults

PubMed Central

Craig, Michael; Wolbers, Thomas; Harris, Mathew A.; Hauff, Patrick; Della Sala, Sergio; Dewar, Michaela

2017-01-01

Flexible spatial navigation depends on cognitive mapping, a function that declines with increasing age. In young adults, a brief period of post-navigation rest promotes the consolidation/integration of spatial memories into accurate cognitive maps. We examined (1) whether rest promotes spatial memory consolidation/integration in older adults and (2) whether the magnitude of the rest benefit changes with increasing age. Young and older adults learned a route through a virtual environment, followed by a 10min delay comprising either wakeful rest or a perceptual task, and a subsequent cognitive mapping task, requiring the pointing to landmarks from different locations. Pointing accuracy was lower in the older than younger adults. However, there was a comparable rest-related enhancement in pointing accuracy in the two age groups. Together our findings suggest that (i) the age-related decline in cognitive mapping cannot be explained by increased consolidation interference in older adults, and (ii) as we grow older rest continues to support the consolidation/integration of spatial memories. PMID:27689512

Fan Size and Foil Type in Recognition Memory.

ERIC Educational Resources Information Center

Walls, Richard T.; And Others

An experiment involving 20 graduate and undergraduate students (7 males and 13 females) at West Virginia University (Morgantown) assessed "fan network structures" of recognition memory. A fan in network memory structure occurs when several facts are connected into a single node (concept). The more links from that concept to various…

Repeated Stimulation of Cultured Networks of Rat Cortical Neurons Induces Parallel Memory Traces

ERIC Educational Resources Information Center

le Feber, Joost; Witteveen, Tim; van Veenendaal, Tamar M.; Dijkstra, Jelle

2015-01-01

During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and…

Associative memory and its cerebral correlates in Alzheimer׳s disease: evidence for distinct deficits of relational and conjunctive memory.

PubMed

Bastin, Christine; Bahri, Mohamed Ali; Miévis, Frédéric; Lemaire, Christian; Collette, Fabienne; Genon, Sarah; Simon, Jessica; Guillaume, Bénédicte; Diana, Rachel A; Yonelinas, Andrew P; Salmon, Eric

2014-10-01

This study investigated the impact of Alzheimer׳s disease (AD) on conjunctive and relational binding in episodic memory. Mild AD patients and controls had to remember item-color associations by imagining color either as a contextual association (relational memory) or as a feature of the item to be encoded (conjunctive memory). Patients׳ performance in each condition was correlated with cerebral metabolism measured by FDG-PET. The results showed that AD patients had an impaired capacity to remember item-color associations, with deficits in both relational and conjunctive memory. However, performance in the two kinds of associative memory varied independently across patients. Partial Least Square analyses revealed that poor conjunctive memory was related to hypometabolism in an anterior temporal-posterior fusiform brain network, whereas relational memory correlated with metabolism in regions of the default mode network. These findings support the hypothesis of distinct neural systems specialized in different types of associative memory and point to heterogeneous profiles of memory alteration in Alzheimer׳s disease as a function of damage to the respective neural networks. Copyright © 2014 Elsevier Ltd. All rights reserved.

Two Unipolar Terminal-Attractor-Based Associative Memories

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang; Wu, Chwan-Hwa

1995-01-01

Two unipolar mathematical models of electronic neural network functioning as terminal-attractor-based associative memory (TABAM) developed. Models comprise sets of equations describing interactions between time-varying inputs and outputs of neural-network memory, regarded as dynamical system. Simplifies design and operation of optoelectronic processor to implement TABAM performing associative recall of images. TABAM concept described in "Optoelectronic Terminal-Attractor-Based Associative Memory" (NPO-18790). Experimental optoelectronic apparatus that performed associative recall of binary images described in "Optoelectronic Inner-Product Neural Associative Memory" (NPO-18491).

Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice.

PubMed

Arime, Yosefu; Akiyama, Kazufumi

2017-01-01

Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC) and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP) mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days) of either saline or PCP (10 mg/kg): (1) a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2) brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s) in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2-3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells) in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2-3 of the prelimbic cortex of the PFC.

Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice

PubMed Central

Akiyama, Kazufumi

2017-01-01

Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC) and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP) mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days) of either saline or PCP (10 mg/kg): (1) a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2) brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s) in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2–3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells) in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2–3 of the prelimbic cortex of the PFC. PMID:29253020

Finding influential nodes for integration in brain networks using optimal percolation theory.

PubMed

Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A

2018-06-11

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

Enhanced storage capacity with errors in scale-free Hopfield neural networks: An analytical study.

PubMed

Kim, Do-Hyun; Park, Jinha; Kahng, Byungnam

2017-01-01

The Hopfield model is a pioneering neural network model with associative memory retrieval. The analytical solution of the model in mean field limit revealed that memories can be retrieved without any error up to a finite storage capacity of O(N), where N is the system size. Beyond the threshold, they are completely lost. Since the introduction of the Hopfield model, the theory of neural networks has been further developed toward realistic neural networks using analog neurons, spiking neurons, etc. Nevertheless, those advances are based on fully connected networks, which are inconsistent with recent experimental discovery that the number of connections of each neuron seems to be heterogeneous, following a heavy-tailed distribution. Motivated by this observation, we consider the Hopfield model on scale-free networks and obtain a different pattern of associative memory retrieval from that obtained on the fully connected network: the storage capacity becomes tremendously enhanced but with some error in the memory retrieval, which appears as the heterogeneity of the connections is increased. Moreover, the error rates are also obtained on several real neural networks and are indeed similar to that on scale-free model networks.

Detection of Brain Reorganization in Pediatric Multiple Sclerosis Using Functional MRI

DTIC Science & Technology

2015-10-01

accomplish this, we apply comparative assessments of fMRI mappings of language, memory , and motor function, and performance on clinical neurocognitive...community at a target rate of 13 volunteers per quarter period; acquire fMRI data for language, memory , and visual-motor functions (months 3-12). c...consensus fMRI activation maps for language, memory , and visual-motor tasks (months 8-12). f) Subtask 1f. Prepare publication to disseminate our

Mapping common aphasia assessments to underlying cognitive processes and their neural substrates

PubMed Central

Lacey, Elizabeth H.; Skipper-Kallal, LM; Xing, S; Fama, ME; Turkeltaub, PE

2017-01-01

Background Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. Objective To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Methods 25 behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high resolution MRI was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. Results The principal components analysis yielded four dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. Conclusions An extensive clinical aphasia assessment identifies four independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual’s specific pattern of deficits and preserved abilities. PMID:28135902

Neural networks supporting autobiographical memory retrieval in post-traumatic stress disorder

PubMed Central

Jacques, Peggy L.; Kragel, Philip A.; Rubin, David C.

2013-01-01

Post-traumatic stress disorder (PTSD) affects the functional recruitment and connectivity between neural regions during autobiographical memory (AM) retrieval that overlap with default and control networks. Whether such univariate changes relate to potential differences in the contribution of large-scale neural networks supporting cognition in PTSD is unknown. In the current functional MRI (fMRI) study we employ independent component analysis to examine the influence the engagement of neural networks during the recall of personal memories in PTSD (15 participants) compared to non-trauma exposed, healthy controls (14 participants). We found that the PTSD group recruited similar neural networks when compared to controls during AM recall, including default network subsystems and control networks, but there were group differences in the spatial and temporal characteristics of these networks. First, there were spatial differences in the contribution of the anterior and posterior midline across the networks, and with the amygdala in particular for the medial temporal subsystem of the default network. Second, there were temporal differences in the relationship of the medial prefrontal subsystem of the default network, with less temporal coupling of this network during AM retrieval in PTSD relative to controls. These findings suggest that spatial and temporal characteristics of the default and control networks potentially differ in PTSD versus healthy controls, and contribute to altered recall of personal memory. PMID:23483523

Soft Vibrational Modes Predict Breaking Events during Force-Induced Protein Unfolding.

PubMed

Habibi, Mona; Plotkin, Steven S; Rottler, Jörg

2018-02-06

We investigate the correlation between soft vibrational modes and unfolding events in simulated force spectroscopy of proteins. Unfolding trajectories are obtained from molecular dynamics simulations of a Gō model of a monomer of a mutant of superoxide dismutase 1 protein containing all heavy atoms in the protein, and a normal mode analysis is performed based on the anisotropic network model. We show that a softness map constructed from the superposition of the amplitudes of localized soft modes correlates with unfolding events at different stages of the unfolding process. Soft residues are up to eight times more likely to undergo disruption of native structure than the average amino acid. The memory of the softness map is retained for extensions of up to several nanometers, but decorrelates more rapidly during force drops. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Proteolytic Degradation of SCOP in the Hippocampus Contributes to Activation of MAP Kinase and Memory

PubMed Central

Shimizu, Kimiko; Phan, Trongha; Mansuy, Isabelle; Storm, Daniel R.

2007-01-01

Summary Because activation of Erk1/2 MAP kinase (MAPK) is critical for hippocampus-dependent memory, there is considerable interest in mechanisms for regulation of MAPK during memory formation. Here we report that MAPK and CREB-mediated transcription are negatively regulated by SCOP (SCN Circadian Oscillatory Protein) and that SCOP is proteolyzed by calpain when hippocampal neurons are stimulated by BDNF, KCl depolarization, or NMDA. Moreover, training for novel object memory decreases SCOP in the hippocampus. To determine if hippocampus-dependent memory is influenced by SCOP in vivo, we generated a transgenic mouse strain for the inducible overexpression of SCOP in the forebrain. Overexpression of SCOP completely blocked memory for novel objects. We conclude that degradation of SCOP by calpain contributes to activation of MAPK during memory formation. PMID:17382888

Frequency–specific network connectivity increases underlie accurate spatiotemporal memory retrieval

PubMed Central

Watrous, Andrew J.; Tandon, Nitin; Connor, Chris; Pieters, Thomas; Ekstrom, Arne D.

2013-01-01

The medial temporal lobes, prefrontal cortex, and parts of parietal cortex form the neural underpinnings of episodic memory, which includes remembering both where and when an event occurred. Yet how these three key regions interact during retrieval of spatial and temporal context remains largely untested. Here, we employed simultaneous electrocorticographical recordings across multiple lobular regions, employing phase synchronization as a measure of network functional connectivity, while patients retrieved spatial and temporal context associated with an episode. Successful memory retrieval was characterized by greater global connectivity compared to incorrect retrieval, with the MTL acting as a convergence hub for these interactions. Spatial vs. temporal context retrieval resulted in prominent differences in both the spectral and temporal patterns of network interactions. These results emphasize dynamic network interactions as central to episodic memory retrieval, providing novel insight into how multiple contexts underlying a single event can be recreated within the same network. PMID:23354333

Effect of memory in non-Markovian Boolean networks illustrated with a case study: A cell cycling process

NASA Astrophysics Data System (ADS)

Ebadi, H.; Saeedian, M.; Ausloos, M.; Jafari, G. R.

2016-11-01

The Boolean network is one successful model to investigate discrete complex systems such as the gene interacting phenomenon. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self-organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function —one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of the yeast cell cycle network, we discover a power-law-like memory with a more robust dynamics than the Markovian dynamics.

Resting connectivity between salience nodes predicts recognition memory.

PubMed

Andreano, Joseph M; Touroutoglou, Alexandra; Dickerson, Bradford C; Barrett, Lisa F

2017-06-01

The resting connectivity of the brain's salience network, particularly the ventral subsystem of the salience network, has been previously associated with various measures of affective reactivity. Numerous studies have demonstrated that increased affective arousal leads to enhanced consolidation of memory. This suggests that individuals with greater ventral salience network connectivity will exhibit greater responses to affective experience, leading to a greater enhancement of memory by affect. To test this hypothesis, resting ventral salience connectivity was measured in 41 young adults, who were then exposed to neutral and negative affect inductions during a paired associate memory test. Memory performance for material learned under both negative and neutral induction was tested for correlation with resting connectivity between major ventral salience nodes. The results showed a significant interaction between mood induction (negative vs neutral) and connectivity between ventral anterior insula and pregenual anterior cingulate cortex, indicating that salience node connectivity predicted memory for material encoded under negative, but not neutral induction. These findings suggest that the network state of the perceiver, measured prior to affective experience, meaningfully influences the extent to which affect modulates memory. Implications of these findings for individuals with affective disorder, who show alterations in both connectivity and memory, are considered. © The Author (2017). Published by Oxford University Press.

Abnormal Neural Network of Primary Insomnia: Evidence from Spatial Working Memory Task fMRI.

PubMed

Li, Yongli; Liu, Liya; Wang, Enfeng; Zhang, Hongju; Dou, Shewei; Tong, Li; Cheng, Jingliang; Chen, Chuanliang; Shi, Dapeng

2016-01-01

Contemporary functional MRI (fMRI) methods can provide a wealth of information about the neural mechanisms associated with primary insomnia (PI), which centrally involve neural network circuits related to spatial working memory. A total of 30 participants diagnosed with PI and without atypical brain anatomy were selected along with 30 age- and gender-matched healthy controls. Subjects were administered the Pittsburgh Sleep Quality Index (PSQI), Hamilton Rating Scale for Depression and clinical assessments of spatial working memory, followed by an MRI scan and fMRI in spatial memory task state. Statistically significant differences between PSQI and spatial working memory were observed between PI patients and controls (p < 0.01). Activation of neural networks related to spatial memory task state in the PI group was observed at the left temporal lobe, left occipital lobe and right frontal lobe. Lower levels of activation were observed in the left parahippocampal gyrus, right parahippocampal gyrus, bilateral temporal cortex, frontal cortex and superior parietal lobule. Participants with PI exhibited characteristic abnormalities in the neural network connectivity related to spatial working memory. These results may be indicative of an underlying pathological mechanism related to spatial working memory deterioration in PI, analogous to recently described mechanisms in other mental health disorders. © 2016 S. Karger AG, Basel.

Syntactic Complexity and Frequency in the Neurocognitive Language System.

PubMed

Yang, Yun-Hsuan; Marslen-Wilson, William D; Bozic, Mirjana

2017-09-01

Prominent neurobiological models of language follow the widely accepted assumption that language comprehension requires two principal mechanisms: a lexicon storing the sound-to-meaning mapping of words, primarily involving bilateral temporal regions, and a combinatorial processor for syntactically structured items, such as phrases and sentences, localized in a left-lateralized network linking left inferior frontal gyrus (LIFG) and posterior temporal areas. However, recent research showing that the processing of simple phrasal sequences may engage only bilateral temporal areas, together with the claims of distributional approaches to grammar, raise the question of whether frequent phrases are stored alongside individual words in temporal areas. In this fMRI study, we varied the frequency of words and of short and long phrases in English. If frequent phrases are indeed stored, then only less frequent items should generate selective left frontotemporal activation, because memory traces for such items would be weaker or not available in temporal cortex. Complementary univariate and multivariate analyses revealed that, overall, simple words (verbs) and long phrases engaged LIFG and temporal areas, whereas short phrases engaged bilateral temporal areas, suggesting that syntactic complexity is a key factor for LIFG activation. Although we found a robust frequency effect for words in temporal areas, no frequency effects were found for the two phrasal conditions. These findings support the conclusion that long and short phrases are analyzed, respectively, in the left frontal network and in a bilateral temporal network but are not retrieved from memory in the same way as simple words during spoken language comprehension.

Hybrid computing using a neural network with dynamic external memory.

PubMed

Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

2016-10-27

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

Array processor architecture connection network

NASA Technical Reports Server (NTRS)

Barnes, George H. (Inventor); Lundstrom, Stephen F. (Inventor); Shafer, Philip E. (Inventor)

1982-01-01

A connection network is disclosed for use between a parallel array of processors and a parallel array of memory modules for establishing non-conflicting data communications paths between requested memory modules and requesting processors. The connection network includes a plurality of switching elements interposed between the processor array and the memory modules array in an Omega networking architecture. Each switching element includes a first and a second processor side port, a first and a second memory module side port, and control logic circuitry for providing data connections between the first and second processor ports and the first and second memory module ports. The control logic circuitry includes strobe logic for examining data arriving at the first and the second processor ports to indicate when the data arriving is requesting data from a requesting processor to a requested memory module. Further, connection circuitry is associated with the strobe logic for examining requesting data arriving at the first and the second processor ports for providing a data connection therefrom to the first and the second memory module ports in response thereto when the data connection so provided does not conflict with a pre-established data connection currently in use.

An investigation of the relationships between junior high school students' (8th and 9th grades) background variables and structure of knowledge recall of biological content

NASA Astrophysics Data System (ADS)

Demetrius, Olive Joyce

The purpose of this study was to examine the relationships between Junior High School students' (8th and 9th grades) background variables (e.g. cognitive factors, prior knowledge, preference for science versus non-science activities, formal and informal activities) and structure of information recall of biological content. In addition, this study will illustrate how flow maps, a graphic display, designed to represent the sequential flow and cross linkage of ideas in information recalled by the learner can be used as a tool for analyzing science learning data. The participants (46 junior high school students) were taught a lesson on the human digestive system during which they were shown a model of the human torso. Their pattern of information recall was determined by using an interview technique to elicit their understanding of the functional anatomy of the human digestive system. The taped responses were later transcribed for construction of the flow map. The interview was also used to assess knowledge recall of biological content. The flow map, science interest questionnaire and the cognitive operations (based on content analysis of student's narrative) were used to analyze data from each respondent. This is a case study using individual subjects and interview techniques. The findings of this study are: (1) Based on flow map data higher academic ability students have more networking of ideas than low ability students. (2) A large percentage of 9th grade low ability students intend to pursue science/applied science course work after leaving school but they lack well organized ways of representing science knowledge in memory. (3) Content analysis of the narratives shows that students with more complex ideational networks use higher order cognitive thought processes compared to those with less networking of ideas. If students are to make a successful transition from low academic performance to high academic performance it seems that more emphasis should be placed on information networking skills. This is specifically likely to be productive for student currently performing on low academic ability levels and yet have high aspirations for pursuing science as a career.

Distributed simulation using a real-time shared memory network

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Mattern, Duane L.; Wong, Edmond; Musgrave, Jeffrey L.

1993-01-01

The Advanced Control Technology Branch of the NASA Lewis Research Center performs research in the area of advanced digital controls for aeronautic and space propulsion systems. This work requires the real-time implementation of both control software and complex dynamical models of the propulsion system. We are implementing these systems in a distributed, multi-vendor computer environment. Therefore, a need exists for real-time communication and synchronization between the distributed multi-vendor computers. A shared memory network is a potential solution which offers several advantages over other real-time communication approaches. A candidate shared memory network was tested for basic performance. The shared memory network was then used to implement a distributed simulation of a ramjet engine. The accuracy and execution time of the distributed simulation was measured and compared to the performance of the non-partitioned simulation. The ease of partitioning the simulation, the minimal time required to develop for communication between the processors and the resulting execution time all indicate that the shared memory network is a real-time communication technique worthy of serious consideration.

Neural network modeling of associative memory: Beyond the Hopfield model

NASA Astrophysics Data System (ADS)

Dasgupta, Chandan

1992-07-01

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

Gps-Denied Geo-Localisation Using Visual Odometry

NASA Astrophysics Data System (ADS)

Gupta, Ashish; Chang, Huan; Yilmaz, Alper

2016-06-01

The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile platform. Our approach has two principal components: public domain transport network data available in GIS databases or OpenStreetMap; and a trajectory of a mobile platform. This trajectory is estimated using visual odometry and 3D view geometry. The transport map information is abstracted as a graph data structure, where various types of roads are modelled as graph edges and typically intersections are modelled as graph nodes. A search for the trajectory in real time in the graph yields the geo-location of the mobile platform. Our approach uses a simple visual sensor and it has a low memory and computational footprint. In this paper, we demonstrate our method for trajectory estimation and provide examples of geolocalization using public-domain map data. With the rapid proliferation of visual sensors as part of automated driving technology and continuous growth in public domain map data, our approach has the potential to completely augment, or even supplant, GPS based navigation since it functions in all environments.

Performance of asynchronous transfer mode (ATM) local area and wide area networks for medical imaging transmission in clinical environment.

PubMed

Huang, H K; Wong, A W; Zhu, X

1997-01-01

Asynchronous transfer mode (ATM) technology emerges as a leading candidate for medical image transmission in both local area network (LAN) and wide area network (WAN) applications. This paper describes the performance of an ATM LAN and WAN network at the University of California, San Francisco. The measurements were obtained using an intensive care unit (ICU) server connecting to four image workstations (WS) at four different locations of a hospital-integrated picture archiving and communication system (HI-PACS) in a daily regular clinical environment. Four types of performance were evaluated: magnetic disk-to-disk, disk-to-redundant array of inexpensive disks (RAID), RAID-to-memory, and memory-to-memory. Results demonstrate that the transmission rate between two workstations can reach 5-6 Mbytes/s from RAID-to-memory, and 8-10 Mbytes/s from memory-to-memory. When the server has to send images to all four workstations simultaneously, the transmission rate to each WS is about 4 Mbytes/s. Both situations are adequate for radiologic image communications for picture archiving and communication systems (PACS) and teleradiology applications.

Lesion mapping of social problem solving.

PubMed

Barbey, Aron K; Colom, Roberto; Paul, Erick J; Chau, Aileen; Solomon, Jeffrey; Grafman, Jordan H

2014-10-01

Accumulating neuroscience evidence indicates that human intelligence is supported by a distributed network of frontal and parietal regions that enable complex, goal-directed behaviour. However, the contributions of this network to social aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 144) that investigates the neural bases of social problem solving (measured by the Everyday Problem Solving Inventory) and examine the degree to which individual differences in performance are predicted by a broad spectrum of psychological variables, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores for each variable were obtained, followed by voxel-based lesion-symptom mapping. Stepwise regression analyses revealed that working memory, processing speed, and emotional intelligence predict individual differences in everyday problem solving. A targeted analysis of specific everyday problem solving domains (involving friends, home management, consumerism, work, information management, and family) revealed psychological variables that selectively contribute to each. Lesion mapping results indicated that social problem solving, psychometric intelligence, and emotional intelligence are supported by a shared network of frontal, temporal, and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The results support an integrative framework for understanding social intelligence and make specific recommendations for the application of the Everyday Problem Solving Inventory to the study of social problem solving in health and disease. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Connecting Spatial Memories of Two Nested Spaces

ERIC Educational Resources Information Center

Zhang, Hui; Mou, Weimin; McNamara, Timothy P.; Wang, Lin

2014-01-01

Four experiments investigated the manner in which people use spatial reference directions to organize spatial memories of 2 conceptually nested layouts. Participants learned directions of 8 remote cities centered to Beijing or Edmonton, where the experiments occurred, using a map or using direct pointing. The map and the environment were aligned,…

Swarming UAS II

DTIC Science & Technology

2010-05-05

employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map, allowing pheromone-like...matter of design, DSE-R-0808 employed biomimicry to model a swarm of UAS as a colony of ants, where each UAS dynamically updates a global memory map

On Data Transfers Over Wide-Area Dedicated Connections

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

Rao, Nageswara S.; Liu, Qiang

Dedicated wide-area network connections are employed in big data and high-performance computing scenarios, since the absence of cross-traffic promises to make it easier to analyze and optimize data transfers over them. However, nonlinear transport dynamics and end-system complexity due to multi-core hosts and distributed file systems make these tasks surprisingly challenging. We present an overview of methods to analyze memory and disk file transfers using extensive measurements over 10 Gbps physical and emulated connections with 0–366 ms round trip times (RTTs). For memory transfers, we derive performance profiles of TCP and UDT throughput as a function of RTT, which showmore » concave regions in contrast to entirely convex regions predicted by previous models. These highly desirable concave regions can be expanded by utilizing large buffers and more parallel flows. We also present Poincar´e maps and Lyapunov exponents of TCP and UDT throughputtraces that indicate complex throughput dynamics. For disk file transfers, we show that throughput can be optimized using a combination of parallel I/O and network threads under direct I/O mode. Our initial throughput measurements of Lustre filesystems mounted over long-haul connections using LNet routers show convex profiles indicative of I/O limits.« less

On the effect of memory in one-dimensional K=4 automata on networks

NASA Astrophysics Data System (ADS)

Alonso-Sanz, Ramón; Cárdenas, Juan Pablo

2008-12-01

The effect of implementing memory in cells of one-dimensional CA, and on nodes of various types of automata on networks with increasing degrees of random rewiring is studied in this article, paying particular attention to the case of four inputs. As a rule, memory induces a moderation in the rate of changing nodes and in the damage spreading, albeit in the latter case memory turns out to be ineffective in the control of the damage as the wiring network moves away from the ordered structure that features proper one-dimensional CA. This article complements the previous work done in the two-dimensional context.

Object-Location Memory: A Lesion-Behavior Mapping Study in Stroke Patients

ERIC Educational Resources Information Center

van Asselen, Marieke; Kessels, Roy P. C.; Frijns, Catharina J. M.; Kappelle, L. Jaap; Neggers, Sebastiaan F. W.; Postma, Albert

2009-01-01

Object-location memory is an important form of spatial memory, comprising different subcomponents that each process specific types of information within memory, i.e. remembering objects, remembering positions and binding these features in memory. In the current study we investigated the neural correlates of binding categorical (relative) or…

Episodic memory retrieval, parietal cortex, and the Default Mode Network: functional and topographic analyses

PubMed Central

Sestieri, Carlo; Corbetta, Maurizio; Romani, Gian Luca; Shulman, Gordon L.

2011-01-01

The default mode network (DMN) is often considered a functionally homogeneous system that is broadly associated with internally directed cognition (e.g. episodic memory, theory of mind, self-evaluation). However, few studies have examined how this network interacts with other networks during putative “default” processes such as episodic memory retrieval. Using fMRI, we investigated the topography and response profile of human parietal regions inside and outside the DMN, independently defined using task-evoked deactivations and resting state functional connectivity, during episodic memory retrieval. Memory retrieval activated posterior nodes of the DMN, particularly the angular gyrus, but also more anterior and dorsal parietal regions that were anatomically separate from the DMN. The two sets of parietal regions showed different resting-state functional connectivity and response profiles. During memory retrieval, responses in DMN regions peaked sooner than non-DMN regions, which in turn showed responses that were sustained until a final memory judgment was reached. Moreover, a parahippocampal region that showed strong resting-state connectivity with parietal DMN regions also exhibited a pattern of task-evoked activity similar to that exhibited by DMN regions. These results suggest that DMN parietal regions directly supported memory retrieval, whereas non-DMN parietal regions were more involved in post-retrieval processes such as memory-based decision making. Finally, a robust functional dissociation within the DMN was observed. While angular gyrus and posterior cingulate/precuneus were significantly activated during memory retrieval, an anterior DMN node in medial prefrontal cortex was strongly deactivated. This latter finding demonstrates functional heterogeneity rather than homogeneity within the DMN during episodic memory retrieval. PMID:21430142

Episodic memory retrieval, parietal cortex, and the default mode network: functional and topographic analyses.

PubMed

Sestieri, Carlo; Corbetta, Maurizio; Romani, Gian Luca; Shulman, Gordon L

2011-03-23

The default mode network (DMN) is often considered a functionally homogeneous system that is broadly associated with internally directed cognition (e.g., episodic memory, theory of mind, self-evaluation). However, few studies have examined how this network interacts with other networks during putative "default" processes such as episodic memory retrieval. Using functional magnetic resonance imaging, we investigated the topography and response profile of human parietal regions inside and outside the DMN, independently defined using task-evoked deactivations and resting-state functional connectivity, during episodic memory retrieval. Memory retrieval activated posterior nodes of the DMN, particularly the angular gyrus, but also more anterior and dorsal parietal regions that were anatomically separate from the DMN. The two sets of parietal regions showed different resting-state functional connectivity and response profiles. During memory retrieval, responses in DMN regions peaked sooner than non-DMN regions, which in turn showed responses that were sustained until a final memory judgment was reached. Moreover, a parahippocampal region that showed strong resting-state connectivity with parietal DMN regions also exhibited a pattern of task-evoked activity similar to that exhibited by DMN regions. These results suggest that DMN parietal regions directly supported memory retrieval, whereas non-DMN parietal regions were more involved in postretrieval processes such as memory-based decision making. Finally, a robust functional dissociation within the DMN was observed. Whereas angular gyrus and posterior cingulate/precuneus were significantly activated during memory retrieval, an anterior DMN node in medial prefrontal cortex was strongly deactivated. This latter finding demonstrates functional heterogeneity rather than homogeneity within the DMN during episodic memory retrieval.

An artificial immune system approach with secondary response for misbehavior detection in mobile ad hoc networks.

PubMed

Sarafijanović, Slavisa; Le Boudec, Jean-Yves

2005-09-01

In mobile ad hoc networks, nodes act both as terminals and information relays, and they participate in a common routing protocol, such as dynamic source routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper, we investigate the use of an artificial immune system (AIS) to detect node misbehavior in a mobile ad hoc network using DSR. The system is inspired by the natural immune system (IS) of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns, and detects new misbehavior. We describe our solution for the classification task of the AIS; it employs negative selection and clonal selection, the algorithms for learning and adaptation used by the natural IS. We define how we map the natural IS concepts such as self, antigen, and antibody to a mobile ad hoc network and give the resulting algorithm for classifying nodes as misbehaving. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters affect the performance of primary and secondary response. Further steps will extend the design by using an analogy to the innate system, danger signal, and memory cells.

Exploring Maps with Greedy Navigators

NASA Astrophysics Data System (ADS)

Lee, Sang Hoon; Holme, Petter

2012-03-01

During the last decade of network research focusing on structural and dynamical properties of networks, the role of network users has been more or less underestimated from the bird’s-eye view of global perspective. In this era of global positioning system equipped smartphones, however, a user’s ability to access local geometric information and find efficient pathways on networks plays a crucial role, rather than the globally optimal pathways. We present a simple greedy spatial navigation strategy as a probe to explore spatial networks. These greedy navigators use directional information in every move they take, without being trapped in a dead end based on their memory about previous routes. We suggest that the centralities measures have to be modified to incorporate the navigators’ behavior, and present the intriguing effect of navigators’ greediness where removing some edges may actually enhance the routing efficiency, which is reminiscent of Braess’s paradox. In addition, using samples of road structures in large cities around the world, it is shown that the navigability measure we define reflects unique structural properties, which are not easy to predict from other topological characteristics. In this respect, we believe that our routing scheme significantly moves the routing problem on networks one step closer to reality, incorporating the inevitable incompleteness of navigators’ information.

Adiabatic quantum optimization for associative memory recall

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

Seddiqi, Hadayat; Humble, Travis S.

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are storedmore » in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.« less

Adiabatic Quantum Optimization for Associative Memory Recall

NASA Astrophysics Data System (ADS)

Seddiqi, Hadayat; Humble, Travis

2014-12-01

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are stored in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.

Large-Scale Fluorescence Calcium-Imaging Methods for Studies of Long-Term Memory in Behaving Mammals

PubMed Central

Jercog, Pablo; Rogerson, Thomas; Schnitzer, Mark J.

2016-01-01

During long-term memory formation, cellular and molecular processes reshape how individual neurons respond to specific patterns of synaptic input. It remains poorly understood how such changes impact information processing across networks of mammalian neurons. To observe how networks encode, store, and retrieve information, neuroscientists must track the dynamics of large ensembles of individual cells in behaving animals, over timescales commensurate with long-term memory. Fluorescence Ca2+-imaging techniques can monitor hundreds of neurons in behaving mice, opening exciting avenues for studies of learning and memory at the network level. Genetically encoded Ca2+ indicators allow neurons to be targeted by genetic type or connectivity. Chronic animal preparations permit repeated imaging of neural Ca2+ dynamics over multiple weeks. Together, these capabilities should enable unprecedented analyses of how ensemble neural codes evolve throughout memory processing and provide new insights into how memories are organized in the brain. PMID:27048190

Adiabatic quantum optimization for associative memory recall

DOE PAGES

Seddiqi, Hadayat; Humble, Travis S.

2014-12-22

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are storedmore » in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.« less

[Extinction and Reconsolidation of Memory].

PubMed

Zuzina, A B; Balaban, P M

2015-01-01

Retrieval of memory followed by reconsolidation can strengthen a memory, while retrieval followed by extinction results in a decrease of memory performance due to weakening of existing memory or formation of a competing memory. In our study we analyzed the behavior and responses of identified neurons involved in the network underlying aversive learning in terrestrial snail Helix, and made an attempt to describe the conditions in which the retrieval of memory leads either to extinction or reconsolidation. In the network underlying the withdrawal behavior, sensory neurons, premotor interneurons, motor neurons, and modulatory for this network serotonergic neurons are identified and recordings from representatives of these groups were made before and after aversive learning. In the network underlying feeding behavior, the premotor modulatory serotonergic interneurons and motor neurons involved in motor program of feeding are identified. Analysis of changes in neural activity after aversive learning showed that modulatory neurons of feeding behavior do not demonstrate any changes (sometimes a decrease of responses to food was observed), while responses to food in withdrawal behavior premotor interneurons changed qualitatively, from under threshold EPSPs to spike discharges. Using a specific for serotonergic neurons neurotoxin 5,7-DiHT it was shown previously that the serotonergic system is necessary for the aversive learning, but is not necessary for maintenance and retrieval of this memory. These results suggest that the serotonergic neurons that are necessary as part of a reinforcement for developing the associative changes in the network may be not necessary for the retrieval of memory. The hypothesis presented in this review concerns the activity of the "reinforcement" serotonergic neurons that is suggested to be the gate condition for the choice between extinction/reconsolidation triggered by memory retrieval: if these serotonergic neurons do not respond during the retrieval due to adaptation, habituation, changes in environment, etc., then we will observe the extinction; while if these neurons respond to the CS during memory retrieval, we will observe the reconsolidation phenomenon.

Short-term memory capacity in networks via the restricted isometry property.

PubMed

Charles, Adam S; Yap, Han Lun; Rozell, Christopher J

2014-06-01

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

Cortical Memory Mechanisms and Language Origins

ERIC Educational Resources Information Center

Aboitiz, Francisco; Garcia, Ricardo R.; Bosman, Conrado; Brunetti, Enzo

2006-01-01

We have previously proposed that cortical auditory-vocal networks of the monkey brain can be partly homologized with language networks that participate in the phonological loop. In this paper, we suggest that other linguistic phenomena like semantic and syntactic processing also rely on the activation of transient memory networks, which can be…

Hierarchically clustered adaptive quantization CMAC and its learning convergence.

PubMed

Teddy, S D; Lai, E M K; Quek, C

2007-11-01

The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC), learning convergence, nonuniform quantization.

Imagining the future: The core episodic simulation network dissociates as a function of timecourse and the amount of simulated information

PubMed Central

Thakral, Preston P.; Benoit, Roland G.; Schacter, Daniel L.

2017-01-01

Neuroimaging data indicate that episodic memory (i.e., remembering specific past experiences) and episodic simulation (i.e., imagining specific future experiences) are associated with enhanced activity in a common set of neural regions, often referred to as the core network. This network comprises the hippocampus, parahippocampal cortex, lateral and medial parietal cortex, lateral temporal cortex, and medial prefrontal cortex. Evidence for a core network has been taken as support for the idea that episodic memory and episodic simulation are supported by common processes. Much remains to be learned about how specific core network regions contribute to specific aspects of episodic simulation. Prior neuroimaging studies of episodic memory indicate that certain regions within the core network are differentially sensitive to the amount of information recollected (e.g., the left lateral parietal cortex). In addition, certain core network regions dissociate as a function of their timecourse of engagement during episodic memory (e.g., transient activity in the posterior hippocampus and sustained activity in the left lateral parietal cortex). In the current study, we assessed whether similar dissociations could be observed during episodic simulation. We found that the left lateral parietal cortex modulates as a function of the amount of simulated details. Of particular interest, while the hippocampus was insensitive to the amount of simulated details, we observed a temporal dissociation within the hippocampus: transient activity occurred in relatively posterior portions of the hippocampus and sustained activity occurred in anterior portions. Because the posterior hippocampal and lateral parietal findings parallel those observed previously during episodic memory, the present results add to the evidence that episodic memory and episodic simulation are supported by common processes. Critically, the present study also provides evidence that regions within the core network support dissociable processes. PMID:28324695

Factors affecting reorganisation of memory encoding networks in temporal lobe epilepsy

PubMed Central

Sidhu, M.K.; Stretton, J.; Winston, G.P.; Symms, M.; Thompson, P.J.; Koepp, M.J.; Duncan, J.S.

2015-01-01

Summary Aims In temporal lobe epilepsy (TLE) due to hippocampal sclerosis reorganisation in the memory encoding network has been consistently described. Distinct areas of reorganisation have been shown to be efficient when associated with successful subsequent memory formation or inefficient when not associated with successful subsequent memory. We investigated the effect of clinical parameters that modulate memory functions: age at onset of epilepsy, epilepsy duration and seizure frequency in a large cohort of patients. Methods We studied 53 patients with unilateral TLE and hippocampal sclerosis (29 left). All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words. A continuous regression analysis was used to investigate the effects of age at onset of epilepsy, epilepsy duration and seizure frequency on the activation patterns in the memory encoding network. Results Earlier age at onset of epilepsy was associated with left posterior hippocampus activations that were involved in successful subsequent memory formation in left hippocampal sclerosis patients. No association of age at onset of epilepsy was seen with face encoding in right hippocampal sclerosis patients. In both left hippocampal sclerosis patients during word encoding and right hippocampal sclerosis patients during face encoding, shorter duration of epilepsy and lower seizure frequency were associated with medial temporal lobe activations that were involved in successful memory formation. Longer epilepsy duration and higher seizure frequency were associated with contralateral extra-temporal activations that were not associated with successful memory formation. Conclusion Age at onset of epilepsy influenced verbal memory encoding in patients with TLE due to hippocampal sclerosis in the speech-dominant hemisphere. Shorter duration of epilepsy and lower seizure frequency were associated with less disruption of the efficient memory encoding network whilst longer duration and higher seizure frequency were associated with greater, inefficient, extra-temporal reorganisation. PMID:25616449

A Memory Efficient Network Encryption Scheme

NASA Astrophysics Data System (ADS)

El-Fotouh, Mohamed Abo; Diepold, Klaus

In this paper, we studied the two widely used encryption schemes in network applications. Shortcomings have been found in both schemes, as these schemes consume either more memory to gain high throughput or low memory with low throughput. The need has aroused for a scheme that has low memory requirements and in the same time possesses high speed, as the number of the internet users increases each day. We used the SSM model [1], to construct an encryption scheme based on the AES. The proposed scheme possesses high throughput together with low memory requirements.

Oxytocin And Vasopressin Modulation Of The Neural Correlates Of Motivation And Emotion: Results From Functional MRI Studies In Awake Rats

PubMed Central

Febo, Marcelo; Ferris, Craig F.

2014-01-01

Oxytocin and vasopressin modulate a range of species typical behavioral functions that include social recognition, maternal-infant attachment, and modulation of memory, offensive aggression, defensive fear reactions, and reward seeking. We have employed novel functional magnetic resonance mapping techniques in awake rats to explore the roles of these neuropeptides in the maternal and non-maternal brain. Results from the functional neuroimaging studies that are summarized here have directly and indirectly confirmed and supported previous findings. Oxytocin is released within the lactating rat brain during suckling stimulation and activates specific subcortical networks in the maternal brain. Both vasopressin and oxytocin modulate brain regions involved unconditioned fear, processing of social stimuli and the expression of agonistic behaviors. Across studies there are relatively consistent brain networks associated with internal motivational drives and emotional states that are modulated by oxytocin and vasopressin. PMID:24486356

Fast and Accurate Simulation of the Cray XMT Multithreaded Supercomputer

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

Villa, Oreste; Tumeo, Antonino; Secchi, Simone

Irregular applications, such as data mining and analysis or graph-based computations, show unpredictable memory/network access patterns and control structures. Highly multithreaded architectures with large processor counts, like the Cray MTA-1, MTA-2 and XMT, appear to address their requirements better than commodity clusters. However, the research on highly multithreaded systems is currently limited by the lack of adequate architectural simulation infrastructures due to issues such as size of the machines, memory footprint, simulation speed, accuracy and customization. At the same time, Shared-memory MultiProcessors (SMPs) with multi-core processors have become an attractive platform to simulate large scale machines. In this paper, wemore » introduce a cycle-level simulator of the highly multithreaded Cray XMT supercomputer. The simulator runs unmodified XMT applications. We discuss how we tackled the challenges posed by its development, detailing the techniques introduced to make the simulation as fast as possible while maintaining a high accuracy. By mapping XMT processors (ThreadStorm with 128 hardware threads) to host computing cores, the simulation speed remains constant as the number of simulated processors increases, up to the number of available host cores. The simulator supports zero-overhead switching among different accuracy levels at run-time and includes a network model that takes into account contention. On a modern 48-core SMP host, our infrastructure simulates a large set of irregular applications 500 to 2000 times slower than real time when compared to a 128-processor XMT, while remaining within 10\\% of accuracy. Emulation is only from 25 to 200 times slower than real time.« less

Chronic spatial working memory deficit associated with the superior longitudinal fasciculus: a study using voxel-based lesion-symptom mapping and intraoperative direct stimulation in right prefrontal glioma surgery.

PubMed

Kinoshita, Masashi; Nakajima, Riho; Shinohara, Harumichi; Miyashita, Katsuyoshi; Tanaka, Shingo; Okita, Hirokazu; Nakada, Mitsutoshi; Hayashi, Yutaka

2016-10-01

OBJECTIVE Although the right prefrontal region is regarded as a silent area, chronic deficits of the executive function, including working memory (WM), could occur after resection of a right prefrontal glioma. This may be overlooked by postoperative standard examinations, and the disabilities could affect the patient's professional life. The right prefrontal region is a part of the frontoparietal network and is subserved by the superior longitudinal fasciculus (SLF); however, the role of the SLF in spatial WM is unclear. This study investigated a persistent spatial WM deficit in patients who underwent right prefrontal glioma resection, and evaluated the relationship between the spatial WM deficit and the SLF. METHODS Spatial WM was examined in 24 patients who underwent prefrontal glioma resection (right, n = 14; left, n = 10) and in 14 healthy volunteers using a spatial 2-back task during the long-term postoperative period. The neural correlates of spatial WM were evaluated using lesion mapping and voxel-based lesion-symptom mapping. In addition, the spatial 2-back task was performed during surgery under direct subcortical electrical stimulation in 2 patients with right prefrontal gliomas. RESULTS Patients with a right prefrontal lesion had a significant chronic spatial WM deficit. Voxel-based lesion-symptom mapping analysis revealed a significant correlation between spatial WM deficit and the region that overlapped the first and second segments of the SLF (SLF I and SLF II). Two patients underwent awake surgery and had difficulties providing the correct responses in the spatial 2-back task with direct subcortical electrical stimulation on the SLF I, which was preserved and confirmed by postoperative diffusion tensor imaging tractography. These patients exhibited no spatial WM deficits during the postoperative immediate and long-term periods. CONCLUSIONS Spatial WM deficits may persist in patients who undergo resection of the tumor located in the right prefrontal brain parenchyma. Injury to the dorsal frontoparietal subcortical white matter pathway, i.e., the SLF I or SLF I and II, could play a causal role in this chronic deficit. A persistent spatial WM deficit, without motor and language deficits, could affect the professional life of the patient. In such cases, awake surgery would be useful to detect the spatial WM network with appropriate task during tumor exploration.

Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment.

PubMed

Bai, Feng; Zhang, Zhijun; Watson, David R; Yu, Hui; Shi, Yongmei; Yuan, Yonggui; Zang, Yufeng; Zhu, Chaozhe; Qian, Yun

2009-06-01

Functional connectivity magnetic resonance imaging technique has revealed the importance of distributed network structures in higher cognitive processes in the human brain. The hippocampus has a key role in a distributed network supporting memory encoding and retrieval. Hippocampal dysfunction is a recurrent finding in memory disorders of aging such as amnestic mild cognitive impairment (aMCI) in which learning- and memory-related cognitive abilities are the predominant impairment. The functional connectivity method provides a novel approach in our attempts to better understand the changes occurring in this structure in aMCI patients. Functional connectivity analysis was used to examine episodic memory retrieval networks in vivo in twenty 28 aMCI patients and 23 well-matched control subjects, specifically between the hippocampal structures and other brain regions. Compared with control subjects, aMCI patients showed significantly lower hippocampus functional connectivity in a network involving prefrontal lobe, temporal lobe, parietal lobe, and cerebellum, and higher functional connectivity to more diffuse areas of the brain than normal aging control subjects. In addition, those regions associated with increased functional connectivity with the hippocampus demonstrated a significantly negative correlation to episodic memory performance. aMCI patients displayed altered patterns of functional connectivity during memory retrieval. The degree of this disturbance appears to be related to level of impairment of processes involved in memory function. Because aMCI is a putative prodromal syndrome to Alzheimer's disease (AD), these early changes in functional connectivity involving the hippocampus may yield important new data to predict whether a patient will eventually develop AD.

Sequential associative memory with nonuniformity of the layer sizes.

PubMed

Teramae, Jun-Nosuke; Fukai, Tomoki

2007-01-01

Sequence retrieval has a fundamental importance in information processing by the brain, and has extensively been studied in neural network models. Most of the previous sequential associative memory embedded sequences of memory patterns have nearly equal sizes. It was recently shown that local cortical networks display many diverse yet repeatable precise temporal sequences of neuronal activities, termed "neuronal avalanches." Interestingly, these avalanches displayed size and lifetime distributions that obey power laws. Inspired by these experimental findings, here we consider an associative memory model of binary neurons that stores sequences of memory patterns with highly variable sizes. Our analysis includes the case where the statistics of these size variations obey the above-mentioned power laws. We study the retrieval dynamics of such memory systems by analytically deriving the equations that govern the time evolution of macroscopic order parameters. We calculate the critical sequence length beyond which the network cannot retrieve memory sequences correctly. As an application of the analysis, we show how the present variability in sequential memory patterns degrades the power-law lifetime distribution of retrieved neural activities.

Neural network based feed-forward high density associative memory

NASA Technical Reports Server (NTRS)

Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

1987-01-01

A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

Spatial Navigation Impairments Among Intellectually High-Functioning Adults With Autism Spectrum Disorder: Exploring Relations With Theory of Mind, Episodic Memory, and Episodic Future Thinking

PubMed Central

2013-01-01

Research suggests that spatial navigation relies on the same neural network as episodic memory, episodic future thinking, and theory of mind (ToM). Such findings have stimulated theories (e.g., the scene construction and self-projection hypotheses) concerning possible common underlying cognitive capacities. Consistent with such theories, autism spectrum disorder (ASD) is characterized by concurrent impairments in episodic memory, episodic future thinking, and ToM. However, it is currently unclear whether spatial navigation is also impaired. Hence, ASD provides a test case for the scene construction and self-projection theories. The study of spatial navigation in ASD also provides a test of the extreme male brain theory of ASD, which predicts intact or superior navigation (purportedly a systemizing skill) performance among individuals with ASD. Thus, the aim of the current study was to establish whether spatial navigation in ASD is impaired, intact, or superior. Twenty-seven intellectually high-functioning adults with ASD and 28 sex-, age-, and IQ-matched neurotypical comparison adults completed the memory island virtual navigation task. Tests of episodic memory, episodic future thinking, and ToM were also completed. Participants with ASD showed significantly diminished performance on the memory island task, and performance was positively related to ToM and episodic memory, but not episodic future thinking. These results suggest that (contra the extreme male brain theory) individuals with ASD have impaired survey-based navigation skills—that is, difficulties generating cognitive maps of the environment—and adds weight to the idea that scene construction/self-projection are impaired in ASD. The theoretical and clinical implications of these results are discussed. PMID:24364620

Rapid shape memory TEMPO-oxidized cellulose nanofibers/polyacrylamide/gelatin hydrogels with enhanced mechanical strength.

PubMed

Li, Nan; Chen, Wei; Chen, Guangxue; Tian, Junfei

2017-09-01

TEMPO-oxidized cellulose nanofibers/polyacrylamide/gelatin shape memory hydrogels were successfully fabricated through a facile in-situ free-radical polymerization method, and double network was formed by chemically cross-linked polyacrylamide (PAM) network and physically cross-linked gelatin network. TEMPO-oxidized cellulose nanofibers (TOCNs) were introduced to improve the mechanical properties of the hydrogel. The structure, shape memory behaviors and mechanical properties of the resulting composite gels with varied gel compositions were investigated. The results obtained from those different studies revealed that TOCNs, gelatin, and PAM could mix with each other homogeneously. Due to the thermoreversible nature of the gelatin network, the composite hydrogels exhibited attractive thermo-induced shape memory properties. In addition, good mechanical properties (strength >200kPa, strain >650%) were achieved. Such composite hydrogels with good shape memory behavior and enhanced mechanical strength would be an attractive candidate for a wide variety of applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Echoes on the motor network: how internal motor control structures afford sensory experience.

PubMed

Burgess, Jed D; Lum, Jarrad A G; Hohwy, Jakob; Enticott, Peter G

2017-12-01

Often, during daily experiences, hearing peers' actions can activate motor regions of the CNS. This activation is termed auditory-motor resonance (AMR) and is thought to represent an internal simulation of one's motor memories. Currently, AMR is demonstrated at the neuronal level in the Macaque and songbird, in conjunction with evidence on a systems level in humans. Here, we review evidence of AMR development from a motor control perspective. In the context of internal modelling, we consider data that demonstrates sensory-guided motor learning and action maintenance, particularly the notion of sensory comparison seen during songbird vocalisation. We suggest that these comparisons generate accurate sensory-to-motor inverse mappings. Furthermore, given reports of mapping decay after songbird learning, we highlight the proposal that the maintenance of these sensorimotor maps potentially explains why frontoparietal regions are activated upon hearing known sounds (i.e., AMR). In addition, we also recommend that activation of these types of internal models outside of action execution may provide an ecological advantage when encountering known stimuli in ambiguous conditions.

A Neural Network Model of Retrieval-Induced Forgetting

ERIC Educational Resources Information Center

Norman, Kenneth A.; Newman, Ehren L.; Detre, Greg

2007-01-01

Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular…

Recoding a cocaine-place memory engram to a neutral engram in the hippocampus.

PubMed

Trouche, Stéphanie; Perestenko, Pavel V; van de Ven, Gido M; Bratley, Claire T; McNamara, Colin G; Campo-Urriza, Natalia; Black, S Lucas; Reijmers, Leon G; Dupret, David

2016-04-01

The hippocampus provides the brain's memory system with a subset of neurons holding a map-like representation of each environment experienced. We found in mice that optogenetic silencing those neurons active in an environment unmasked a subset of quiet neurons, enabling the emergence of an alternative map. When applied in a cocaine-paired environment, this intervention neutralized an otherwise long-lasting drug-place preference, showing that recoding a spatial memory engram can alleviate associated maladaptive behavior.

Towards quantum networks of single spins: analysis of a quantum memory with an optical interface in diamond.

PubMed

Blok, M S; Kalb, N; Reiserer, A; Taminiau, T H; Hanson, R

2015-01-01

Single defect centers in diamond have emerged as a powerful platform for quantum optics experiments and quantum information processing tasks. Connecting spatially separated nodes via optical photons into a quantum network will enable distributed quantum computing and long-range quantum communication. Initial experiments on trapped atoms and ions as well as defects in diamond have demonstrated entanglement between two nodes over several meters. To realize multi-node networks, additional quantum bit systems that store quantum states while new entanglement links are established are highly desirable. Such memories allow for entanglement distillation, purification and quantum repeater protocols that extend the size, speed and distance of the network. However, to be effective, the memory must be robust against the entanglement generation protocol, which typically must be repeated many times. Here we evaluate the prospects of using carbon nuclear spins in diamond as quantum memories that are compatible with quantum networks based on single nitrogen vacancy (NV) defects in diamond. We present a theoretical framework to describe the dephasing of the nuclear spins under repeated generation of NV spin-photon entanglement and show that quantum states can be stored during hundreds of repetitions using typical experimental coupling parameters. This result demonstrates that nuclear spins with weak hyperfine couplings are promising quantum memories for quantum networks.

Analysis of correlation between white matter changes and functional responses in thalamic stroke: a DTI & EEG study.

PubMed

Duru, Adil Deniz; Duru, Dilek Göksel; Yumerhodzha, Sami; Bebek, Nerses

2016-06-01

Diffusion tensor imaging (DTI) allows in vivo structural brain mapping and detection of microstructural disruption of white matter (WM). One of the commonly used parameters for grading the anisotropic diffusivity in WM is fractional anisotropy (FA). FA value helps to quantify the directionality of the local tract bundle. Therefore, FA images are being used in voxelwise statistical analyses (VSA). The present study used Tract-Based Spatial Statistics (TBSS) of FA images across subjects, and computes the mean skeleton map to detect voxelwise knowledge of the tracts yielding to groupwise comparison. The skeleton image illustrates WM structure and shows any changes caused by brain damage. The microstructure of WM in thalamic stroke is investigated, and the VSA results of healthy control and thalamic stroke patients are reported. It has been shown that several skeleton regions were affected subject to the presence of thalamic stroke (FWE, p < 0.05). Furthermore the correlation of quantitative EEG (qEEG) scores and neurophysiological tests with the FA skeleton for the entire test group is also investigated. We compared measurements that are related to the same fibers across subjects, and discussed implications for VSA of WM in thalamic stroke cases, for the relationship between behavioral tests and FA skeletons, and for the correlation between the FA maps and qEEG scores.Results obtained through the regression analyses did not exceed the corrected statistical threshold values for multiple comparisons (uncorrected, p < 0.05). However, in the regression analysis of FA values and the theta band activity of EEG, cingulum bundle and corpus callosum were found to be related. These areas are parts of the Default Mode Network (DMN) where DMN is known to be involved in resting state EEG theta activity. The relation between the EEG alpha band power values and FA values of the skeleton was found to support the cortico-thalamocortical cycles for both subject groups. Further, the neurophysiological tests including Benton Face Recognition (BFR), Digit Span test (DST), Warrington Topographic Memory test (WTMT), California Verbal Learning test (CVLT) has been regressed with the FA skeleton maps for both subject groups. Our results corresponding to DST task were found to be similar with previously reported findings for working memory and episodic memory tasks. For the WTMT, FA values of the cingulum (right) that plays a role in memory process was found to be related with the behavioral responses. Splenium of corpus callosum was found to be correlated for both subject groups for the BFR.

Multi-voxel pattern classification differentiates personally experienced event memories from secondhand event knowledge.

PubMed

Chow, Tiffany E; Westphal, Andrew J; Rissman, Jesse

2018-04-11

Studies of autobiographical memory retrieval often use photographs to probe participants' memories for past events. Recent neuroimaging work has shown that viewing photographs depicting events from one's own life evokes a characteristic pattern of brain activity across a network of frontal, parietal, and medial temporal lobe regions that can be readily distinguished from brain activity associated with viewing photographs from someone else's life (Rissman, Chow, Reggente, and Wagner, 2016). However, it is unclear whether the neural signatures associated with remembering a personally experienced event are distinct from those associated with recognizing previously encountered photographs of an event. The present experiment used a novel functional magnetic resonance imaging (fMRI) paradigm to investigate putative differences in brain activity patterns associated with these distinct expressions of memory retrieval. Eighteen participants wore necklace-mounted digital cameras to capture events from their everyday lives over the course of three weeks. One week later, participants underwent fMRI scanning, where on each trial they viewed a sequence of photographs depicting either an event from their own life or from another participant's life and judged their memory for this event. Importantly, half of the trials featured photographic sequences that had been shown to participants during a laboratory session administered the previous day. Multi-voxel pattern analyses assessed the sensitivity of two brain networks of interest-as identified by a meta-analysis of prior autobiographical and laboratory-based memory retrieval studies-to the original source of the photographs (own life or other's life) and their experiential history as stimuli (previewed or non-previewed). The classification analyses revealed a striking dissociation: activity patterns within the autobiographical memory network were significantly more diagnostic than those within the laboratory-based network as to whether photographs depicted one's own personal experience (regardless of whether they had been previously seen), whereas activity patterns within the laboratory-based memory network were significantly more diagnostic than those within the autobiographical memory network as to whether photographs had been previewed (regardless of whether they were from the participant's own life). These results, also apparent in whole-brain searchlight classifications, provide evidence for dissociable patterns of activation across two putative memory networks as a function of whether real-world photographs trigger the retrieval of firsthand experiences or secondhand event knowledge. Copyright © 2018 Elsevier Inc. All rights reserved.

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect

PubMed Central

Knoblauch, Andreas; Körner, Edgar; Körner, Ursula; Sommer, Friedrich T.

2014-01-01

Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are “potential synapses” defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the “effectual network connectivity”, that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect. PMID:24858841

A Time-Aware Routing Map for Indoor Evacuation †

PubMed Central

Zhao, Haifeng; Winter, Stephan

2016-01-01

Knowledge of dynamic environments expires over time. Thus, using static maps of the environment for decision making is problematic, especially in emergency situations, such as evacuations. This paper suggests a fading memory model for mapping dynamic environments: a mechanism to put less trust on older knowledge in decision making. The model has been assessed by simulating indoor evacuations, adopting and comparing various strategies in decision making. Results suggest that fading memory generally improves this decision making. PMID:26797610

Distinct hippocampal versus frontoparietal-network contributions to retrieval and memory-guided exploration

PubMed Central

Bridge, Donna J.; Cohen, Neal J.; Voss, Joel L.

2017-01-01

Memory can profoundly influence new learning, presumably because memory optimizes exploration of to-be-learned material. Although hippocampus and frontoparietal networks have been implicated in memory-guided exploration, their specific and interactive roles have not been identified. We examined eye movements during fMRI scanning to identify neural correlates of the influences of memory retrieval on exploration and learning. Following retrieval of one object in a multi-object array, viewing was strategically directed away from the retrieved object toward non-retrieved objects, such that exploration was directed towards to-be-learned content. Retrieved objects later served as optimal reminder cues, indicating that exploration caused memory to become structured around the retrieved content. Hippocampal activity was associated with memory retrieval whereas frontoparietal activity varied with strategic viewing patterns deployed following retrieval, thus providing spatiotemporal dissociation of memory retrieval from memory-guided learning strategies. Time-lagged fMRI connectivity analyses indicated that hippocampal activity predicted frontoparietal activity to a greater extent for a condition in which retrieval guided exploration than for a passive control condition in which exploration was not influenced by retrieval. This demonstrates network-level interaction effects specific to influences of memory on strategic exploration. These findings show how memory guides behavior during learning and demonstrate distinct yet interactive hippocampal-frontoparietal roles in implementing strategic exploration behaviors that determine the fate of evolving memory representations. PMID:28471729

Distinct Hippocampal versus Frontoparietal Network Contributions to Retrieval and Memory-guided Exploration.

PubMed

Bridge, Donna J; Cohen, Neal J; Voss, Joel L

2017-08-01

Memory can profoundly influence new learning, presumably because memory optimizes exploration of to-be-learned material. Although hippocampus and frontoparietal networks have been implicated in memory-guided exploration, their specific and interactive roles have not been identified. We examined eye movements during fMRI scanning to identify neural correlates of the influences of memory retrieval on exploration and learning. After retrieval of one object in a multiobject array, viewing was strategically directed away from the retrieved object toward nonretrieved objects, such that exploration was directed toward to-be-learned content. Retrieved objects later served as optimal reminder cues, indicating that exploration caused memory to become structured around the retrieved content. Hippocampal activity was associated with memory retrieval, whereas frontoparietal activity varied with strategic viewing patterns deployed after retrieval, thus providing spatiotemporal dissociation of memory retrieval from memory-guided learning strategies. Time-lagged fMRI connectivity analyses indicated that hippocampal activity predicted frontoparietal activity to a greater extent for a condition in which retrieval guided exploration occurred than for a passive control condition in which exploration was not influenced by retrieval. This demonstrates network-level interaction effects specific to influences of memory on strategic exploration. These findings show how memory guides behavior during learning and demonstrate distinct yet interactive hippocampal-frontoparietal roles in implementing strategic exploration behaviors that determine the fate of evolving memory representations.

A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

NASA Astrophysics Data System (ADS)

Pawlicki, Ted

1988-03-01

Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions of hierarchical indexing. (i.e. the specificity, adjunct, and parent indices) It supports the notion that multiple canonical views of an object may have to be stored in memory to enable its efficient identification. The use of variable fields in the state space vectors appears to keep the number of required nodes in the network down to a tractable number while imposing a semantic value on different areas of the state space. This semantic imposition supports an interface between the analogical aspects of neural networks and the propositional paradigms of symbolic processing.

Direct access inter-process shared memory

DOEpatents

Brightwell, Ronald B; Pedretti, Kevin; Hudson, Trammell B

2013-10-22

A technique for directly sharing physical memory between processes executing on processor cores is described. The technique includes loading a plurality of processes into the physical memory for execution on a corresponding plurality of processor cores sharing the physical memory. An address space is mapped to each of the processes by populating a first entry in a top level virtual address table for each of the processes. The address space of each of the processes is cross-mapped into each of the processes by populating one or more subsequent entries of the top level virtual address table with the first entry in the top level virtual address table from other processes.

GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.

PubMed

Kim, Jeremie S; Senol Cali, Damla; Xin, Hongyi; Lee, Donghyuk; Ghose, Saugata; Alser, Mohammed; Hassan, Hasan; Ergin, Oguz; Alkan, Can; Mutlu, Onur

2018-05-09

Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments. We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x-6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x-3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm. GRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be applied to any read mapper. We hope that our results provide inspiration for new works to design other bioinformatics algorithms that take advantage of emerging technologies and new processing paradigms, such as processing-in-memory using 3D-stacked memory devices.

Mild traumatic brain injury: graph-model characterization of brain networks for episodic memory.

PubMed

Tsirka, Vasso; Simos, Panagiotis G; Vakis, Antonios; Kanatsouli, Kassiani; Vourkas, Michael; Erimaki, Sofia; Pachou, Ellie; Stam, Cornelis Jan; Micheloyannis, Sifis

2011-02-01

Episodic memory is among the cognitive functions that can be affected in the acute phase following mild traumatic brain injury (MTBI). The present study used EEG recordings to evaluate global synchronization and network organization of rhythmic activity during the encoding and recognition phases of an episodic memory task varying in stimulus type (kaleidoscope images, pictures, words, and pseudowords). Synchronization of oscillatory activity was assessed using a linear and nonlinear connectivity estimator and network analyses were performed using algorithms derived from graph theory. Twenty five MTBI patients (tested within days post-injury) and healthy volunteers were closely matched on demographic variables, verbal ability, psychological status variables, as well as on overall task performance. Patients demonstrated sub-optimal network organization, as reflected by changes in graph parameters in the theta and alpha bands during both encoding and recognition. There were no group differences in spectral energy during task performance or on network parameters during a control condition (rest). Evidence of less optimally organized functional networks during memory tasks was more prominent for pictorial than for verbal stimuli. Copyright © 2010 Elsevier B.V. All rights reserved.

A neural network model of memory and higher cognitive functions.

PubMed

Vogel, David D

2005-01-01

I first describe a neural network model of associative memory in a small region of the brain. The model depends, unconventionally, on disinhibition of inhibitory links between excitatory neurons rather than long-term potentiation (LTP) of excitatory projections. The model may be shown to have advantages over traditional neural network models both in terms of information storage capacity and biological plausibility. The learning and recall algorithms are independent of network architecture, and require no thresholds or finely graded synaptic strengths. Several copies of this local network are then connected by means of many, weak, reciprocal, excitatory projections that allow one region to control the recall of information in another to produce behaviors analogous to serial memory, classical and operant conditioning, secondary reinforcement, refabrication of memory, and fabrication of possible future events. The network distinguishes between perceived and recalled events, and can predicate its response on the absence as well as the presence of particular stimuli. Some of these behaviors are achieved in ways that seem to provide instances of self-awareness and imagination, suggesting that consciousness may emerge as an epiphenomenon in simple brains.

Organism-Level Analysis of Vaccination Reveals Networks of Protection across Tissues.

PubMed

Kadoki, Motohiko; Patil, Ashwini; Thaiss, Cornelius C; Brooks, Donald J; Pandey, Surya; Deep, Deeksha; Alvarez, David; von Andrian, Ulrich H; Wagers, Amy J; Nakai, Kenta; Mikkelsen, Tarjei S; Soumillon, Magali; Chevrier, Nicolas

2017-10-05

A fundamental challenge in immunology is to decipher the principles governing immune responses at the whole-organism scale. Here, using a comparative infection model, we observe immune signal propagation within and between organs to obtain a dynamic map of immune processes at the organism level. We uncover two inter-organ mechanisms of protective immunity mediated by soluble and cellular factors. First, analyzing ligand-receptor connectivity across tissues reveals that type I IFNs trigger a whole-body antiviral state, protecting the host within hours after skin vaccination. Second, combining parabiosis, single-cell analyses, and gene knockouts, we uncover a multi-organ web of tissue-resident memory T cells that functionally adapt to their environment to stop viral spread across the organism. These results have implications for manipulating tissue-resident memory T cells through vaccination and open up new lines of inquiry for the analysis of immune responses at the organism level. Copyright © 2017 Elsevier Inc. All rights reserved.

Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

PubMed Central

Ramsey, Lenny E.; Metcalf, Nicholas V.; Chacko, Ravi V.; Weinberger, Kilian; Baldassarre, Antonello; Hacker, Carl D.; Shulman, Gordon L.; Corbetta, Maurizio

2016-01-01

Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain–behavior relationships in stroke. PMID:27402738

Empirical modeling of an alcohol expectancy memory network using multidimensional scaling.

PubMed

Rather, B C; Goldman, M S; Roehrich, L; Brannick, M

1992-02-01

Risk-related antecedent variables can be linked to later alcohol consumption by memory processes, and alcohol expectancies may be one relevant memory content. To advance research in this area, it would be useful to apply current memory models such as semantic network theory to explain drinking decision processes. We used multidimensional scaling (MDS) to empirically model a preliminary alcohol expectancy semantic network, from which a theoretical account of drinking decision making was generated. Subanalyses (PREFMAP) showed how individuals with differing alcohol consumption histories may have had different association pathways within the expectancy network. These pathways may have, in turn influenced future drinking levels and behaviors while the person was under the influence of alcohol. All individuals associated positive/prosocial effects with drinking, but heavier drinkers indicated arousing effects as their highest probability associates, whereas light drinkers expected sedation. An important early step in this MDS modeling process is the determination of iso-meaning expectancy adjective groups, which correspond to theoretical network nodes.

Memory formation orchestrates the wiring of adult-born hippocampal neurons into brain circuits.

PubMed

Petsophonsakul, Petnoi; Richetin, Kevin; Andraini, Trinovita; Roybon, Laurent; Rampon, Claire

2017-08-01

During memory formation, structural rearrangements of dendritic spines provide a mean to durably modulate synaptic connectivity within neuronal networks. New neurons generated throughout the adult life in the dentate gyrus of the hippocampus contribute to learning and memory. As these neurons become incorporated into the network, they generate huge numbers of new connections that modify hippocampal circuitry and functioning. However, it is yet unclear as to how the dynamic process of memory formation influences their synaptic integration into neuronal circuits. New memories are established according to a multistep process during which new information is first acquired and then consolidated to form a stable memory trace. Upon recall, memory is transiently destabilized and vulnerable to modification. Using contextual fear conditioning, we found that learning was associated with an acceleration of dendritic spines formation of adult-born neurons, and that spine connectivity becomes strengthened after memory consolidation. Moreover, we observed that afferent connectivity onto adult-born neurons is enhanced after memory retrieval, while extinction training induces a change of spine shapes. Together, these findings reveal that the neuronal activity supporting memory processes strongly influences the structural dendritic integration of adult-born neurons into pre-existing neuronal circuits. Such change of afferent connectivity is likely to impact the overall wiring of hippocampal network, and consequently, to regulate hippocampal function.

Attentional networks and visuospatial working memory capacity in social anxiety.

PubMed

Moriya, Jun

2018-02-01

Social anxiety is associated with attentional bias and working memory for emotional stimuli; however, the ways in which social anxiety affects cognitive functions involving non-emotional stimuli remains unclear. The present study focused on the role of attentional networks (i.e. alerting, orienting, and executive control networks) and visuospatial working memory capacity (WMC) for non-emotional stimuli in the context of social anxiety. One hundred and seventeen undergraduates completed questionnaires on social anxiety. They then performed an attentional network test and a change detection task to measure visuospatial WMC. Orienting network and visuospatial WMC were positively correlated with social anxiety. A multiple regression analysis showed significant positive associations of alerting, orienting, and visuospatial WMC with social anxiety. Alerting, orienting networks, and high visuospatial WMC for non-emotional stimuli may predict degree of social anxiety.

Nanophotonic rare-earth quantum memory with optically controlled retrieval.

PubMed

Zhong, Tian; Kindem, Jonathan M; Bartholomew, John G; Rochman, Jake; Craiciu, Ioana; Miyazono, Evan; Bettinelli, Marco; Cavalli, Enrico; Verma, Varun; Nam, Sae Woo; Marsili, Francesco; Shaw, Matthew D; Beyer, Andrew D; Faraon, Andrei

2017-09-29

Optical quantum memories are essential elements in quantum networks for long-distance distribution of quantum entanglement. Scalable development of quantum network nodes requires on-chip qubit storage functionality with control of the readout time. We demonstrate a high-fidelity nanophotonic quantum memory based on a mesoscopic neodymium ensemble coupled to a photonic crystal cavity. The nanocavity enables >95% spin polarization for efficient initialization of the atomic frequency comb memory and time bin-selective readout through an enhanced optical Stark shift of the comb frequencies. Our solid-state memory is integrable with other chip-scale photon source and detector devices for multiplexed quantum and classical information processing at the network nodes. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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

Lee, Y.C.; Doolen, G.; Chen, H.H.

A high-order correlation tensor formalism for neural networks is described. The model can simulate auto associative, heteroassociative, as well as multiassociative memory. For the autoassociative model, simulation results show a drastic increase in the memory capacity and speed over that of the standard Hopfield-like correlation matrix methods. The possibility of using multiassociative memory for a learning universal inference network is also discussed. 9 refs., 5 figs.

Slave to the Rhythm: Experimental Tests of a Model for Verbal Short-Term Memory and Long-Term Sequence Learning

ERIC Educational Resources Information Center

Hitch, Graham J.; Flude, Brenda; Burgess, Neil

2009-01-01

Three experiments tested predictions of a neural network model of phonological short-term memory that assumes separate representations for order and item information, order being coded via a context-timing signal [Burgess, N., & Hitch, G. J. (1999). Memory for serial order: A network model of the phonological loop and its timing. "Psychological…

Avalanches and generalized memory associativity in a network model for conscious and unconscious mental functioning

NASA Astrophysics Data System (ADS)

Siddiqui, Maheen; Wedemann, Roseli S.; Jensen, Henrik Jeldtoft

2018-01-01

We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure.

Optical waveguides with memory effect using photochromic material for neural network

NASA Astrophysics Data System (ADS)

Tanimoto, Keisuke; Amemiya, Yoshiteru; Yokoyama, Shin

2018-04-01

An optical neural network using a waveguide with a memory effect, a photodiode, CMOS circuits and LEDs was proposed. To realize the neural network, optical waveguides with a memory effect were fabricated using a cladding layer containing the photochromic material “diarylethene”. The transmittance of green light was decreased by UV light irradiation and recovered by the passage of green light through the waveguide. It was confirmed that the transmittance versus total energy of the green light that passed through the waveguide well fit the universal exponential curve.

Theta synchronization networks emerge during human object-place memory encoding.

PubMed

Sato, Naoyuki; Yamaguchi, Yoko

2007-03-26

Recent rodent hippocampus studies have suggested that theta rhythm-dependent neural dynamics ('theta phase precession') is essential for an on-line memory formation. A computational study indicated that the phase precession enables a human object-place association memory with voluntary eye movements, although it is still an open question whether the human brain uses the dynamics. Here we elucidated subsequent memory-correlated activities in human scalp electroencephalography in an object-place association memory designed according the former computational study. Our results successfully demonstrated that subsequent memory recall is characterized by an increase in theta power and coherence, and further, that multiple theta synchronization networks emerge. These findings suggest the human theta dynamics in common with rodents in episodic memory formation.

A Reading Attempt of the Urban Memory of Eskisehir Osmangazi University Meselik Campus via Cognitive Mapping

NASA Astrophysics Data System (ADS)

Alptekin, Orkun

2017-10-01

University campuses have a small city view containing basic city functions such as work, accommodation, rest and transportation. They are spaces of social life that occupy large areas, have population density and different activities, change and grow with the cities they live in, and memorize the past accumulations. In this context, it is necessary for campuses to form and protect their own memories like cities. Campus memory is the ability of individuals to keep, maintain and - when necessary- reveal the experiences, sensations, comprehensions gathered from physical environment. "Cognitive mapping" is used to reveal the physical and emotional relationship that individuals make with the city and the individual-city interaction. Cognitive maps are created graphically using verbal and geometric items on paper by remembering these coded urban images. In this study, to determine the urban images belonging to Eskisehir Osmangazi University Meselik Campus, architecture students who have a short period experience of the campus were asked to note the areas they interact with the campus on the cognitive map. Campus memory items are identified by analysing the cognitive maps of the individuals who experienced the campus. In the direction of the obtained data, the campus area was re-read with five basic elements of Lynch: paths, districts, edges, nodes, and landmarks. As a result of these analyses, it is seen that religious structure, which is a large symbolic structure, located next to the main entrance in the settlement and health care facilities defined as landmarks are located in the memory of most of the individuals. Then, paths, nodes, districts, edges and educational buildings are listed respectively in cognitive maps.

Meeting the memory challenges of brain-scale network simulation.

PubMed

Kunkel, Susanne; Potjans, Tobias C; Eppler, Jochen M; Plesser, Hans Ekkehard; Morrison, Abigail; Diesmann, Markus

2011-01-01

The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity, and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10(5) neurons with up to 10(9) synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been investigated in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Blue Gene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of neuronal simulators as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place. As a consequence, development cycles can be shorter and less expensive. Applying the model to our freely available Neural Simulation Tool (NEST), we identify the software components dominant at different scales, and develop general strategies for reducing the memory consumption, in particular by using data structures that exploit the sparseness of the local representation of the network. We show that these adaptations enable our simulation software to scale up to the order of 10,000 processors and beyond. As memory consumption issues are likely to be relevant for any software dealing with complex connectome data on such architectures, our approach and our findings should be useful for researchers developing novel neuroinformatics solutions to the challenges posed by the connectome project.

Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

PubMed

Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

2005-12-01

In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

Not only … but also: REM sleep creates and NREM Stage 2 instantiates landmark junctions in cortical memory networks.

PubMed

Llewellyn, Sue; Hobson, J Allan

2015-07-01

This article argues both rapid eye movement (REM) and non-rapid eye movement (NREM) sleep contribute to overnight episodic memory processes but their roles differ. Episodic memory may have evolved from memory for spatial navigation in animals and humans. Equally, mnemonic navigation in world and mental space may rely on fundamentally equivalent processes. Consequently, the basic spatial network characteristics of pathways which meet at omnidirectional nodes or junctions may be conserved in episodic brain networks. A pathway is formally identified with the unidirectional, sequential phases of an episodic memory. In contrast, the function of omnidirectional junctions is not well understood. In evolutionary terms, both animals and early humans undertook tours to a series of landmark junctions, to take advantage of resources (food, water and shelter), whilst trying to avoid predators. Such tours required memory for emotionally significant landmark resource-place-danger associations and the spatial relationships amongst these landmarks. In consequence, these tours may have driven the evolution of both spatial and episodic memory. The environment is dynamic. Resource-place associations are liable to shift and new resource-rich landmarks may be discovered, these changes may require re-wiring in neural networks. To realise these changes, REM may perform an associative, emotional encoding function between memory networks, engendering an omnidirectional landmark junction which is instantiated in the cortex during NREM Stage 2. In sum, REM may preplay associated elements of past episodes (rather than replay individual episodes), to engender an unconscious representation which can be used by the animal on approach to a landmark junction in wake. Copyright © 2015 Elsevier Inc. All rights reserved.

Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks.

PubMed

Tutubalina, Elena; Miftahutdinov, Zulfat; Nikolenko, Sergey; Malykh, Valentin

2018-06-12

Text mining of scientific libraries and social media has already proven itself as a reliable tool for drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language System (UMLS), is known as medical concept normalization. This task is challenging due to the differences in the use of medical terminology between health care professionals and social media texts coming from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks and semantic representation of one- or multi-word expressions: we develop end-to-end architectures directly tailored to the task, including bidirectional Long Short-Term Memory, Gated Recurrent Units with an attention mechanism, and additional semantic similarity features based on UMLS. Our evaluation against a standard benchmark shows that recurrent neural networks improve results over an effective baseline for classification based on convolutional neural networks. A qualitative examination of mentions discovered in a dataset of user reviews collected from popular online health information platforms as well as a quantitative evaluation both show improvements in the semantic representation of health-related expressions in social media. Copyright © 2018. Published by Elsevier Inc.

Internet protocol network mapper

DOEpatents

Youd, David W.; Colon III, Domingo R.; Seidl, Edward T.

2016-02-23

A network mapper for performing tasks on targets is provided. The mapper generates a map of a network that specifies the overall configuration of the network. The mapper inputs a procedure that defines how the network is to be mapped. The procedure specifies what, when, and in what order the tasks are to be performed. Each task specifies processing that is to be performed for a target to produce results. The procedure may also specify input parameters for a task. The mapper inputs initial targets that specify a range of network addresses to be mapped. The mapper maps the network by, for each target, executing the procedure to perform the tasks on the target. The results of the tasks represent the mapping of the network defined by the initial targets.

Through the Immune Looking Glass: A Model for Brain Memory Strategies

PubMed Central

Sánchez-Ramón, Silvia; Faure, Florence

2016-01-01

The immune system (IS) and the central nervous system (CNS) are complex cognitive networks involved in defining the identity (self) of the individual through recognition and memory processes that enable one to anticipate responses to stimuli. Brain memory has traditionally been classified as either implicit or explicit on psychological and anatomical grounds, with reminiscences of the evolutionarily-based innate-adaptive IS responses. Beyond the multineuronal networks of the CNS, we propose a theoretical model of brain memory integrating the CNS as a whole. This is achieved by analogical reasoning between the operational rules of recognition and memory processes in both systems, coupled to an evolutionary analysis. In this new model, the hippocampus is no longer specifically ascribed to explicit memory but rather it both becomes part of the innate (implicit) memory system and tightly controls the explicit memory system. Alike the antigen presenting cells for the IS, the hippocampus would integrate transient and pseudo-specific (i.e., danger-fear) memories and would drive the formation of long-term and highly specific or explicit memories (i.e., the taste of the Proust’s madeleine cake) by the more complex and recent, evolutionarily speaking, neocortex. Experimental and clinical evidence is provided to support the model. We believe that the singularity of this model’s approximation could help to gain a better understanding of the mechanisms operating in brain memory strategies from a large-scale network perspective. PMID:26869886

Improvement of the Hopfield Neural Network by MC-Adaptation Rule

NASA Astrophysics Data System (ADS)

Zhou, Zhen; Zhao, Hong

2006-06-01

We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.

Designs of Concept Maps and Their Impacts on Readers' Performance in Memory and Reasoning while Reading

ERIC Educational Resources Information Center

Tzeng, Jeng-Yi

2010-01-01

From the perspective of the Fuzzy Trace Theory, this study investigated the impacts of concept maps with two strategic orientations (comprehensive and thematic representations) on readers' performance of cognitive operations (such as perception, verbatim memory, gist reasoning and syntheses) while the readers were reading two history articles that…

Effect of Memory Support and Elicited Production on Fast Mapping of New Words by Adolescents with Down Syndrome

ERIC Educational Resources Information Center

Chapman, Robin S.; Sindberg, Heidi; Bridge, Cynthia; Gigstead, Katherine; Hesketh, Linda

2006-01-01

Purpose: The purpose of this study was to determine whether memory support and elicited production differentially benefited fast mapping of new vocabulary (comprehension, production accuracy, and speed) in adolescents with Down syndrome (DS) compared with typically developing (TD) children matched for syntax comprehension. The study also examined…

An elementary quantum network using robust nuclear spin qubits in diamond

NASA Astrophysics Data System (ADS)

Kalb, Norbert; Reiserer, Andreas; Humphreys, Peter; Blok, Machiel; van Bemmelen, Koen; Twitchen, Daniel; Markham, Matthew; Taminiau, Tim; Hanson, Ronald

Quantum registers containing multiple robust qubits can form the nodes of future quantum networks for computation and communication. Information storage within such nodes must be resilient to any type of local operation. Here we demonstrate multiple robust memories by employing five nuclear spins adjacent to a nitrogen-vacancy defect centre in diamond. We characterize the storage of quantum superpositions and their resilience to entangling attempts with the electron spin of the defect centre. The storage fidelity is found to be limited by the probabilistic electron spin reset after failed entangling attempts. Control over multiple memories is then utilized to encode states in decoherence protected subspaces with increased robustness. Furthermore we demonstrate memory control in two optically linked network nodes and characterize the storage capabilities of both memories in terms of the process fidelity with the identity. These results pave the way towards multi-qubit quantum algorithms in a remote network setting.

Hidden long evolutionary memory in a model biochemical network

NASA Astrophysics Data System (ADS)

Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-04-01

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

Endogenous-cue prospective memory involving incremental updating of working memory: an fMRI study.

PubMed

Halahalli, Harsha N; John, John P; Lukose, Ammu; Jain, Sanjeev; Kutty, Bindu M

2015-11-01

Prospective memory paradigms are conventionally classified on the basis of event-, time-, or activity-based intention retrieval. In the vast majority of such paradigms, intention retrieval is provoked by some kind of external event. However, prospective memory retrieval cues that prompt intention retrieval in everyday life are commonly endogenous, i.e., linked to a specific imagined retrieval context. We describe herein a novel prospective memory paradigm wherein the endogenous cue is generated by incremental updating of working memory, and investigated the hemodynamic correlates of this task. Eighteen healthy adult volunteers underwent functional magnetic resonance imaging while they performed a prospective memory task where the delayed intention was triggered by an endogenous cue generated by incremental updating of working memory. Working memory and ongoing task control conditions were also administered. The 'endogenous-cue prospective memory condition' with incremental working memory updating was associated with maximum activations in the right rostral prefrontal cortex, and additional activations in the brain regions that constitute the bilateral fronto-parietal network, central and dorsal salience networks as well as cerebellum. In the working memory control condition, maximal activations were noted in the left dorsal anterior insula. Activation of the bilateral dorsal anterior insula, a component of the central salience network, was found to be unique to this 'endogenous-cue prospective memory task' in comparison to previously reported exogenous- and endogenous-cue prospective memory tasks without incremental working memory updating. Thus, the findings of the present study highlight the important role played by the dorsal anterior insula in incremental working memory updating that is integral to our endogenous-cue prospective memory task.

Virtual optical network mapping and core allocation in elastic optical networks using multi-core fibers

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-11-01

Virtualization technology can greatly improve the efficiency of the networks by allowing the virtual optical networks to share the resources of the physical networks. However, it will face some challenges, such as finding the efficient strategies for virtual nodes mapping, virtual links mapping and spectrum assignment. It is even more complex and challenging when the physical elastic optical networks using multi-core fibers. To tackle these challenges, we establish a constrained optimization model to determine the optimal schemes of optical network mapping, core allocation and spectrum assignment. To solve the model efficiently, tailor-made encoding scheme, crossover and mutation operators are designed. Based on these, an efficient genetic algorithm is proposed to obtain the optimal schemes of the virtual nodes mapping, virtual links mapping, core allocation. The simulation experiments are conducted on three widely used networks, and the experimental results show the effectiveness of the proposed model and algorithm.

The list-composition effect in memory for emotional and neutral pictures: Differential contribution of ventral and dorsal attention networks to successful encoding.

PubMed

Barnacle, Gemma E; Montaldi, Daniela; Talmi, Deborah; Sommer, Tobias

2016-09-01

The Emotional enhancement of memory (EEM) is observed in immediate free-recall memory tests when emotional and neutral stimuli are encoded and tested together ("mixed lists"), but surprisingly, not when they are encoded and tested separately ("pure lists"). Here our aim was to investigate whether the effect of list-composition (mixed versus pure lists) on the EEM is due to differential allocation of attention. We scanned participants with fMRI during encoding of semantically-related emotional (negative valence only) and neutral pictures. Analysis of memory performance data replicated previous work, demonstrating an interaction between list composition and emotional valence. In mixed lists, neural subsequent memory effects in the dorsal attention network were greater for neutral stimulus encoding, while neural subsequent memory effects for emotional stimuli were found in a region associated with the ventral attention network. These results imply that when life experiences include both emotional and neutral elements, memory for the latter is more highly correlated with neural activity representing goal-directed attention processing at encoding. Copyright © 2016. Published by Elsevier Ltd.

Cognitive Control Network Contributions to Memory-Guided Visual Attention.

PubMed

Rosen, Maya L; Stern, Chantal E; Michalka, Samantha W; Devaney, Kathryn J; Somers, David C

2016-05-01

Visual attentional capacity is severely limited, but humans excel in familiar visual contexts, in part because long-term memories guide efficient deployment of attention. To investigate the neural substrates that support memory-guided visual attention, we performed a set of functional MRI experiments that contrast long-term, memory-guided visuospatial attention with stimulus-guided visuospatial attention in a change detection task. Whereas the dorsal attention network was activated for both forms of attention, the cognitive control network(CCN) was preferentially activated during memory-guided attention. Three posterior nodes in the CCN, posterior precuneus, posterior callosal sulcus/mid-cingulate, and lateral intraparietal sulcus exhibited the greatest specificity for memory-guided attention. These 3 regions exhibit functional connectivity at rest, and we propose that they form a subnetwork within the broader CCN. Based on the task activation patterns, we conclude that the nodes of this subnetwork are preferentially recruited for long-term memory guidance of visuospatial attention. Published by Oxford University Press 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Sequence memory based on coherent spin-interaction neural networks.

PubMed

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

2014-12-01

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

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

PubMed

Anishchenko, Anastasia; Treves, Alessandro

2006-10-01

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

Scale-free networks as an epiphenomenon of memory

NASA Astrophysics Data System (ADS)

Caravelli, F.; Hamma, A.; Di Ventra, M.

2015-01-01

Many realistic networks are scale free, with small characteristic path lengths, high clustering, and power law in their degree distribution. They can be obtained by dynamical networks in which a preferential attachment process takes place. However, this mechanism is non-local, in the sense that it requires knowledge of the whole graph in order for the graph to be updated. Instead, if preferential attachment and realistic networks occur in physical systems, these features need to emerge from a local model. In this paper, we propose a local model and show that a possible ingredient (which is often underrated) for obtaining scale-free networks with local rules is memory. Such a model can be realised in solid-state circuits, using non-linear passive elements with memory such as memristors, and thus can be tested experimentally.

Interaction between attentional systems and episodic memory encoding: the impact of conflict on binding of information.

PubMed

Sperduti, Marco; Armougum, Allan; Makowski, Dominique; Blondé, Philippe; Piolino, Pascale

2017-12-01

Episodic memory (EM) is defined as a long-term memory system that stores information that can be retrieved along with details of the context of the original events (binding). Several studies have shown that manipulation of attention during encoding can impact subsequent memory performance. An influential model of attention distinguishes between three partially independent attentional networks: the alerting, the orienting and the executive or conflict resolution component. To date, the impact of the engagement of these sub-systems during encoding on item and relational context binding has not been investigated. Here, we developed a new task combining the Attentional Network Test and an incidental episodic memory encoding task to study this issue. We reported that when the alerting network was not solicited, resolving conflict hindered item encoding. Moreover, resolving conflict, independently of the cueing condition, had a negative impact on context binding. These novel findings could have a potential impact in the understanding EM formation, and memory disorders in different populations, including healthy elderly people.

Stability analysis for stochastic BAM nonlinear neural network with delays

NASA Astrophysics Data System (ADS)

Lv, Z. W.; Shu, H. S.; Wei, G. L.

2008-02-01

In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria.

Efficient computation of aerodynamic influence coefficients for aeroelastic analysis on a transputer network

NASA Technical Reports Server (NTRS)

Janetzke, David C.; Murthy, Durbha V.

1991-01-01

Aeroelastic analysis is multi-disciplinary and computationally expensive. Hence, it can greatly benefit from parallel processing. As part of an effort to develop an aeroelastic capability on a distributed memory transputer network, a parallel algorithm for the computation of aerodynamic influence coefficients is implemented on a network of 32 transputers. The aerodynamic influence coefficients are calculated using a 3-D unsteady aerodynamic model and a parallel discretization. Efficiencies up to 85 percent were demonstrated using 32 processors. The effect of subtask ordering, problem size, and network topology are presented. A comparison to results on a shared memory computer indicates that higher speedup is achieved on the distributed memory system.

Short-term memory of motor network performance via activity-dependent potentiation of Na+/K+ pump function.

PubMed

Zhang, Hong-Yan; Sillar, Keith T

2012-03-20

Brain networks memorize previous performance to adjust their output in light of past experience. These activity-dependent modifications generally result from changes in synaptic strengths or ionic conductances, and ion pumps have only rarely been demonstrated to play a dynamic role. Locomotor behavior is produced by central pattern generator (CPG) networks and modified by sensory and descending signals to allow for changes in movement frequency, intensity, and duration, but whether or how the CPG networks recall recent activity is largely unknown. In Xenopus frog tadpoles, swim bout duration correlates linearly with interswim interval, suggesting that the locomotor network retains a short-term memory of previous output. We discovered an ultraslow, minute-long afterhyperpolarization (usAHP) in network neurons following locomotor episodes. The usAHP is mediated by an activity- and sodium spike-dependent enhancement of electrogenic Na(+)/K(+) pump function. By integrating spike frequency over time and linking the membrane potential of spinal neurons to network performance, the usAHP plays a dynamic role in short-term motor memory. Because Na(+)/K(+) pumps are ubiquitously expressed in neurons of all animals and because sodium spikes inevitably accompany network activity, the usAHP may represent a phylogenetically conserved but largely overlooked mechanism for short-term memory of neural network function. Copyright © 2012 Elsevier Ltd. All rights reserved.

Memory functions reveal structural properties of gene regulatory networks

PubMed Central

Perez-Carrasco, Ruben

2018-01-01

Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. PMID:29470492

Mapping virtual addresses to different physical addresses for value disambiguation for thread memory access requests

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

Gala, Alan; Ohmacht, Martin

A multiprocessor system includes nodes. Each node includes a data path that includes a core, a TLB, and a first level cache implementing disambiguation. The system also includes at least one second level cache and a main memory. For thread memory access requests, the core uses an address associated with an instruction format of the core. The first level cache uses an address format related to the size of the main memory plus an offset corresponding to hardware thread meta data. The second level cache uses a physical main memory address plus software thread meta data to store the memorymore » access request. The second level cache accesses the main memory using the physical address with neither the offset nor the thread meta data after resolving speculation. In short, this system includes mapping of a virtual address to a different physical addresses for value disambiguation for different threads.« less

Portraying the unique contribution of the default mode network to internally driven mnemonic processes

PubMed Central

Shapira-Lichter, Irit; Oren, Noga; Jacob, Yael; Gruberger, Michal; Hendler, Talma

2013-01-01

Numerous neuroimaging studies have implicated default mode network (DMN) involvement in both internally driven processes and memory. Nevertheless, it is unclear whether memory operations reflect a particular case of internally driven processing or alternatively involve the DMN in a distinct manner, possibly depending on memory type. This question is critical for refining neurocognitive memory theorem in the context of other endogenic processes and elucidating the functional significance of this key network. We used functional MRI to examine DMN activity and connectivity patterns while participants overtly generated words according to nonmnemonic (phonemic) or mnemonic (semantic or episodic) cues. Overall, mnemonic word fluency was found to elicit greater DMN activity and stronger within-network functional connectivity compared with nonmnemonic fluency. Furthermore, two levels of functional organization of memory retrieval were shown. First, across both mnemonic tasks, activity was greater mainly in the posterior cingulate cortex, implying selective contribution to generic aspects of memory beyond its general involvement in endogenous processes. Second, parts of the DMN showed distinct selectivity for each of the mnemonic conditions; greater recruitment of the anterior prefrontal cortex, retroesplenial cortex, and hippocampi and elevated connectivity between anterior and posterior medial DMN nodes characterized the semantic condition, whereas increased recruitment of posterior DMN components and elevated connectivity between them characterized the episodic condition. This finding emphasizes the involvement of DMN elements in discrete aspects of memory retrieval. Altogether, our results show a specific contribution of the DMN to memory processes, corresponding to the specific type of memory retrieval. PMID:23479650

Dopamine modulation of GABAergic function enables network stability and input selectivity for sustaining working memory in a computational model of the prefrontal cortex.

PubMed

Lew, Sergio E; Tseng, Kuei Y

2014-12-01

Dopamine modulation of GABAergic transmission in the prefrontal cortex (PFC) is thought to be critical for sustaining cognitive processes such as working memory and decision-making. Here, we developed a neurocomputational model of the PFC that includes physiological features of the facilitatory action of dopamine on fast-spiking interneurons to assess how a GABAergic dysregulation impacts on the prefrontal network stability and working memory. We found that a particular non-linear relationship between dopamine transmission and GABA function is required to enable input selectivity in the PFC for the formation and retention of working memory. Either degradation of the dopamine signal or the GABAergic function is sufficient to elicit hyperexcitability in pyramidal neurons and working memory impairments. The simulations also revealed an inverted U-shape relationship between working memory and dopamine, a function that is maintained even at high levels of GABA degradation. In fact, the working memory deficits resulting from reduced GABAergic transmission can be rescued by increasing dopamine tone and vice versa. We also examined the role of this dopamine-GABA interaction for the termination of working memory and found that the extent of GABAergic excitation needed to reset the PFC network begins to occur when the activity of fast-spiking interneurons surpasses 40 Hz. Together, these results indicate that the capability of the PFC to sustain working memory and network stability depends on a robust interplay of compensatory mechanisms between dopamine tone and the activity of local GABAergic interneurons.

Increased functional connectivity in the ventral and dorsal streams during retrieval of novel words in professional musicians.

PubMed

Dittinger, Eva; Valizadeh, Seyed Abolfazl; Jäncke, Lutz; Besson, Mireille; Elmer, Stefan

2018-02-01

Current models of speech and language processing postulate the involvement of two parallel processing streams (the dual stream model): a ventral stream involved in mapping sensory and phonological representations onto lexical and conceptual representations and a dorsal stream contributing to sound-to-motor mapping, articulation, and to how verbal information is encoded and manipulated in memory. Based on previous evidence showing that music training has an influence on language processing, cognitive functions, and word learning, we examined EEG-based intracranial functional connectivity in the ventral and dorsal streams while musicians and nonmusicians learned the meaning of novel words through picture-word associations. In accordance with the dual stream model, word learning was generally associated with increased beta functional connectivity in the ventral stream compared to the dorsal stream. In addition, in the linguistically most demanding "semantic task," musicians outperformed nonmusicians, and this behavioral advantage was accompanied by increased left-hemispheric theta connectivity in both streams. Moreover, theta coherence in the left dorsal pathway was positively correlated with the number of years of music training. These results provide evidence for a complex interplay within a network of brain regions involved in semantic processing and verbal memory functions, and suggest that intensive music training can modify its functional architecture leading to advantages in novel word learning. © 2017 Wiley Periodicals, Inc.

Altered segregation between task-positive and task-negative regions in mild traumatic brain injury.

PubMed

Sours, Chandler; Kinnison, Joshua; Padmala, Srikanth; Gullapalli, Rao P; Pessoa, Luiz

2018-06-01

Changes in large-scale brain networks that accompany mild traumatic brain injury (mTBI) were investigated using functional magnetic resonance imaging (fMRI) during the N-back working memory task at two cognitive loads (1-back and 2-back). Thirty mTBI patients were examined during the chronic stage of injury and compared to 28 control participants. Demographics and behavioral performance were matched across groups. Due to the diffuse nature of injury, we hypothesized that there would be an imbalance in the communication between task-positive and Default Mode Network (DMN) regions in the context of effortful task execution. Specifically, a graph-theoretic measure of modularity was used to quantify the extent to which groups of brain regions tended to segregate into task-positive and DMN sub-networks. Relative to controls, mTBI patients showed reduced segregation between the DMN and task-positive networks, but increased functional connectivity within the DMN regions during the more cognitively demanding 2-back task. Together, our findings reveal that patients exhibit alterations in the communication between and within neural networks during a cognitively demanding task. These findings reveal altered processes that persist through the chronic stage of injury, highlighting the need for longitudinal research to map the neural recovery of mTBI patients.

Cytokines and cytokine networks target neurons to modulate long-term potentiation.

PubMed

Prieto, G Aleph; Cotman, Carl W

2017-04-01

Cytokines play crucial roles in the communication between brain cells including neurons and glia, as well as in the brain-periphery interactions. In the brain, cytokines modulate long-term potentiation (LTP), a cellular correlate of memory. Whether cytokines regulate LTP by direct effects on neurons or by indirect mechanisms mediated by non-neuronal cells is poorly understood. Elucidating neuron-specific effects of cytokines has been challenging because most brain cells express cytokine receptors. Moreover, cytokines commonly increase the expression of multiple cytokines in their target cells, thus increasing the complexity of brain cytokine networks even after single-cytokine challenges. Here, we review evidence on both direct and indirect-mediated modulation of LTP by cytokines. We also describe novel approaches based on neuron- and synaptosome-enriched systems to identify cytokines able to directly modulate LTP, by targeting neurons and synapses. These approaches can test multiple samples in parallel, thus allowing the study of multiple cytokines simultaneously. Hence, a cytokine networks perspective coupled with neuron-specific analysis may contribute to delineation of maps of the modulation of LTP by cytokines. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cytokines and cytokine networks target neurons to modulate long-term potentiation

PubMed Central

Prieto, G. Aleph; Cotman, Carl W.

2017-01-01

Cytokines play crucial roles in the communication between brain cells including neurons and glia, as well as in the brain-periphery interactions. In the brain, cytokines modulate long-term potentiation (LTP), a cellular correlate of memory. Whether cytokines regulate LTP by direct effects on neurons or by indirect mechanisms mediated by non-neuronal cells is poorly understood. Elucidating neuron-specific effects of cytokines has been challenging because most brain cells express cytokine receptors. Moreover, cytokines commonly increase the expression of multiple cytokines in their target cells, thus increasing the complexity of brain cytokine networks even after single-cytokine challenges. Here, we review evidence on both direct and indirect-mediated modulation of LTP by cytokines. We also describe novel approaches based on neuron- and synaptosome-enriched systems to identify cytokines able to directly modulate LTP, by targeting neurons and synapses. These approaches can test multiple samples in parallel, thus allowing the study of multiple cytokines simultaneously. Hence, a cytokine networks perspective coupled with neuron-specific analysis may contribute to delineation of maps of the modulation of LTP by cytokines. PMID:28377062

Single-Frame Terrain Mapping Software for Robotic Vehicles

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.

2011-01-01

This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each map. The map is compressed into a vector prior to delivery to another system.

γ-Aminobutyric Acid Type A Receptor Potentiation Inhibits Learning in a Computational Network Model.

PubMed

Storer, Kingsley P; Reeke, George N

2018-04-17

Propofol produces memory impairment at concentrations well below those abolishing consciousness. Episodic memory, mediated by the hippocampus, is most sensitive. Two potentially overlapping scenarios may explain how γ-aminobutyric acid receptor type A (GABAA) potentiation by propofol disrupts episodic memory-the first mediated by shifting the balance from excitation to inhibition while the second involves disruption of rhythmic oscillations. We use a hippocampal network model to explore these scenarios. The basis for these experiments is the proposal that the brain represents memories as groups of anatomically dispersed strongly connected neurons. A neuronal network with connections modified by synaptic plasticity was exposed to patterned stimuli, after which spiking output demonstrated evidence of stimulus-related neuronal group development analogous to memory formation. The effect of GABAA potentiation on this memory model was studied in 100 unique networks. GABAA potentiation consistent with moderate propofol effects reduced neuronal group size formed in response to a patterned stimulus by around 70%. Concurrently, accuracy of a Bayesian classifier in identifying learned patterns in the network output was reduced. Greater potentiation led to near total failure of group formation. Theta rhythm variations had no effect on group size or classifier accuracy. Memory formation is widely thought to depend on changes in neuronal connection strengths during learning that enable neuronal groups to respond with greater facility to familiar stimuli. This experiment suggests the ability to form such groups is sensitive to alteration in the balance between excitation and inhibition such as that resulting from administration of a γ-aminobutyric acid-mediated anesthetic agent.

Frontoparietal cognitive control of verbal memory recall in Alzheimer's disease.

PubMed

Dhanjal, Novraj S; Wise, Richard J S

2014-08-01

Episodic memory retrieval is reliant upon cognitive control systems, of which 2 have been identified with functional neuroimaging: a cingulo-opercular salience network (SN) and a frontoparietal executive network (EN). In Alzheimer's disease (AD), pathology is distributed throughout higher-order cortices. The hypotheses were that this frontoparietal pathology would impair activity associated with verbal memory recall; and that central cholinesterase inhibition (ChI) would modulate this, improving memory recall. Functional magnetic resonance imaging was used to study normal participants and 2 patient groups: mild cognitive impairment (MCI) and AD. Activity within the EN and SN was observed during free recall of previously heard sentences, and related to measures of recall accuracy. In normal subjects, trials with reduced recall were associated with greater activity in both the SN and EN. Better recall was associated with greater activity in medial regions of the default mode network. By comparison, AD patients showed attenuated responses in both the SN and EN compared with either controls or MCI patients, even after recall performance was matched between groups. Following ChI, AD patients showed no modulation of activity within the SN, but increased activity within the EN. There was also enhanced activity within regions associated with episodic and semantic memory during less successful recall, requiring greater cognitive control. The results indicate that in AD, impaired responses of cognitive control networks during verbal memory recall are partly responsible for reduced recall performance. One action of symptom-modifying treatment is partially to reverse the abnormal function of frontoparietal cognitive control and temporal lobe memory networks. © 2014 American Neurological Association.

Memory and pattern storage in neural networks with activity dependent synapses

NASA Astrophysics Data System (ADS)

Mejias, J. F.; Torres, J. J.

2009-01-01

We present recently obtained results on the influence of the interplay between several activity dependent synaptic mechanisms, such as short-term depression and facilitation, on the maximum memory storage capacity in an attractor neural network [1]. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve activity patterns [2], synaptic facilitation is able to enhance the memory capacity in different situations. In particular, we find that a convenient balance between depression and facilitation can enhance the memory capacity, reaching maximal values similar to those obtained with static synapses, that is, without activity-dependent processes. We also argue, employing simple arguments, that this level of balance is compatible with experimental data recorded from some cortical areas, where depression and facilitation may play an important role for both memory-oriented tasks and information processing. We conclude that depressing synapses with a certain level of facilitation allow to recover the good retrieval properties of networks with static synapses while maintaining the nonlinear properties of dynamic synapses, convenient for information processing and coding.

DNA methylation mediates neural processing after odor learning in the honeybee

PubMed Central

Biergans, Stephanie D.; Claudianos, Charles; Reinhard, Judith; Galizia, C. Giovanni

2017-01-01

DNA methyltransferases (Dnmts) - epigenetic writers catalyzing the transfer of methyl-groups to cytosine (DNA methylation) – regulate different aspects of memory formation in many animal species. In honeybees, Dnmt activity is required to adjust the specificity of olfactory reward memories and bees’ relearning capability. The physiological relevance of Dnmt-mediated DNA methylation in neural networks, however, remains unknown. Here, we investigated how Dnmt activity impacts neuroplasticity in the bees’ primary olfactory center, the antennal lobe (AL) an equivalent of the vertebrate olfactory bulb. The AL is crucial for odor discrimination, an indispensable process in forming specific odor memories. Using pharmacological inhibition, we demonstrate that Dnmt activity influences neural network properties during memory formation in vivo. We show that Dnmt activity promotes fast odor pattern separation in trained bees. Furthermore, Dnmt activity during memory formation increases both the number of responding glomeruli and the response magnitude to a novel odor. These data suggest that Dnmt activity is necessary for a form of homoeostatic network control which might involve inhibitory interneurons in the AL network. PMID:28240742

A New Random Walk for Replica Detection in WSNs.

PubMed

Aalsalem, Mohammed Y; Khan, Wazir Zada; Saad, N M; Hossain, Md Shohrab; Atiquzzaman, Mohammed; Khan, Muhammad Khurram

2016-01-01

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical.

A New Random Walk for Replica Detection in WSNs

PubMed Central

Aalsalem, Mohammed Y.; Saad, N. M.; Hossain, Md. Shohrab; Atiquzzaman, Mohammed; Khan, Muhammad Khurram

2016-01-01

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical. PMID:27409082

Community Seismic Network (CSN)

NASA Astrophysics Data System (ADS)

Clayton, R. W.; Kohler, M. D.; Heaton, T. H.; Massari, A.; Guy, R.; Bunn, J.; Chandy, M.

2015-12-01

The CSN now has approximately 600 stations in the northern Los Angeles region. The sensors are class-C MEMs accelerometers that are packaged with backup power and data memory and are connected to a cloud-based processing system through the Internet. Most of the sensors are located in an xy-spatial network with an average minimum station spacing of 800 m. This density allows the lateral variations in ground motion to be determined, which will lead to detailed microzonation maps of the region. Approximately 100 of the sensors are located on campuses of the Los Angeles Unified School District (LAUSD), and this is part of a plan to provide schools with critical earthquake information immediately following an earthquake using the ShakeCast system. The software system in the sensors is being upgraded to allow on site measurements of PGA and PVA to be sent directly to the ShakeMap and earthquake early warning systems. More than 160 of the sensor packages are located on multiple floors of buildings with typically one or two 3-component sensors per floor. With these data we can identify traveling waves in the building, as well as determine the eigenfrequencies and mode shapes. By monitoring these quantities with high spatial density before, during, and after a major shaking event, we hope to determine the state of health of the structure.

Performing a local reduction operation on a parallel computer

DOEpatents

Blocksome, Michael A; Faraj, Daniel A

2013-06-04

A parallel computer including compute nodes, each including two reduction processing cores, a network write processing core, and a network read processing core, each processing core assigned an input buffer. Copying, in interleaved chunks by the reduction processing cores, contents of the reduction processing cores' input buffers to an interleaved buffer in shared memory; copying, by one of the reduction processing cores, contents of the network write processing core's input buffer to shared memory; copying, by another of the reduction processing cores, contents of the network read processing core's input buffer to shared memory; and locally reducing in parallel by the reduction processing cores: the contents of the reduction processing core's input buffer; every other interleaved chunk of the interleaved buffer; the copied contents of the network write processing core's input buffer; and the copied contents of the network read processing core's input buffer.

Performing a local reduction operation on a parallel computer

DOEpatents

Blocksome, Michael A.; Faraj, Daniel A.

2012-12-11

A parallel computer including compute nodes, each including two reduction processing cores, a network write processing core, and a network read processing core, each processing core assigned an input buffer. Copying, in interleaved chunks by the reduction processing cores, contents of the reduction processing cores' input buffers to an interleaved buffer in shared memory; copying, by one of the reduction processing cores, contents of the network write processing core's input buffer to shared memory; copying, by another of the reduction processing cores, contents of the network read processing core's input buffer to shared memory; and locally reducing in parallel by the reduction processing cores: the contents of the reduction processing core's input buffer; every other interleaved chunk of the interleaved buffer; the copied contents of the network write processing core's input buffer; and the copied contents of the network read processing core's input buffer.

Remote Memory Access Protocol Target Node Intellectual Property

NASA Technical Reports Server (NTRS)

Haddad, Omar

2013-01-01

The MagnetoSpheric Multiscale (MMS) mission had a requirement to use the Remote Memory Access Protocol (RMAP) over its SpaceWire network. At the time, no known intellectual property (IP) cores were available for purchase. Additionally, MMS preferred to implement the RMAP functionality with control over the low-level details of the design. For example, not all the RMAP standard functionality was needed, and it was desired to implement only the portions of the RMAP protocol that were needed. RMAP functionality had been previously implemented in commercial off-the-shelf (COTS) products, but the IP core was not available for purchase. The RMAP Target IP core is a VHDL (VHSIC Hardware Description Language description of a digital logic design suitable for implementation in an FPGA (field-programmable gate array) or ASIC (application-specific integrated circuit) that parses SpaceWire packets that conform to the RMAP standard. The RMAP packet protocol allows a network host to access and control a target device using address mapping. This capability allows SpaceWire devices to be managed in a standardized way that simplifies the hardware design of the device, as well as the development of the software that controls the device. The RMAP Target IP core has some features that are unique and not specified in the RMAP standard. One such feature is the ability to automatically abort transactions if the back-end logic does not respond to read/write requests within a predefined time. When a request times out, the RMAP Target IP core automatically retracts the request and returns a command response with an appropriate status in the response packet s header. Another such feature is the ability to control the SpaceWire node or router using RMAP transactions in the extended address range. This allows the SpaceWire network host to manage the SpaceWire network elements using RMAP packets, which reduces the number of protocols that the network host needs to support.

A class Hierarchical, object-oriented approach to virtual memory management

NASA Technical Reports Server (NTRS)

Russo, Vincent F.; Campbell, Roy H.; Johnston, Gary M.

1989-01-01

The Choices family of operating systems exploits class hierarchies and object-oriented programming to facilitate the construction of customized operating systems for shared memory and networked multiprocessors. The software is being used in the Tapestry laboratory to study the performance of algorithms, mechanisms, and policies for parallel systems. Described here are the architectural design and class hierarchy of the Choices virtual memory management system. The software and hardware mechanisms and policies of a virtual memory system implement a memory hierarchy that exploits the trade-off between response times and storage capacities. In Choices, the notion of a memory hierarchy is captured by abstract classes. Concrete subclasses of those abstractions implement a virtual address space, segmentation, paging, physical memory management, secondary storage, and remote (that is, networked) storage. Captured in the notion of a memory hierarchy are classes that represent memory objects. These classes provide a storage mechanism that contains encapsulated data and have methods to read or write the memory object. Each of these classes provides specializations to represent the memory hierarchy.

Sleep on your memory traces: How sleep effects can be explained by Act-In, a functional memory model.

PubMed

Cherdieu, Mélaine; Versace, Rémy; Rey, Amandine E; Vallet, Guillaume T; Mazza, Stéphanie

2018-06-01

Numerous studies have explored the effect of sleep on memory. It is well known that a period of sleep, compared to a similar period of wakefulness, protects memories from interference, improves performance, and might also reorganize memory traces in a way that encourages creativity and rule extraction. It is assumed that these benefits come from the reactivation of brain networks, mainly involving the hippocampal structure, as well as from their synchronization with neocortical networks during sleep, thereby underpinning sleep-dependent memory consolidation and reorganization. However, this memory reorganization is difficult to explain within classical memory models. The present paper aims to describe whether the influence of sleep on memory could be explained using a multiple trace memory model that is consistent with the concept of embodied cognition: the Act-In (activation-integration) memory model. We propose an original approach to the results observed in sleep research on the basis of two simple mechanisms, namely activation and integration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hippocampal brain-network coordination during volitional exploratory behavior enhances learning

PubMed Central

Voss, Joel L.; Gonsalves, Brian D.; Federmeier, Kara D.; Tranel, Daniel; Cohen, Neal J.

2010-01-01

Exploratory behaviors during learning determine what is studied and when, helping to optimize subsequent memory performance. We manipulated how much control subjects had over the position of a moving window through which they studied objects and their locations, in order to elucidate the cognitive and neural determinants of exploratory behaviors. Our behavioral, neuropsychological, and neuroimaging data indicate volitional control benefits memory performance, and is linked to a brain network centered on the hippocampus. Increases in correlated activity between the hippocampus and other areas were associated with specific aspects of memory, suggesting that volitional control optimizes interactions among specialized neural systems via the hippocampus. Memory is therefore an active process intrinsically linked to behavior. Furthermore, brain structures typically seen as passive participants in memory encoding (e.g., the hippocampus) are actually part of an active network that controls behavior dynamically as it unfolds. PMID:21102449

Pseudo-orthogonalization of memory patterns for associative memory.

PubMed

Oku, Makito; Makino, Takaki; Aihara, Kazuyuki

2013-11-01

A new method for improving the storage capacity of associative memory models on a neural network is proposed. The storage capacity of the network increases in proportion to the network size in the case of random patterns, but, in general, the capacity suffers from correlation among memory patterns. Numerous solutions to this problem have been proposed so far, but their high computational cost limits their scalability. In this paper, we propose a novel and simple solution that is locally computable without any iteration. Our method involves XNOR masking of the original memory patterns with random patterns, and the masked patterns and masks are concatenated. The resulting decorrelated patterns allow higher storage capacity at the cost of the pattern length. Furthermore, the increase in the pattern length can be reduced through blockwise masking, which results in a small amount of capacity loss. Movie replay and image recognition are presented as examples to demonstrate the scalability of the proposed method.

Hippocampal brain-network coordination during volitional exploratory behavior enhances learning.

PubMed

Voss, Joel L; Gonsalves, Brian D; Federmeier, Kara D; Tranel, Daniel; Cohen, Neal J

2011-01-01

Exploratory behaviors during learning determine what is studied and when, helping to optimize subsequent memory performance. To elucidate the cognitive and neural determinants of exploratory behaviors, we manipulated the control that human subjects had over the position of a moving window through which they studied objects and their locations. Our behavioral, neuropsychological and neuroimaging data indicate that volitional control benefits memory performance and is linked to a brain network that is centered on the hippocampus. Increases in correlated activity between the hippocampus and other areas were associated with specific aspects of memory, which suggests that volitional control optimizes interactions among specialized neural systems through the hippocampus. Memory is therefore an active process that is intrinsically linked to behavior. Furthermore, brain structures that are typically seen as passive participants in memory encoding (for example, the hippocampus) are actually part of an active network that controls behavior dynamically as it unfolds.

Retrieval of high-fidelity memory arises from distributed cortical networks.

PubMed

Wais, Peter E; Jahanikia, Sahar; Steiner, Daniel; Stark, Craig E L; Gazzaley, Adam

2017-04-01

Medial temporal lobe (MTL) function is well established as necessary for memory of facts and events. It is likely that lateral cortical regions critically guide cognitive control processes to tune in high-fidelity details that are most relevant for memory retrieval. Here, convergent results from functional and structural MRI show that retrieval of detailed episodic memory arises from lateral cortical-MTL networks, including regions of inferior frontal and angular gyrii. Results also suggest that recognition of items based on low-fidelity, generalized information, rather than memory arising from retrieval of relevant episodic details, is not associated with functional connectivity between MTL and lateral cortical regions. Additionally, individual differences in microstructural properties in white matter pathways, associated with distributed MTL-cortical networks, are positively correlated with better performance on a mnemonic discrimination task. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Cognitive memory.

PubMed

Widrow, Bernard; Aragon, Juan Carlos

2013-05-01

Regarding the workings of the human mind, memory and pattern recognition seem to be intertwined. You generally do not have one without the other. Taking inspiration from life experience, a new form of computer memory has been devised. Certain conjectures about human memory are keys to the central idea. The design of a practical and useful "cognitive" memory system is contemplated, a memory system that may also serve as a model for many aspects of human memory. The new memory does not function like a computer memory where specific data is stored in specific numbered registers and retrieval is done by reading the contents of the specified memory register, or done by matching key words as with a document search. Incoming sensory data would be stored at the next available empty memory location, and indeed could be stored redundantly at several empty locations. The stored sensory data would neither have key words nor would it be located in known or specified memory locations. Sensory inputs concerning a single object or subject are stored together as patterns in a single "file folder" or "memory folder". When the contents of the folder are retrieved, sights, sounds, tactile feel, smell, etc., are obtained all at the same time. Retrieval would be initiated by a query or a prompt signal from a current set of sensory inputs or patterns. A search through the memory would be made to locate stored data that correlates with or relates to the prompt input. The search would be done by a retrieval system whose first stage makes use of autoassociative artificial neural networks and whose second stage relies on exhaustive search. Applications of cognitive memory systems have been made to visual aircraft identification, aircraft navigation, and human facial recognition. Concerning human memory, reasons are given why it is unlikely that long-term memory is stored in the synapses of the brain's neural networks. Reasons are given suggesting that long-term memory is stored in DNA or RNA. Neural networks are an important component of the human memory system, and their purpose is for information retrieval, not for information storage. The brain's neural networks are analog devices, subject to drift and unplanned change. Only with constant training is reliable action possible. Good training time is during sleep and while awake and making use of one's memory. A cognitive memory is a learning system. Learning involves storage of patterns or data in a cognitive memory. The learning process for cognitive memory is unsupervised, i.e. autonomous. Copyright © 2013 Elsevier Ltd. All rights reserved.

Execution models for mapping programs onto distributed memory parallel computers

NASA Technical Reports Server (NTRS)

Sussman, Alan

1992-01-01

The problem of exploiting the parallelism available in a program to efficiently employ the resources of the target machine is addressed. The problem is discussed in the context of building a mapping compiler for a distributed memory parallel machine. The paper describes using execution models to drive the process of mapping a program in the most efficient way onto a particular machine. Through analysis of the execution models for several mapping techniques for one class of programs, we show that the selection of the best technique for a particular program instance can make a significant difference in performance. On the other hand, the results of benchmarks from an implementation of a mapping compiler show that our execution models are accurate enough to select the best mapping technique for a given program.

Dynamic Photorefractive Memory and its Application for Opto-Electronic Neural Networks.

NASA Astrophysics Data System (ADS)

Sasaki, Hironori

This dissertation describes the analysis of the photorefractive crystal dynamics and its application for opto-electronic neural network systems. The realization of the dynamic photorefractive memory is investigated in terms of the following aspects: fast memory update, uniform grating multiplexing schedules and the prevention of the partial erasure of existing gratings. The fast memory update is realized by the selective erasure process that superimposes a new grating on the original one with an appropriate phase shift. The dynamics of the selective erasure process is analyzed using the first-order photorefractive material equations and experimentally confirmed. The effects of beam coupling and fringe bending on the selective erasure dynamics are also analyzed by numerically solving a combination of coupled wave equations and the photorefractive material equation. Incremental recording technique is proposed as a uniform grating multiplexing schedule and compared with the conventional scheduled recording technique in terms of phase distribution in the presence of an external dc electric field, as well as the image gray scale dependence. The theoretical analysis and experimental results proved the superiority of the incremental recording technique over the scheduled recording. Novel recirculating information memory architecture is proposed and experimentally demonstrated to prevent partial degradation of the existing gratings by accessing the memory. Gratings are circulated through a memory feed back loop based on the incremental recording dynamics and demonstrate robust read/write/erase capabilities. The dynamic photorefractive memory is applied to opto-electronic neural network systems. Module architecture based on the page-oriented dynamic photorefractive memory is proposed. This module architecture can implement two complementary interconnection organizations, fan-in and fan-out. The module system scalability and the learning capabilities are theoretically investigated using the photorefractive dynamics described in previous chapters of the dissertation. The implementation of the feed-forward image compression network with 900 input and 9 output neurons with 6-bit interconnection accuracy is experimentally demonstrated. Learning of the Perceptron network that determines sex based on input face images of 900 pixels is also successfully demonstrated.

The impact of social activities, social networks, social support and social relationships on the cognitive functioning of healthy older adults: a systematic review.

PubMed

Kelly, Michelle E; Duff, Hollie; Kelly, Sara; McHugh Power, Joanna E; Brennan, Sabina; Lawlor, Brian A; Loughrey, David G

2017-12-19

Social relationships, which are contingent on access to social networks, promote engagement in social activities and provide access to social support. These social factors have been shown to positively impact health outcomes. In the current systematic review, we offer a comprehensive overview of the impact of social activities, social networks and social support on the cognitive functioning of healthy older adults (50+) and examine the differential effects of aspects of social relationships on various cognitive domains. We followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, and collated data from randomised controlled trials (RCTs), genetic and observational studies. Independent variables of interest included subjective measures of social activities, social networks, and social support, and composite measures of social relationships (CMSR). The primary outcome of interest was cognitive function divided into domains of episodic memory, semantic memory, overall memory ability, working memory, verbal fluency, reasoning, attention, processing speed, visuospatial abilities, overall executive functioning and global cognition. Thirty-nine studies were included in the review; three RCTs, 34 observational studies, and two genetic studies. Evidence suggests a relationship between (1) social activity and global cognition and overall executive functioning, working memory, visuospatial abilities and processing speed but not episodic memory, verbal fluency, reasoning or attention; (2) social networks and global cognition but not episodic memory, attention or processing speed; (3) social support and global cognition and episodic memory but not attention or processing speed; and (4) CMSR and episodic memory and verbal fluency but not global cognition. The results support prior conclusions that there is an association between social relationships and cognitive function but the exact nature of this association remains unclear. Implications of the findings are discussed and suggestions for future research provided. PROSPERO 2012: CRD42012003248 .

Shape memory polymers

DOEpatents

Wilson, Thomas S.; Bearinger, Jane P.

2017-08-29

New shape memory polymer compositions, methods for synthesizing new shape memory polymers, and apparatus comprising an actuator and a shape memory polymer wherein the shape memory polymer comprises at least a portion of the actuator. A shape memory polymer comprising a polymer composition which physically forms a network structure wherein the polymer composition has shape-memory behavior and can be formed into a permanent primary shape, re-formed into a stable secondary shape, and controllably actuated to recover the permanent primary shape. Polymers have optimal aliphatic network structures due to minimization of dangling chains by using monomers that are symmetrical and that have matching amine and hydroxl groups providing polymers and polymer foams with clarity, tight (narrow temperature range) single transitions, and high shape recovery and recovery force that are especially useful for implanting in the human body.

Shape memory polymers

DOEpatents

Wilson, Thomas S.; Bearinger, Jane P.

2015-06-09

New shape memory polymer compositions, methods for synthesizing new shape memory polymers, and apparatus comprising an actuator and a shape memory polymer wherein the shape memory polymer comprises at least a portion of the actuator. A shape memory polymer comprising a polymer composition which physically forms a network structure wherein the polymer composition has shape-memory behavior and can be formed into a permanent primary shape, re-formed into a stable secondary shape, and controllably actuated to recover the permanent primary shape. Polymers have optimal aliphatic network structures due to minimization of dangling chains by using monomers that are symmetrical and that have matching amine and hydroxyl groups providing polymers and polymer foams with clarity, tight (narrow temperature range) single transitions, and high shape recovery and recovery force that are especially useful for implanting in the human body.

Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits

PubMed Central

Mai, Naikeng; Zhong, Xiaomei; Chen, Ben; Peng, Qi; Wu, Zhangying; Zhang, Weiru; Ouyang, Cong; Ning, Yuping

2017-01-01

Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients. PMID:28878666

Weight Rich-Club Analysis in the White Matter Network of Late-Life Depression with Memory Deficits.

PubMed

Mai, Naikeng; Zhong, Xiaomei; Chen, Ben; Peng, Qi; Wu, Zhangying; Zhang, Weiru; Ouyang, Cong; Ning, Yuping

2017-01-01

Patients with late-life depression (LLD) have a higher incident of developing dementia, especially individuals with memory deficits. However, little is known about the white matter characteristics of LLD with memory deficits (LLD-MD) in the human connectome, especially for the rich-club coefficient, which is an indicator that describes the organization pattern of hub in the network. To address this question, diffusion tensor imaging of 69 participants [15 LLD-MD patients; 24 patients with LLD with intact memory (LLD-IM); and 30 healthy controls (HC)] was applied to construct a brain network for each individual. A full-scale battery of neuropsychological tests were used for grouping, and evaluating executive function, processing speed and memory. Rich-club analysis and global network properties were utilized to describe the topological features in each group. Network-based statistics (NBS) were calculated to identify the impaired subnetwork in the LLD-MD group relative to that in the LLD-IM group. We found that compared with HC participants, patients with LLD (LLD-MD and LLD-IM) had relatively impaired rich-club organizations and rich-club connectivity. In addition, LLD-MD group exhibited lower feeder and local connective average strength than LLD-IM group. Furthermore, global network properties, such as the shortest path length, connective strength, efficiency and fault tolerant efficiency, were significantly decreased in the LLD-MD group relative to those in the LLD-IM and HC groups. According to NBS analysis, a subnetwork, including right cognitive control network (CCN) and corticostriatal circuits, were disrupted in LLD-MD patients. In conclusion, the disease effects of LLD were prevalent in rich-club organization. Feeder and local connections, especially in the subnetwork including right CCN and corticostriatal circuits, were further impaired in those with memory deficits. Global network properties were disrupted in LLD-MD patients relative to those in LLD-IM patients.

Investigating Ground Swarm Robotics Using Agent Based Simulation

DTIC Science & Technology

2006-12-01

Incorporation of virtual pheromones as a shared memory map is modeled as an additional capability that is found to enhance the robustness and reliability of the...virtual pheromones as a shared memory map is modeled as an additional capability that is found to enhance the robustness and reliability of the swarm... PHEROMONES .......................................... 42 1. Repel Friends under Inorganic SA.................................................. 45 2. Max

The Relationship between Phonological Memory, Receptive Vocabulary, and Fast Mapping in Young Children with Specific Language Impairment

ERIC Educational Resources Information Center

Gray, Shelley

2006-01-01

Purpose: This study assessed the fast mapping performance of children with specific language impairment (SLI) across the preschool to kindergarten age span in relation to their phonological memory and vocabulary development. Method: Fifty-three children diagnosed with SLI and 53 children with normal language (NL) matched for age and gender (30…

A Violation of the Conditional Independence Assumption in the Two-High-Threshold Model of Recognition Memory

ERIC Educational Resources Information Center

Chen, Tina; Starns, Jeffrey J.; Rotello, Caren M.

2015-01-01

The 2-high-threshold (2HT) model of recognition memory assumes that test items result in distinct internal states: they are either detected or not, and the probability of responding at a particular confidence level that an item is "old" or "new" depends on the state-response mapping parameters. The mapping parameters are…

Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays

NASA Astrophysics Data System (ADS)

Wan, Li; Zhou, Qinghua

2007-10-01

The stability property of stochastic hybrid bidirectional associate memory (BAM) neural networks with discrete delays is considered. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the delay-independent sufficient conditions to guarantee the exponential stability of the equilibrium solution for such networks are given by using the nonnegative semimartingale convergence theorem.

Image display device in digital TV

DOEpatents

Choi, Seung Jong [Seoul, KR

2006-07-18

Disclosed is an image display device in a digital TV that is capable of carrying out the conversion into various kinds of resolution by using single bit map data in the digital TV. The image display device includes: a data processing part for executing bit map conversion, compression, restoration and format-conversion for text data; a memory for storing the bit map data obtained according to the bit map conversion and compression in the data processing part and image data inputted from an arbitrary receiving part, the receiving part receiving one of digital image data and analog image data; an image outputting part for reading the image data from the memory; and a display processing part for mixing the image data read from the image outputting part and the bit map data converted in format from the a data processing part. Therefore, the image display device according to the present invention can convert text data in such a manner as to correspond with various resolution, carry out the compression for bit map data, thereby reducing the memory space, and support text data of an HTML format, thereby providing the image with the text data of various shapes.

Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory.

PubMed

Gruenenfelder, Thomas M; Recchia, Gabriel; Rubin, Tim; Jones, Michael N

2016-08-01

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts. Copyright © 2015 Cognitive Science Society, Inc.

Robust sequential working memory recall in heterogeneous cognitive networks

PubMed Central

Rabinovich, Mikhail I.; Sokolov, Yury; Kozma, Robert

2014-01-01

Psychiatric disorders are often caused by partial heterogeneous disinhibition in cognitive networks, controlling sequential and spatial working memory (SWM). Such dynamic connectivity changes suggest that the normal relationship between the neuronal components within the network deteriorates. As a result, competitive network dynamics is qualitatively altered. This dynamics defines the robust recall of the sequential information from memory and, thus, the SWM capacity. To understand pathological and non-pathological bifurcations of the sequential memory dynamics, here we investigate the model of recurrent inhibitory-excitatory networks with heterogeneous inhibition. We consider the ensemble of units with all-to-all inhibitory connections, in which the connection strengths are monotonically distributed at some interval. Based on computer experiments and studying the Lyapunov exponents, we observed and analyzed the new phenomenon—clustered sequential dynamics. The results are interpreted in the context of the winnerless competition principle. Accordingly, clustered sequential dynamics is represented in the phase space of the model by two weakly interacting quasi-attractors. One of them is similar to the sequential heteroclinic chain—the regular image of SWM, while the other is a quasi-chaotic attractor. Coexistence of these quasi-attractors means that the recall of the normal information sequence is intermittently interrupted by episodes with chaotic dynamics. We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions. PMID:25452717

Network inference from functional experimental data (Conference Presentation)

NASA Astrophysics Data System (ADS)

Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.

2016-03-01

Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic conditions.

An optimization method of VON mapping for energy efficiency and routing in elastic optical networks

NASA Astrophysics Data System (ADS)

Liu, Huanlin; Xiong, Cuilian; Chen, Yong; Li, Changping; Chen, Derun

2018-03-01

To improve resources utilization efficiency, network virtualization in elastic optical networks has been developed by sharing the same physical network for difference users and applications. In the process of virtual nodes mapping, longer paths between physical nodes will consume more spectrum resources and energy. To address the problem, we propose a virtual optical network mapping algorithm called genetic multi-objective optimize virtual optical network mapping algorithm (GM-OVONM-AL), which jointly optimizes the energy consumption and spectrum resources consumption in the process of virtual optical network mapping. Firstly, a vector function is proposed to balance the energy consumption and spectrum resources by optimizing population classification and crowding distance sorting. Then, an adaptive crossover operator based on hierarchical comparison is proposed to improve search ability and convergence speed. In addition, the principle of the survival of the fittest is introduced to select better individual according to the relationship of domination rank. Compared with the spectrum consecutiveness-opaque virtual optical network mapping-algorithm and baseline-opaque virtual optical network mapping algorithm, simulation results show the proposed GM-OVONM-AL can achieve the lowest bandwidth blocking probability and save the energy consumption.

Functional magnetic resonance imaging.

PubMed

Buchbinder, Bradley R

2016-01-01

Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks. © 2016 Elsevier B.V. All rights reserved.

The Statistical Mechanics of Dilute, Disordered Systems

NASA Astrophysics Data System (ADS)

Blackburn, Roger Michael

Available from UMI in association with The British Library. Requires signed TDF. A graph partitioning problem with variable inter -partition costs is studied by exploiting its mapping on to the Ashkin-Teller spin glass. The cavity method is used to derive the TAP equations and free energy for both extensively connected and dilute systems. Unlike Ising and Potts spin glasses, the self-consistent equation for the distribution of effective fields does not have a solution solely made up of delta functions. Numerical integration is used to find the stable solution, from which the ground state energy is calculated. Simulated annealing is used to test the results. The retrieving activity distribution for networks of boolean functions trained as associative memories for optimal capacity is derived. For infinite networks, outputs are shown to be frozen, in contrast to dilute asymmetric networks trained with the Hebb rule. For finite networks, a steady leaking to the non-retrieving attractor is demonstrated. Simulations of quenched networks are reported which show a departure from this picture: some configurations remain frozen for all time, while others follow cycles of small periods. An estimate of the critical capacity from the simulations is found to be in broad agreement with recent analytical results. The existing theory is extended to include noise on recall, and the behaviour is found to be robust to noise up to order 1/c^2 for networks with connectivity c.

Mapping interference resolution across task domains: A shared control process in left inferior frontal gyrus

PubMed Central

Nelson, James K.; Reuter-Lorenz, Patricia A.; Persson, Jonas; Sylvester, Ching-Yune C.; Jonides, John

2009-01-01

Work in functional neuroimaging has mapped interference resolution processing onto left inferior frontal regions for both verbal working memory and a variety of semantic processing tasks. The proximity of the identified regions from these different tasks suggests the existence of a common, domain-general interference resolution mechanism. The current research specifically tests this idea in a within-subject design using fMRI to assess the activation associated with variable selection requirements in a semantic retrieval task (verb generation) and a verbal working memory task with a trial-specific proactive interference manipulation (recent-probes). High interference trials on both tasks were associated with activity in the midventrolateral region of the left inferior frontal gyrus, and the regions activated in each task strongly overlapped. The results indicate that an elemental component of executive control associated with interference resolution during retrieval from working memory and from semantic memory can be mapped to a common portion of the left inferior frontal gyrus. PMID:19111526

MMAPDNG: A new, fast code backed by a memory-mapped database for simulating delayed γ-ray emission with MCNPX package

NASA Astrophysics Data System (ADS)

Lou, Tak Pui; Ludewigt, Bernhard

2015-09-01

The simulation of the emission of beta-delayed gamma rays following nuclear fission and the calculation of time-dependent energy spectra is a computational challenge. The widely used radiation transport code MCNPX includes a delayed gamma-ray routine that is inefficient and not suitable for simulating complex problems. This paper describes the code "MMAPDNG" (Memory-Mapped Delayed Neutron and Gamma), an optimized delayed gamma module written in C, discusses usage and merits of the code, and presents results. The approach is based on storing required Fission Product Yield (FPY) data, decay data, and delayed particle data in a memory-mapped file. When compared to the original delayed gamma-ray code in MCNPX, memory utilization is reduced by two orders of magnitude and the ray sampling is sped up by three orders of magnitude. Other delayed particles such as neutrons and electrons can be implemented in future versions of MMAPDNG code using its existing framework.

Feature to prototype transition in neural networks

NASA Astrophysics Data System (ADS)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

A simplified memory network model based on pattern formations

NASA Astrophysics Data System (ADS)

Xu, Kesheng; Zhang, Xiyun; Wang, Chaoqing; Liu, Zonghua

2014-12-01

Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to have a long-period rhythmic synchronous firing in a scale-free network, provided the existence of both the high-degree hubs and the loops formed by low-degree nodes. We here present a simplified memory network model to show that the self-sustained synchronous firing can be observed even without these two necessary conditions. This simplified network consists of two loops of coupled excitable neurons with different synaptic conductance and with one node being the sensory neuron to receive an external stimulus signal. This model can be further used to show how the diversity of firing patterns can be selectively formed by varying the signal frequency, duration of the stimulus and network topology, which corresponds to the patterns of STM and LTM with different time scales. A theoretical analysis is presented to explain the underlying mechanism of firing patterns.

Landmarks selection in street map design

NASA Astrophysics Data System (ADS)

Kao, C. J.

2014-02-01

In Taiwan many electrical maps present their landmarks according to the category of the feature, a designer short of knowledge about mental representation of space, can cause the map to lose its communication effects. To resolve this map design problem, in this research through long-term memory recall, navigation and observation, and short-term memory processing 111 participants were asked to select the proper landmark from study area. The results reveal that in Taiwan convenience stores are the most popular local landmark in rural and urban areas. Their commercial signs have a unique design and bright color. Contrasted to their background, this makes the convenience store a salient feature. This study also developed a rule to assess the priority of the landmarks to design them in different scale maps.

The parietal memory network activates similarly for true and associative false recognition elicited via the DRM procedure.

PubMed

McDermott, Kathleen B; Gilmore, Adrian W; Nelson, Steven M; Watson, Jason M; Ojemann, Jeffrey G

2017-02-01

Neuroimaging investigations of human memory encoding and retrieval have revealed that multiple regions of parietal cortex contribute to memory. Recently, a sparse network of regions within parietal cortex has been identified using resting state functional connectivity (MRI techniques). The regions within this network exhibit consistent task-related responses during memory formation and retrieval, leading to its being called the parietal memory network (PMN). Among its signature patterns are: deactivation during initial experience with an item (e.g., encoding); activation during subsequent repetitions (e.g., at retrieval); greater activation for successfully retrieved familiar words than novel words (e.g., hits relative to correctly-rejected lures). The question of interest here is whether novel words that are subjectively experienced as having been recently studied would elicit PMN activation similar to that of hits. That is, we compared old items correctly recognized to two types of novel items on a recognition test: those correctly identified as new and those incorrectly labeled as old due to their strong associative relation to the studied words (in the DRM false memory protocol). Subjective oldness plays a strong role in driving activation, as hits and false alarms activated similarly (and greater than correctly-rejected lures). Copyright © 2016 Elsevier Ltd. All rights reserved.

An exemplar-familiarity model predicts short-term and long-term probe recognition across diverse forms of memory search.

PubMed

Nosofsky, Robert M; Cox, Gregory E; Cao, Rui; Shiffrin, Richard M

2014-11-01

Experiments were conducted to test a modern exemplar-familiarity model on its ability to account for both short-term and long-term probe recognition within the same memory-search paradigm. Also, making connections to the literature on attention and visual search, the model was used to interpret differences in probe-recognition performance across diverse conditions that manipulated relations between targets and foils across trials. Subjects saw lists of from 1 to 16 items followed by a single item recognition probe. In a varied-mapping condition, targets and foils could switch roles across trials; in a consistent-mapping condition, targets and foils never switched roles; and in an all-new condition, on each trial a completely new set of items formed the memory set. In the varied-mapping and all-new conditions, mean correct response times (RTs) and error proportions were curvilinear increasing functions of memory set size, with the RT results closely resembling ones from hybrid visual-memory search experiments reported by Wolfe (2012). In the consistent-mapping condition, new-probe RTs were invariant with set size, whereas old-probe RTs increased slightly with increasing study-test lag. With appropriate choice of psychologically interpretable free parameters, the model accounted well for the complete set of results. The work provides support for the hypothesis that a common set of processes involving exemplar-based familiarity may govern long-term and short-term probe recognition across wide varieties of memory- search conditions. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Social networks: Evolving graphs with memory dependent edges

NASA Astrophysics Data System (ADS)

Grindrod, Peter; Parsons, Mark

2011-10-01

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

Multistability in bidirectional associative memory neural networks

NASA Astrophysics Data System (ADS)

Huang, Gan; Cao, Jinde

2008-04-01

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2 n-dimensional networks can have 3 equilibria and 2 equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.

Failure detection in high-performance clusters and computers using chaotic map computations

DOEpatents

Rao, Nageswara S.

2015-09-01

A programmable media includes a processing unit capable of independent operation in a machine that is capable of executing 10.sup.18 floating point operations per second. The processing unit is in communication with a memory element and an interconnect that couples computing nodes. The programmable media includes a logical unit configured to execute arithmetic functions, comparative functions, and/or logical functions. The processing unit is configured to detect computing component failures, memory element failures and/or interconnect failures by executing programming threads that generate one or more chaotic map trajectories. The central processing unit or graphical processing unit is configured to detect a computing component failure, memory element failure and/or an interconnect failure through an automated comparison of signal trajectories generated by the chaotic maps.

Interaction of multiple networks modulated by the working memory training based on real-time fMRI

NASA Astrophysics Data System (ADS)

Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie

2015-03-01

Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

Synaptic tagging, evaluation of memories, and the distal reward problem.

PubMed

Päpper, Marc; Kempter, Richard; Leibold, Christian

2011-01-01

Long-term synaptic plasticity exhibits distinct phases. The synaptic tagging hypothesis suggests an early phase in which synapses are prepared, or "tagged," for protein capture, and a late phase in which those proteins are integrated into the synapses to achieve memory consolidation. The synapse specificity of the tags is consistent with conventional neural network models of associative memory. Memory consolidation through protein synthesis, however, is neuron specific, and its functional role in those models has not been assessed. Here, using a theoretical network model, we test the tagging hypothesis on its potential to prolong memory lifetimes in an online-learning paradigm. We find that protein synthesis, though not synapse specific, prolongs memory lifetimes if it is used to evaluate memory items on a cellular level. In our model we assume that only "important" memory items evoke protein synthesis such that these become more stable than "unimportant" items, which do not evoke protein synthesis. The network model comprises an equilibrium distribution of synaptic states that is very susceptible to the storage of new items: Most synapses are in a state in which they are plastic and can be changed easily, whereas only those synapses that are essential for the retrieval of the important memory items are in the stable late phase. The model can solve the distal reward problem, where the initial exposure of a memory item and its evaluation are temporally separated. Synaptic tagging hence provides a viable mechanism to consolidate and evaluate memories on a synaptic basis.

Shift and rotation invariant photorefractive crystal-based associative memory

NASA Astrophysics Data System (ADS)

Uang, Chii-Maw; Lin, Wei-Feng; Lu, Ming-Huei; Lu, Guowen; Lu, Mingzhe

1995-08-01

A shift and rotation invariant photorefractive (PR) crystal based associative memory is addressed. The proposed associative memory has three layers: the feature extraction, inner- product, and output mapping layers. The feature extraction is performed by expanding an input object into a set of circular harmonic expansions (CHE) in the Fourier domain to acquire both the shift and rotation invariant properties. The inner product operation is performed by taking the advantage of Bragg diffraction of the bulky PR-crystal. The output mapping is achieved by using the massive storage capacity of the PR-crystal. In the training process, memories are stored in another PR-crystal by using the wavelength multiplexing technique. During the recall process, the output from the winner-take-all processor decides which wavelength should be used to read out the memory from the PR-crystal.

Brain Network Changes and Memory Decline in Aging

PubMed Central

Beason-Held, Lori L.; Hohman, Timothy J.; Venkatraman, Vijay; An, Yang; Resnick, Susan M.

2016-01-01

One theory of age-related cognitive decline proposes that changes within the default mode network (DMN) of the brain impact the ability to successfully perform cognitive operations. To investigate this theory, we examined functional covariance within brain networks using regional cerebral blood flow data, measured by 15O-water PET, from 99 participants (mean baseline age 68.6 ±7.5) in the Baltimore Longitudinal Study of Aging collected over a 7.4 year period. The sample was divided in tertiles based on longitudinal performance on a verbal recognition memory task administered during scanning, and functional covariance was compared between the upper (improvers) and lower (decliners) tertile groups. The DMN and verbal memory networks (VMN) were then examined during the verbal memory scan condition. For each network, group differences in node-to-network coherence and individual node-to-node covariance relationships were assessed at baseline and in change over time. Compared with improvers, decliners showed differences in node-to-network coherence and in node-to-node relationships in the DMN but not the VMN during verbal memory. These DMN differences reflected greater covariance with better task performance at baseline and both increasing and declining covariance with declining task performance over time for decliners. When examined during the resting state alone, the direction of change in DMN covariance was similar to that seen during task performance, but node-to-node relationships differed from those observed during the task condition. These results suggest that disengagement of DMN components during task performance is not essential for successful cognitive performance as previously proposed. Instead, a proper balance in network processes may be needed to support optimal task performance. PMID:27319002

Memory loss in Alzheimer's disease

PubMed Central

Jahn, Holger

2013-01-01

Loss of memory is among the first symptoms reported by patients suffering from Alzheimer's disease (AD) and by their caretakers. Working memory and long-term declarative memory are affected early during the course of the disease. The individual pattern of impaired memory functions correlates with parameters of structural or functional brain integrity. AD pathology interferes with the formation of memories from the molecular level to the framework of neural networks. The investigation of AD memory loss helps to identify the involved neural structures, such as the default mode network, the influence of epigenetic and genetic factors, such as ApoE4 status, and evolutionary aspects of human cognition. Clinically, the analysis of memory assists the definition of AD subtypes, disease grading, and prognostic predictions. Despite new AD criteria that allow the earlier diagnosis of the disease by inclusion of biomarkers derived from cerebrospinal fluid or hippocampal volume analysis, neuropsychological testing remains at the core of AD diagnosis. PMID:24459411

System for loading executable code into volatile memory in a downhole tool

DOEpatents

Hall, David R.; Bartholomew, David B.; Johnson, Monte L.

2007-09-25

A system for loading an executable code into volatile memory in a downhole tool string component comprises a surface control unit comprising executable code. An integrated downhole network comprises data transmission elements in communication with the surface control unit and the volatile memory. The executable code, stored in the surface control unit, is not permanently stored in the downhole tool string component. In a preferred embodiment of the present invention, the downhole tool string component comprises boot memory. In another embodiment, the executable code is an operating system executable code. Preferably, the volatile memory comprises random access memory (RAM). A method for loading executable code to volatile memory in a downhole tool string component comprises sending the code from the surface control unit to a processor in the downhole tool string component over the network. A central processing unit writes the executable code in the volatile memory.

Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference.

PubMed

Zeithamova, Dagmar; Dominick, April L; Preston, Alison R

2012-07-12

Memory enables flexible use of past experience to inform new behaviors. Although leading theories hypothesize that this fundamental flexibility results from the formation of integrated memory networks relating multiple experiences, the neural mechanisms that support memory integration are not well understood. Here, we demonstrate that retrieval-mediated learning, whereby prior event details are reinstated during encoding of related experiences, supports participants' ability to infer relationships between distinct events that share content. Furthermore, we show that activation changes in a functionally coupled hippocampal and ventral medial prefrontal cortical circuit track the formation of integrated memories and successful inferential memory performance. These findings characterize the respective roles of these regions in retrieval-mediated learning processes that support relational memory network formation and inferential memory in the human brain. More broadly, these data reveal fundamental mechanisms through which memory representations are constructed into prospectively useful formats. Copyright © 2012 Elsevier Inc. All rights reserved.

Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference

PubMed Central

Zeithamova, Dagmar; Dominick, April L.; Preston, Alison R.

2012-01-01

SUMMARY Memory enables flexible use of past experience to inform new behaviors. Though leading theories hypothesize that this fundamental flexibility results from the formation of integrated memory networks relating multiple experiences, the neural mechanisms that support memory integration are not well understood. Here, we demonstrate that retrieval-mediated learning, whereby prior event details are reinstated during encoding of related experiences, supports participants’ ability to infer relationships between distinct events that share content. Furthermore, we show that activation changes in a functionally coupled hippocampal and ventral medial prefrontal cortical circuit track the formation of integrated memories and successful inferential memory performance. These findings characterize the respective roles of these regions in retrieval-mediated learning processes that support relational memory network formation and inferential memory in the human brain. More broadly, these data reveal fundamental mechanisms through which memory representations are constructed into prospectively useful formats. PMID:22794270

Engrams and circuits crucial for systems consolidation of a memory.

PubMed

Kitamura, Takashi; Ogawa, Sachie K; Roy, Dheeraj S; Okuyama, Teruhiro; Morrissey, Mark D; Smith, Lillian M; Redondo, Roger L; Tonegawa, Susumu

2017-04-07

Episodic memories initially require rapid synaptic plasticity within the hippocampus for their formation and are gradually consolidated in neocortical networks for permanent storage. However, the engrams and circuits that support neocortical memory consolidation have thus far been unknown. We found that neocortical prefrontal memory engram cells, which are critical for remote contextual fear memory, were rapidly generated during initial learning through inputs from both the hippocampal-entorhinal cortex network and the basolateral amygdala. After their generation, the prefrontal engram cells, with support from hippocampal memory engram cells, became functionally mature with time. Whereas hippocampal engram cells gradually became silent with time, engram cells in the basolateral amygdala, which were necessary for fear memory, were maintained. Our data provide new insights into the functional reorganization of engrams and circuits underlying systems consolidation of memory. Copyright © 2017, American Association for the Advancement of Science.

Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI.

PubMed

Magnuson, Matthew Evan; Thompson, Garth John; Schwarb, Hillary; Pan, Wen-Ju; McKinley, Andy; Schumacher, Eric H; Keilholz, Shella Dawn

2015-12-01

The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.

Memory and learning behaviors mimicked in nanogranular SiO2-based proton conductor gated oxide-based synaptic transistors

NASA Astrophysics Data System (ADS)

Wan, Chang Jin; Zhu, Li Qiang; Zhou, Ju Mei; Shi, Yi; Wan, Qing

2013-10-01

In neuroscience, signal processing, memory and learning function are established in the brain by modifying ionic fluxes in neurons and synapses. Emulation of memory and learning behaviors of biological systems by nanoscale ionic/electronic devices is highly desirable for building neuromorphic systems or even artificial neural networks. Here, novel artificial synapses based on junctionless oxide-based protonic/electronic hybrid transistors gated by nanogranular phosphorus-doped SiO2-based proton-conducting films are fabricated on glass substrates by a room-temperature process. Short-term memory (STM) and long-term memory (LTM) are mimicked by tuning the pulse gate voltage amplitude. The LTM process in such an artificial synapse is due to the proton-related interfacial electrochemical reaction. Our results are highly desirable for building future neuromorphic systems or even artificial networks via electronic elements.In neuroscience, signal processing, memory and learning function are established in the brain by modifying ionic fluxes in neurons and synapses. Emulation of memory and learning behaviors of biological systems by nanoscale ionic/electronic devices is highly desirable for building neuromorphic systems or even artificial neural networks. Here, novel artificial synapses based on junctionless oxide-based protonic/electronic hybrid transistors gated by nanogranular phosphorus-doped SiO2-based proton-conducting films are fabricated on glass substrates by a room-temperature process. Short-term memory (STM) and long-term memory (LTM) are mimicked by tuning the pulse gate voltage amplitude. The LTM process in such an artificial synapse is due to the proton-related interfacial electrochemical reaction. Our results are highly desirable for building future neuromorphic systems or even artificial networks via electronic elements. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr02987e

Effective visual working memory capacity: an emergent effect from the neural dynamics in an attractor network.

PubMed

Dempere-Marco, Laura; Melcher, David P; Deco, Gustavo

2012-01-01

The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.

Effective Visual Working Memory Capacity: An Emergent Effect from the Neural Dynamics in an Attractor Network

PubMed Central

Dempere-Marco, Laura; Melcher, David P.; Deco, Gustavo

2012-01-01

The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions. PMID:22952608

Use of Preoperative Functional MRI to Predict Verbal Memory Decline After Temporal Lobe Epilepsy Surgery

PubMed Central

Binder, Jeffrey R.; Sabsevitz, David S.; Swanson, Sara J.; Hammeke, Thomas A.; Raghavan, Manoj; Mueller, Wade M.

2010-01-01

Purpose Verbal memory decline is a frequent complication of left anterior temporal lobectomy (L-ATL). The goal of this study was to determine whether preoperative language mapping using functional magnetic resonance imaging (fMRI) is useful for predicting which patients are likely to experience verbal memory decline after L-ATL. Methods Sixty L-ATL patients underwent preoperative language mapping with fMRI, preoperative intracarotid amobarbital (Wada) testing for language and memory lateralization, and pre- and postoperative neuropsychological testing. Demographic, historical, neuropsychological, and imaging variables were examined for their ability to predict pre- to postoperative memory change. Results Verbal memory decline occurred in over 30% of patients. Good preoperative performance, late age at onset of epilepsy, left dominance on fMRI, and left dominance on the Wada test were each predictive of memory decline. Preoperative performance and age at onset together accounted for roughly 50% of the variance in memory outcome (p < .001), and fMRI explained an additional 10% of this variance (p ≤ .003). Neither Wada memory asymmetry nor Wada language asymmetry added additional predictive power beyond these noninvasive measures. Discussion Preoperative fMRI is useful for identifying patients at high risk for verbal memory decline prior to L-ATL surgery. Lateralization of language is correlated with lateralization of verbal memory, whereas Wada memory testing is either insufficiently reliable or insufficiently material-specific to accurately localize verbal memory processes. PMID:18435753

Fast entrainment of human electroencephalogram to a theta-band photic flicker during successful memory encoding.

PubMed

Sato, Naoyuki

2013-01-01

Theta band power (4-8 Hz) in the scalp electroencephalogram (EEG) is thought to be stronger during memory encoding for subsequently remembered items than for forgotten items. According to simultaneous EEG-functional magnetic resonance imaging (fMRI) measurements, the memory-dependent EEG theta is associated with multiple regions of the brain. This suggests that the multiple regions cooperate with EEG theta synchronization during successful memory encoding. However, a question still remains: What kind of neural dynamic organizes such a memory-dependent global network? In this study, the modulation of the EEG theta entrainment property during successful encoding was hypothesized to lead to EEG theta synchronization among a distributed network. Then, a transient response of EEG theta to a theta-band photic flicker with a short duration was evaluated during memory encoding. In the results, flicker-induced EEG power increased and decreased with a time constant of several hundred milliseconds following the onset and the offset of the flicker, respectively. Importantly, the offset response of EEG power was found to be significantly decreased during successful encoding. Moreover, the offset response of the phase locking index was also found to associate with memory performance. According to computational simulations, the results are interpreted as a smaller time constant (i.e., faster response) of a driven harmonic oscillator rather than a change in the spontaneous oscillatory input. This suggests that the fast response of EEG theta forms a global EEG theta network among memory-related regions during successful encoding, and it contributes to a flexible formation of the network along the time course.

Memory Retrieval Time and Memory Capacity of the CA3 Network: Role of Gamma Frequency Oscillations

ERIC Educational Resources Information Center

de Almeida, Licurgo; Idiart, Marco; Lisman, John E.

2007-01-01

The existence of recurrent synaptic connections in CA3 led to the hypothesis that CA3 is an autoassociative network similar to the Hopfield networks studied by theorists. CA3 undergoes gamma frequency periodic inhibition that prevents a persistent attractor state. This argues against the analogy to Hopfield nets, in which an attractor state can be…

Order recall in verbal short-term memory: The role of semantic networks.

PubMed

Poirier, Marie; Saint-Aubin, Jean; Mair, Ali; Tehan, Gerry; Tolan, Anne

2015-04-01

In their recent article, Acheson, MacDonald, and Postle (Journal of Experimental Psychology: Learning, Memory, and Cognition 37:44-59, 2011) made an important but controversial suggestion: They hypothesized that (a) semantic information has an effect on order information in short-term memory (STM) and (b) order recall in STM is based on the level of activation of items within the relevant lexico-semantic long-term memory (LTM) network. However, verbal STM research has typically led to the conclusion that factors such as semantic category have a large effect on the number of correctly recalled items, but little or no impact on order recall (Poirier & Saint-Aubin, Quarterly Journal of Experimental Psychology 48A:384-404, 1995; Saint-Aubin, Ouellette, & Poirier, Psychonomic Bulletin & Review 12:171-177, 2005; Tse, Memory 17:874-891, 2009). Moreover, most formal models of short-term order memory currently suggest a separate mechanism for order coding-that is, one that is separate from item representation and not associated with LTM lexico-semantic networks. Both of the experiments reported here tested the predictions that we derived from Acheson et al. The findings show that, as predicted, manipulations aiming to affect the activation of item representations significantly impacted order memory.

Intrahemispheric theta rhythm desynchronization impairs working memory.

PubMed

Alekseichuk, Ivan; Pabel, Stefanie Corinna; Antal, Andrea; Paulus, Walter

2017-01-01

There is a growing interest in large-scale connectivity as one of the crucial factors in working memory. Correlative evidence has revealed the anatomical and electrophysiological players in the working memory network, but understanding of the effective role of their connectivity remains elusive. In this double-blind, placebo-controlled study we aimed to identify the causal role of theta phase connectivity in visual-spatial working memory. The frontoparietal network was over- or de-synchronized in the anterior-posterior direction by multi-electrode, 6 Hz transcranial alternating current stimulation (tACS). A decrease in memory performance and increase in reaction time was caused by frontoparietal intrahemispheric desynchronization. According to the diffusion drift model, this originated in a lower signal-to-noise ratio, known as the drift rate index, in the memory system. The EEG analysis revealed a corresponding decrease in phase connectivity between prefrontal and parietal areas after tACS-driven desynchronization. The over-synchronization did not result in any changes in either the behavioral or electrophysiological levels in healthy participants. Taken together, we demonstrate the feasibility of manipulating multi-site large-scale networks in humans, and the disruptive effect of frontoparietal desynchronization on theta phase connectivity and visual-spatial working memory.

Emotional state and local versus global spatial memory.

PubMed

Brunyé, Tad T; Mahoney, Caroline R; Augustyn, Jason S; Taylor, Holly A

2009-02-01

The present work investigated the effects of participant emotional state on global versus local memory for map-based information. Participants were placed into one of four emotion induction groups, crossing high and low arousal with positive and negative valence, or a control group. They then studied a university campus map and completed two memory tests, free recall and spatial statement verification. Converging evidence from these two tasks demonstrated that arousal amplifies symbolic distance effects and leads to a globally-focused spatial mental representation, partially at the expense of local knowledge. These results were found for both positively- and negatively-valenced affective states. The present study is the first investigation of emotional effects on spatial memory, and has implications for theories of emotion and spatial cognition.

Architecture for Multiple Interacting Robot Intelligences

NASA Technical Reports Server (NTRS)

Peters, Richard Alan, II (Inventor)

2008-01-01

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a database associative memory (DBAM) that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM.

Soft-error tolerance and energy consumption evaluation of embedded computer with magnetic random access memory in practical systems using computer simulations

NASA Astrophysics Data System (ADS)

Nebashi, Ryusuke; Sakimura, Noboru; Sugibayashi, Tadahiko

2017-08-01

We evaluated the soft-error tolerance and energy consumption of an embedded computer with magnetic random access memory (MRAM) using two computer simulators. One is a central processing unit (CPU) simulator of a typical embedded computer system. We simulated the radiation-induced single-event-upset (SEU) probability in a spin-transfer-torque MRAM cell and also the failure rate of a typical embedded computer due to its main memory SEU error. The other is a delay tolerant network (DTN) system simulator. It simulates the power dissipation of wireless sensor network nodes of the system using a revised CPU simulator and a network simulator. We demonstrated that the SEU effect on the embedded computer with 1 Gbit MRAM-based working memory is less than 1 failure in time (FIT). We also demonstrated that the energy consumption of the DTN sensor node with MRAM-based working memory can be reduced to 1/11. These results indicate that MRAM-based working memory enhances the disaster tolerance of embedded computers.

Gating of memory encoding of time-delayed cross-frequency MEG networks revealed by graph filtration based on persistent homology

PubMed Central

Hahm, Jarang; Lee, Hyekyoung; Park, Hyojin; Kang, Eunjoo; Kim, Yu Kyeong; Chung, Chun Kee; Kang, Hyejin; Lee, Dong Soo

2017-01-01

To explain gating of memory encoding, magnetoencephalography (MEG) was analyzed over multi-regional network of negative correlations between alpha band power during cue (cue-alpha) and gamma band power during item presentation (item-gamma) in Remember (R) and No-remember (NR) condition. Persistent homology with graph filtration on alpha-gamma correlation disclosed topological invariants to explain memory gating. Instruction compliance (R-hits minus NR-hits) was significantly related to negative coupling between the left superior occipital (cue-alpha) and the left dorsolateral superior frontal gyri (item-gamma) on permutation test, where the coupling was stronger in R than NR. In good memory performers (R-hits minus false alarm), the coupling was stronger in R than NR between the right posterior cingulate (cue-alpha) and the left fusiform gyri (item-gamma). Gating of memory encoding was dictated by inter-regional negative alpha-gamma coupling. Our graph filtration over MEG network revealed these inter-regional time-delayed cross-frequency connectivity serve gating of memory encoding. PMID:28169281

Task positive and default mode networks during a working memory in children with primary monosymptomatic nocturnal enuresis and healthy controls.

PubMed

Zhang, Kaihua; Ma, Jun; Lei, Du; Wang, Mengxing; Zhang, Jilei; Du, Xiaoxia

2015-10-01

Nocturnal enuresis is a common developmental disorder in children, and primary monosymptomatic nocturnal enuresis (PMNE) is the dominant subtype. This study investigated brain functional abnormalities that are specifically related to working memory in children with PMNE using function magnetic resonance imaging (fMRI) in combination with an n-back task. Twenty children with PMNE and 20 healthy children, group-matched for age and sex, participated in this experiment. Several brain regions exhibited reduced activation during the n-back task in children with PMNE, including the right precentral gyrus and the right inferior parietal lobule extending to the postcentral gyrus. Children with PMNE exhibited decreased cerebral activation in the task-positive network, increased task-related cerebral deactivation during a working memory task, and longer response times. Patients exhibited different brain response patterns to different levels of working memory and tended to compensate by greater default mode network deactivation to sustain normal working memory function. Our results suggest that children with PMNE have potential working memory dysfunction.

Memory recall and spike-frequency adaptation

NASA Astrophysics Data System (ADS)

Roach, James P.; Sander, Leonard M.; Zochowski, Michal R.

2016-05-01

The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

Memory Transmission in Small Groups and Large Networks: An Agent-Based Model.

PubMed

Luhmann, Christian C; Rajaram, Suparna

2015-12-01

The spread of social influence in large social networks has long been an interest of social scientists. In the domain of memory, collaborative memory experiments have illuminated cognitive mechanisms that allow information to be transmitted between interacting individuals, but these experiments have focused on small-scale social contexts. In the current study, we took a computational approach, circumventing the practical constraints of laboratory paradigms and providing novel results at scales unreachable by laboratory methodologies. Our model embodied theoretical knowledge derived from small-group experiments and replicated foundational results regarding collaborative inhibition and memory convergence in small groups. Ultimately, we investigated large-scale, realistic social networks and found that agents are influenced by the agents with which they interact, but we also found that agents are influenced by nonneighbors (i.e., the neighbors of their neighbors). The similarity between these results and the reports of behavioral transmission in large networks offers a major theoretical insight by linking behavioral transmission to the spread of information. © The Author(s) 2015.

Large memory capacity in chaotic artificial neural networks: a view of the anti-integrable limit.

PubMed

Lin, Wei; Chen, Guanrong

2009-08-01

In the literature, it was reported that the chaotic artificial neural network model with sinusoidal activation functions possesses a large memory capacity as well as a remarkable ability of retrieving the stored patterns, better than the conventional chaotic model with only monotonic activation functions such as sigmoidal functions. This paper, from the viewpoint of the anti-integrable limit, elucidates the mechanism inducing the superiority of the model with periodic activation functions that includes sinusoidal functions. Particularly, by virtue of the anti-integrable limit technique, this paper shows that any finite-dimensional neural network model with periodic activation functions and properly selected parameters has much more abundant chaotic dynamics that truly determine the model's memory capacity and pattern-retrieval ability. To some extent, this paper mathematically and numerically demonstrates that an appropriate choice of the activation functions and control scheme can lead to a large memory capacity and better pattern-retrieval ability of the artificial neural network models.

Granger causality analysis reveals distinct spatio-temporal connectivity patterns in motor and perceptual visuo-spatial working memory.

PubMed

Protopapa, Foteini; Siettos, Constantinos I; Evdokimidis, Ioannis; Smyrnis, Nikolaos

2014-01-01

We employed spectral Granger causality analysis on a full set of 56 electroencephalographic recordings acquired during the execution of either a 2D movement pointing or a perceptual (yes/no) change detection task with memory and non-memory conditions. On the basis of network characteristics across frequency bands, we provide evidence for the full dissociation of the corresponding cognitive processes. Movement-memory trial types exhibited higher degree nodes during the first 2 s of the delay period, mainly at central, left frontal and right-parietal areas. Change detection-memory trial types resulted in a three-peak temporal pattern of the total degree with higher degree nodes emerging mainly at central, right frontal, and occipital areas. Functional connectivity networks resulting from non-memory trial types were characterized by more sparse structures for both tasks. The movement-memory trial types encompassed an apparent coarse flow from frontal to parietal areas while the opposite flow from occipital, parietal to central and frontal areas was evident for the change detection-memory trial types. The differences among tasks and conditions were more profound in α (8-12 Hz) and β (12-30 Hz) and less in γ (30-45 Hz) band. Our results favor the hypothesis which considers spatial working memory as a by-product of specific mental processes that engages common brain areas under different network organizations.

Functional connectivity pattern during rest within the episodic memory network in association with episodic memory performance in bipolar disorder.

PubMed

Oertel-Knöchel, Viola; Reinke, Britta; Matura, Silke; Prvulovic, David; Linden, David E J; van de Ven, Vincent

2015-02-28

In this study, we sought to examine the intrinsic functional organization of the episodic memory network during rest in bipolar disorder (BD). The previous work suggests that deficits in intrinsic functional connectivity may account for impaired memory performance. We hypothesized that regions involved in episodic memory processing would reveal aberrant functional connectivity in patients with bipolar disorder. We examined 21 patients with BD and 21 healthy matched controls who underwent functional magnetic resonance imaging (fMRI) during a resting condition. We did a seed-based functional connectivity analysis (SBA), using the regions of the episodic memory network that showed a significantly different activation pattern during task-related fMRI as seeds. The functional connectivity scores (FC) were further correlated with episodic memory task performance. Our results revealed decreased FC scores within frontal areas and between frontal and temporal/hippocampal/limbic regions in BD patients in comparison with controls. We observed higher FC in BD patients compared with controls between frontal and limbic regions. The decrease in fronto-frontal functional connectivity in BD patients showed a significant positive association with episodic memory performance. The association between task-independent dysfunctional frontal-limbic FC and episodic memory performance may be relevant for current pathophysiological models of the disease. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Distinct Gamma-Band Components Reflect the Short-Term Memory Maintenance of Different Sound Lateralization Angles

PubMed Central

Heidegger, Tonio; Wibral, Michael; Altmann, Christian F.; Lutzenberger, Werner

2008-01-01

Oscillatory activity in human electro- or magnetoencephalogram has been related to cortical stimulus representations and their modulation by cognitive processes. Whereas previous work has focused on gamma-band activity (GBA) during attention or maintenance of representations, there is little evidence for GBA reflecting individual stimulus representations. The present study aimed at identifying stimulus-specific GBA components during auditory spatial short-term memory. A total of 28 adults were assigned to 1 of 2 groups who were presented with only right- or left-lateralized sounds, respectively. In each group, 2 sample stimuli were used which differed in their lateralization angles (15° or 45°) with respect to the midsagittal plane. Statistical probability mapping served to identify spectral amplitude differences between 15° versus 45° stimuli. Distinct GBA components were found for each sample stimulus in different sensors over parieto-occipital cortex contralateral to the side of stimulation peaking during the middle 200–300 ms of the delay phase. The differentiation between “preferred” and “nonpreferred” stimuli during the final 100 ms of the delay phase correlated with task performance. These findings suggest that the observed GBA components reflect the activity of distinct networks tuned to spatial sound features which contribute to the maintenance of task-relevant information in short-term memory. PMID:18252742

Activation of the Basolateral Amygdala Induces Long-Term Enhancement of Specific Memory Representations in the Cerebral Cortex

PubMed Central

Chavez, Candice M.; McGaugh, James L.; Weinberger, Norman M.

2013-01-01

The basolateral amygdala (BLA) modulates memory, particularly for arousing or emotional events, during post-training periods of consolidation. It strengthens memories whose substrates in part or whole are stored remotely, in structures such as the hippocampus, striatum and cerebral cortex. However, the mechanisms by which the BLA influences distant memory traces are unknown, largely because of the need for identifiable target mnemonic representations. Associative tuning plasticity in the primary auditory cortex (A1) constitutes a well-characterized candidate specific memory substrate that is ubiquitous across species, tasks and motivational states. When tone predicts reinforcement, the tuning of cells in A1 shifts toward or to the signal frequency within its tonotopic map, producing an over-representation of behaviorally important sounds. Tuning shifts have the cardinal attributes of forms of memory, including associativity, specificity, rapid induction, consolidation and long-term retention and are therefore likely memory representations. We hypothesized that the BLA strengthens memories by increasing their cortical representations. We recorded multiple unit activity from A1 of rats that received a single discrimination training session in which two tones (2.0 s) separated by 1.25 octaves were either paired with brief electrical stimulation (400 ms) of the BLA (CS+) or not (CS−). Frequency response areas generated by presenting a matrix of test tones (0.5–53.82 kHz, 0–70 dB) were obtained before training and daily for three weeks post-training. Tuning both at threshold and above threshold shifted predominantly toward the CS+ beginning on Day 1. Tuning shifts were maintained for the entire three weeks. Absolute threshold and bandwidth decreased, producing less enduring increases in sensitivity and selectivity. BLA-induced tuning shifts were associative, highly specific and long-lasting. We propose that the BLA strengthens memory for important experiences by increasing the number of neurons that come to best represent that event. Traumatic, intrusive memories might reflect abnormally extensive representational networks due to hyper-activity of the BLA consequent to the release of excessive amounts of stress hormones. PMID:23266792

Oscillations in Spurious States of the Associative Memory Model with Synaptic Depression

NASA Astrophysics Data System (ADS)

Murata, Shin; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

2014-12-01

The associative memory model is a typical neural network model that can store discretely distributed fixed-point attractors as memory patterns. When the network stores the memory patterns extensively, however, the model has other attractors besides the memory patterns. These attractors are called spurious memories. Both spurious states and memory states are in equilibrium, so there is little difference between their dynamics. Recent physiological experiments have shown that the short-term dynamic synapse called synaptic depression decreases its efficacy of transmission to postsynaptic neurons according to the activities of presynaptic neurons. Previous studies revealed that synaptic depression destabilizes the memory states when the number of memory patterns is finite. However, it is very difficult to study the dynamical properties of the spurious states if the number of memory patterns is proportional to the number of neurons. We investigate the effect of synaptic depression on spurious states by Monte Carlo simulation. The results demonstrate that synaptic depression does not affect the memory states but mainly destabilizes the spurious states and induces periodic oscillations.

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

DOE PAGES

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron; ...

2017-11-21

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation atmore » scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.« less

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

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

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation atmore » scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.« less

Image Mapping and Visual Attention on the Sensory Ego-Sphere

NASA Technical Reports Server (NTRS)

Fleming, Katherine Achim; Peters, Richard Alan, II

2012-01-01

The Sensory Ego-Sphere (SES) is a short-term memory for a robot in the form of an egocentric, tessellated, spherical, sensory-motor map of the robot s locale. Visual attention enables fast alignment of overlapping images without warping or position optimization, since an attentional point (AP) on the composite typically corresponds to one on each of the collocated regions in the images. Such alignment speeds analysis of the multiple images of the area. Compositing and attention were performed two ways and compared: (1) APs were computed directly on the composite and not on the full-resolution images until the time of retrieval; and (2) the attentional operator was applied to all incoming imagery. It was found that although the second method was slower, it produced consistent and, thereby, more useful APs. The SES is an integral part of a control system that will enable a robot to learn new behaviors based on its previous experiences, and that will enable it to recombine its known behaviors in such a way as to solve related, but novel, task problems with apparent creativity. The approach is to combine sensory-motor data association and dimensionality reduction to learn navigation and manipulation tasks as sequences of basic behaviors that can be implemented with a small set of closed-loop controllers. Over time, the aggregate of behaviors and their transition probabilities form a stochastic network. Then given a task, the robot finds a path in the network that leads from its current state to the goal. The SES provides a short-term memory for the cognitive functions of the robot, association of sensory and motor data via spatio-temporal coincidence, direction of the attention of the robot, navigation through spatial localization with respect to known or discovered landmarks, and structured data sharing between the robot and human team members, the individuals in multi-robot teams, or with a C3 center.

Robust image retrieval from noisy inputs using lattice associative memories

NASA Astrophysics Data System (ADS)

Urcid, Gonzalo; Nieves-V., José Angel; García-A., Anmi; Valdiviezo-N., Juan Carlos

2009-02-01

Lattice associative memories also known as morphological associative memories are fully connected feedforward neural networks with no hidden layers, whose computation at each node is carried out with lattice algebra operations. These networks are a relatively recent development in the field of associative memories that has proven to be an alternative way to work with sets of pattern pairs for which the storage and retrieval stages use minimax algebra. Different associative memory models have been proposed to cope with the problem of pattern recall under input degradations, such as occlusions or random noise, where input patterns can be composed of binary or real valued entries. In comparison to these and other artificial neural network memories, lattice algebra based memories display better performance for storage and recall capability; however, the computational techniques devised to achieve that purpose require additional processing or provide partial success when inputs are presented with undetermined noise levels. Robust retrieval capability of an associative memory model is usually expressed by a high percentage of perfect recalls from non-perfect input. The procedure described here uses noise masking defined by simple lattice operations together with appropriate metrics, such as the normalized mean squared error or signal to noise ratio, to boost the recall performance of either the min or max lattice auto-associative memories. Using a single lattice associative memory, illustrative examples are given that demonstrate the enhanced retrieval of correct gray-scale image associations from inputs corrupted with random noise.

Attractor neural networks with resource-efficient synaptic connectivity

NASA Astrophysics Data System (ADS)

Pehlevan, Cengiz; Sengupta, Anirvan

Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.

Complex-valued multistate associative memory with nonlinear multilevel functions for gray-level image reconstruction.

PubMed

Tanaka, Gouhei; Aihara, Kazuyuki

2009-09-01

A widely used complex-valued activation function for complex-valued multistate Hopfield networks is revealed to be essentially based on a multilevel step function. By replacing the multilevel step function with other multilevel characteristics, we present two alternative complex-valued activation functions. One is based on a multilevel sigmoid function, while the other on a characteristic of a multistate bifurcating neuron. Numerical experiments show that both modifications to the complex-valued activation function bring about improvements in network performance for a multistate associative memory. The advantage of the proposed networks over the complex-valued Hopfield networks with the multilevel step function is more outstanding when a complex-valued neuron represents a larger number of multivalued states. Further, the performance of the proposed networks in reconstructing noisy 256 gray-level images is demonstrated in comparison with other recent associative memories to clarify their advantages and disadvantages.

Analogue spin-orbit torque device for artificial-neural-network-based associative memory operation

NASA Astrophysics Data System (ADS)

Borders, William A.; Akima, Hisanao; Fukami, Shunsuke; Moriya, Satoshi; Kurihara, Shouta; Horio, Yoshihiko; Sato, Shigeo; Ohno, Hideo

2017-01-01

We demonstrate associative memory operations reminiscent of the brain using nonvolatile spintronics devices. Antiferromagnet-ferromagnet bilayer-based Hall devices, which show analogue-like spin-orbit torque switching under zero magnetic fields and behave as artificial synapses, are used. An artificial neural network is used to associate memorized patterns from their noisy versions. We develop a network consisting of a field-programmable gate array and 36 spin-orbit torque devices. An effect of learning on associative memory operations is successfully confirmed for several 3 × 3-block patterns. A discussion on the present approach for realizing spintronics-based artificial intelligence is given.

Spatial light modulators and applications III; Proceedings of the Meeting, San Diego, CA, Aug. 7, 8, 1989

NASA Astrophysics Data System (ADS)

Efron, Uzi

Recent advances in the technology and applications of spatial light modulators (SLMs) are discussed in review essays by leading experts. Topics addressed include materials for SLMs, SLM devices and device technology, applications to optical data processing, and applications to artificial neural networks. Particular attention is given to nonlinear optical polymers, liquid crystals, magnetooptic SLMs, multiple-quantum-well SLMs, deformable-mirror SLMs, three-dimensional optical memories, applications of photorefractive devices to optical computing, photonic neurocomputers and learning machines, holographic associative memories, SLMs as parallel memories for optoelectronic neural networks, and coherent-optics implementations of neural-network models.

Spatial light modulators and applications III; Proceedings of the Meeting, San Diego, CA, Aug. 7, 8, 1989

NASA Technical Reports Server (NTRS)

Efron, Uzi (Editor)

1990-01-01

Recent advances in the technology and applications of spatial light modulators (SLMs) are discussed in review essays by leading experts. Topics addressed include materials for SLMs, SLM devices and device technology, applications to optical data processing, and applications to artificial neural networks. Particular attention is given to nonlinear optical polymers, liquid crystals, magnetooptic SLMs, multiple-quantum-well SLMs, deformable-mirror SLMs, three-dimensional optical memories, applications of photorefractive devices to optical computing, photonic neurocomputers and learning machines, holographic associative memories, SLMs as parallel memories for optoelectronic neural networks, and coherent-optics implementations of neural-network models.

Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks

PubMed Central

Kato, Hideyuki; Ikeguchi, Tohru

2016-01-01

Specific memory might be stored in a subnetwork consisting of a small population of neurons. To select neurons involved in memory formation, neural competition might be essential. In this paper, we show that excitable neurons are competitive and organize into two assemblies in a recurrent network with spike timing-dependent synaptic plasticity (STDP) and axonal conduction delays. Neural competition is established by the cooperation of spontaneously induced neural oscillation, axonal conduction delays, and STDP. We also suggest that the competition mechanism in this paper is one of the basic functions required to organize memory-storing subnetworks into fine-scale cortical networks. PMID:26840529

Cognitive control, attention, and the other race effect in memory.

PubMed

Brown, Thackery I; Uncapher, Melina R; Chow, Tiffany E; Eberhardt, Jennifer L; Wagner, Anthony D

2017-01-01

People are better at remembering faces from their own race than other races-a phenomenon with significant societal implications. This Other Race Effect (ORE) in memory could arise from different attentional allocation to, and cognitive control over, same- and other-race faces during encoding. Deeper or more differentiated processing of same-race faces could yield more robust representations of same- vs. other-race faces that could support better recognition memory. Conversely, to the extent that other-race faces may be characterized by lower perceptual expertise, attention and cognitive control may be more important for successful encoding of robust, distinct representations of these stimuli. We tested a mechanistic model in which successful encoding of same- and other-race faces, indexed by subsequent memory performance, is differentially predicted by (a) engagement of frontoparietal networks subserving top-down attention and cognitive control, and (b) interactions between frontoparietal networks and fusiform cortex face processing. European American (EA) and African American (AA) participants underwent fMRI while intentionally encoding EA and AA faces, and ~24 hrs later performed an "old/new" recognition memory task. Univariate analyses revealed greater engagement of frontoparietal top-down attention and cognitive control networks during encoding for same- vs. other-race faces, stemming particularly from a failure to engage the cognitive control network during processing of other-race faces that were subsequently forgotten. Psychophysiological interaction (PPI) analyses further revealed that OREs were characterized by greater functional interaction between medial intraparietal sulcus, a component of the top-down attention network, and fusiform cortex during same- than other-race face encoding. Together, these results suggest that group-based face memory biases at least partially stem from differential allocation of cognitive control and top-down attention during encoding, such that same-race memory benefits from elevated top-down attentional engagement with face processing regions; conversely, reduced recruitment of cognitive control circuitry appears more predictive of memory failure when encoding out-group faces.

Cognitive control, attention, and the other race effect in memory

PubMed Central

Uncapher, Melina R.; Chow, Tiffany E.; Eberhardt, Jennifer L.; Wagner, Anthony D.

2017-01-01

People are better at remembering faces from their own race than other races–a phenomenon with significant societal implications. This Other Race Effect (ORE) in memory could arise from different attentional allocation to, and cognitive control over, same- and other-race faces during encoding. Deeper or more differentiated processing of same-race faces could yield more robust representations of same- vs. other-race faces that could support better recognition memory. Conversely, to the extent that other-race faces may be characterized by lower perceptual expertise, attention and cognitive control may be more important for successful encoding of robust, distinct representations of these stimuli. We tested a mechanistic model in which successful encoding of same- and other-race faces, indexed by subsequent memory performance, is differentially predicted by (a) engagement of frontoparietal networks subserving top-down attention and cognitive control, and (b) interactions between frontoparietal networks and fusiform cortex face processing. European American (EA) and African American (AA) participants underwent fMRI while intentionally encoding EA and AA faces, and ~24 hrs later performed an “old/new” recognition memory task. Univariate analyses revealed greater engagement of frontoparietal top-down attention and cognitive control networks during encoding for same- vs. other-race faces, stemming particularly from a failure to engage the cognitive control network during processing of other-race faces that were subsequently forgotten. Psychophysiological interaction (PPI) analyses further revealed that OREs were characterized by greater functional interaction between medial intraparietal sulcus, a component of the top-down attention network, and fusiform cortex during same- than other-race face encoding. Together, these results suggest that group-based face memory biases at least partially stem from differential allocation of cognitive control and top-down attention during encoding, such that same-race memory benefits from elevated top-down attentional engagement with face processing regions; conversely, reduced recruitment of cognitive control circuitry appears more predictive of memory failure when encoding out-group faces. PMID:28282414

Mapping the connectivity underlying multimodal (verbal and non-verbal) semantic processing: a brain electrostimulation study.

PubMed

Moritz-Gasser, Sylvie; Herbet, Guillaume; Duffau, Hugues

2013-08-01

Accessing the meaning of words, objects, people and facts is a human ability, made possible thanks to semantic processing. Although studies concerning its cortical organization are proficient, the subcortical connectivity underlying this semantic network received less attention. We used intraoperative direct electrostimulation, which mimics a transient virtual lesion during brain surgery for glioma in eight awaken patients, to map the anatomical white matter substrate subserving the semantic system. Patients performed a picture naming task and a non-verbal semantic association test during the electrical mapping. Direct electrostimulation of the inferior fronto-occipital fascicle, a poorly known ventral association pathway which runs throughout the brain, induced in all cases semantic disturbances. These transient disorders were highly reproducible, and concerned verbal as well as non-verbal output. Our results highlight for the first time the essential role of the left inferior fronto-occipital fascicle in multimodal (and not only in verbal) semantic processing. On the basis of these original findings, and in the lights of phylogenetic considerations regarding this fascicle, we suggest its possible implication in the monitoring of the human level of consciousness related to semantic memory, namely noetic consciousness. Copyright © 2013 Elsevier Ltd. All rights reserved.

Programmable Analog Memory Resistors For Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Ramesham, Rajeshuni; Thakoor, Sarita; Daud, Taher; Thakoor, Anilkumar P.

1990-01-01

Electrical resistance of new solid-state device altered repeatedly by suitable control signals, yet remains at steady value when control signal removed. Resistance set at low value ("on" state), high value ("off" state), or at any convenient intermediate value and left there until new value desired. Circuits of this type particularly useful in nonvolatile, associative electronic memories based on models of neural networks. Such programmable analog memory resistors ideally suited as synaptic interconnects in "self-learning" neural nets. Operation of device depends on electrochromic property of WO3, which when pure is insulator. Potential uses include nonvolatile, erasable, electronically programmable read-only memories.

fastBMA: scalable network inference and transitive reduction.

PubMed

Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E; Yeung, Ka Yee

2017-10-01

Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). © The Authors 2017. Published by Oxford University Press.

Social Network Mapping: A New Tool For The Leadership Toolbox

DTIC Science & Technology

2002-04-01

SOCIAL NETWORK MAPPING: A NEW TOOL FOR THE LEADERSHIP TOOLBOX By Elisabeth J. Strines, Colonel, USAF 8037 Washington Road Alexandria...valid OMB control number. 1. REPORT DATE 00 APR 2002 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Social Network Mapping: A...describes the concept of social network mapping and demonstrates how it can be used by squadron commanders and leaders at all levels to provide subtle

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

[Advances in Acupuncture Mechanism Research on the Changes of Synaptic Plasticity: "Pain Memory" for Chronic Pain].

PubMed

Yang, Yi-Ling; Huang, Jian-Peng; Jiang, Li; Liu, Jian-Hua

2017-12-25

Previous studies have shown that there are many common structures between the neural network of pain and memory, and the main structure in the pain network is also part of the memory network. Chronic pain is characterized by recurrent attacks and is associated with persistent ectopic impulse, which causes changes in synaptic structure and function based on nerve activity. These changes may induce long-term potentiation of synaptic transmission, and ultimately lead to changes in the central nervous system to produce "pain memory". Acupuncture is an effective method in treating chronic pain. It has been proven that acupuncture can affect the spinal cord dorsal horn, hippocampus, cingulate gyrus and other related areas. The possible mechanisms of action include opioid-induced analgesia, activation of glial cells, and the expression of brain derived neurotrophic factor (BDNF). In this study, we systematically review the brain structures, stage of "pain memory" and the mechanisms of acupuncture on synaptic plasticity in chronic pain.

Structural correlates of impaired working memory in hippocampal sclerosis.

PubMed

Winston, Gavin P; Stretton, Jason; Sidhu, Meneka K; Symms, Mark R; Thompson, Pamela J; Duncan, John S

2013-07-01

Temporal lobe epilepsy (TLE) has been considered to impair long-term memory, whilst not affecting working memory, but recent evidence suggests that working memory is compromised. Functional MRI (fMRI) studies demonstrate that working memory involves a bilateral frontoparietal network the activation of which is disrupted in hippocampal sclerosis (HS). A specific role of the hippocampus to deactivate during working memory has been proposed with this mechanism faulty in patients with HS. Structural correlates of disrupted working memory in HS have not been explored. We studied 54 individuals with medically refractory TLE and unilateral HS (29 left) and 28 healthy controls. Subjects underwent 3T structural MRI, a visuospatial n-back fMRI paradigm and diffusion tensor imaging (DTI). Working memory capacity assessed by three span tasks (digit span backwards, gesture span, motor sequences) was combined with performance in the visuospatial paradigm to give a global working memory measure. Gray and white matter changes were investigated using voxel-based morphometry and voxel-based analysis of DTI, respectively. Individuals with left or right HS performed less well than healthy controls on all measures of working memory. fMRI demonstrated a bilateral frontoparietal network during the working memory task with reduced activation of the right parietal lobe in both patient groups. In left HS, gray matter loss was seen in the ipsilateral hippocampus and parietal lobe, with maintenance of the gray matter volume of the contralateral parietal lobe associated with better performance. White matter integrity within the frontoparietal network, in particular the superior longitudinal fasciculus and cingulum, and the contralateral temporal lobe, was associated with working memory performance. In right HS, gray matter loss was also seen in the ipsilateral hippocampus and parietal lobe. Working memory performance correlated with the gray matter volume of both frontal lobes and white matter integrity within the frontoparietal network and contralateral temporal lobe. Our data provide further evidence that working memory is disrupted in HS and impaired integrity of both gray and white matter is seen in functionally relevant areas. We suggest this forms the structural basis of the impairment of working memory, indicating widespread and functionally significant structural changes in patients with apparently isolated HS. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.