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Statistical mechanics of neocortical interactions: A scaling paradigm applied to electroencephalography
 PHYS. REV. A
, 1991
"... A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single neuron lev ..."
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Cited by 47 (41 self)
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A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing largescale properties of shortterm memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. In this paper, a more stringent test is placed on SMNI: The algebraic and numerical algorithms previously developed in this and similar systems are brought to bear to fit large sets of EEG and evoked potential data being collected to investigate genetic predispositions to alcoholism and to extract brain “signatures” of shortterm memory. Using the numerical algorithm of Very Fast Simulated ReAnnealing, it is demonstrated that SMNI can indeed fit this data within experimentally observed ranges of its underlying neuronalsynaptic parameters, and use the quantitative modeling results to examine physical neocortical mechanisms to discriminate between highrisk and lowrisk populations genetically predisposed to alcoholism. Since this first study is a control to span relatively long time epochs, similar to earlier attempts to establish such correlations, this discrimination is inconclusive because of other neuronal activity which can mask such effects. However, the SMNI model is shown to be consistent
Cell Assemblies, Associative Memory and Temporal Structure in Brain Signals
"... : In this work we discuss Hebb's old ideas about cell assemblies in the light of recent results concerning temporal structure and correlations in neural signals. We want to give a conceptual, necessarily only rough picture, how ideas about `binding by synchronisation', `synfire chains', `local and g ..."
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Cited by 19 (7 self)
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: In this work we discuss Hebb's old ideas about cell assemblies in the light of recent results concerning temporal structure and correlations in neural signals. We want to give a conceptual, necessarily only rough picture, how ideas about `binding by synchronisation', `synfire chains', `local and global assemblies', `short and long term memory' and `behaviour' might be integrated into a coherent model of brain functioning based on neuronal assemblies. Keywords: cell assemblies, synchronization, gammaoscillations, synfire chains, memory, behaviour 1 ASSEMBLIES AND ASSOCIATIVE MEMORIES 1.1 Cell Assemblies Cell assemblies have been introduced by Donald Hebb with the intention of providing a functional and at the same time structural model for cortical processes and neuronal representations of external events (Hebb, 1949). According to Hebb's ideas, stimuli, objects, things, but also more abstract entities like concepts, contextual relations, ideas, and so on are thought of being repre...
Matching Performance of Binary Correlation Matrix Memories
"... We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as heteroassociative memories. The framework is applicable to nonrecursive, fullyconnected systems with binary (0,1) Hebbian weights and hardlimited threshold. It can handle both fu ..."
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Cited by 18 (12 self)
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We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as heteroassociative memories. The framework is applicable to nonrecursive, fullyconnected systems with binary (0,1) Hebbian weights and hardlimited threshold. It can handle both full and partial matching of single or multiple data items in nonsquare memories. Theoretical development takes place under a probability theory framework. Inherent uncertainties in the matching process are accommodated by the use of probability distributions to describe the numbers of correct and incorrect neuron responses during retrieval. Theoretical predictions are verified experimentally for mediumsized memories and used to aid the design of larger systems. The results highlight the Matching Performance of CMMs 2 fact that correlationbased models can act as highly efficient memories provided a small probability of retrieval error is accepted. Keywords Neural Associative Memories, Co...
Associative Data Storage and Retrieval in Neural Networks
, 1995
"... Associative storage and retrieval of binary random patterns in various neural net models with onestep thresholddetection retrieval and local learning rules are the subject of this paper. For different heteroassociation and autoassociation memory tasks, specified by the properties of the pattern s ..."
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Cited by 9 (6 self)
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Associative storage and retrieval of binary random patterns in various neural net models with onestep thresholddetection retrieval and local learning rules are the subject of this paper. For different heteroassociation and autoassociation memory tasks, specified by the properties of the pattern sets to be stored and upper bounds on the retrieval errors, we compare the performance of various models of finite as well as asymptotically infinite size. In infinite models, we consider the case of asymptotically sparse patterns, where the mean activity in a pattern vanishes, and study two asymptotic fidelity requirements: constant error probabilities and vanishing error probabilities. A signaltonoise ratio analysis is carried out for one retrieval step where the calculations are comparatively straightforward and easy. As performance measures we propose and evaluate information capacities in bits/synapse which also take into account the important property of fault tolerance. For autoasso...
The Storage Capacity of Forgetful Neural Networks
, 1995
"... In this report, we derive a two stage algorithm to evaluate the storage ca pacity of a forgetful neural network using any smooth learning scheme. ..."
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Cited by 6 (1 self)
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In this report, we derive a two stage algorithm to evaluate the storage ca pacity of a forgetful neural network using any smooth learning scheme.
The Influence of SelfConnection on the Performance of PseudoInverse Autoassociative Networks
, 2001
"... Within the last decade there has been a considerable amount of interest in pseudoinverse autoassociative neural networks (PINNs), which are networks designed with the pseudoinverse learning rule. This interest is attributed to their high capacity and retrieval capability: the limit of 0.5N for the ..."
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Cited by 6 (3 self)
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Within the last decade there has been a considerable amount of interest in pseudoinverse autoassociative neural networks (PINNs), which are networks designed with the pseudoinverse learning rule. This interest is attributed to their high capacity and retrieval capability: the limit of 0.5N for the associative capacity (N being the number of neurons), obtained by Personnaz et al. and Kanter and Sompolinsky [1], [2], is probably the most referred to result concerning the networks. Recently though, it has been shown that the network capacity is higher than 0.5N when the reduction of selfconnections is taken into account. The fact that selfconnections affect the performance of the networks has been observed by many researchers, but no rigorous investigation of this phenomenon seems to have been done. In the paper we summarize the results obtained on the phenomenon and show that by partially reducing selfconnections the capacity of the PINN can be increased up to 0.8N , with the attract...
Models of distributed associative memory networks in the brain. Theory in Biosciences 122:55–69. [FTS
 Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society
, 2003
"... Although experimental evidence for distributed cell assemblies is growing, theories of cell assemblies are still marginalized in theoretical neuroscience. We argue that this has to do with shortcomings of the currently best understood assembly theories, the ones based on formal associative memory mo ..."
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Cited by 4 (0 self)
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Although experimental evidence for distributed cell assemblies is growing, theories of cell assemblies are still marginalized in theoretical neuroscience. We argue that this has to do with shortcomings of the currently best understood assembly theories, the ones based on formal associative memory models. These only insufficiently reflect anatomical and physiological properties of nervous tissue and their functionality is too restricted to provide a framework for cognitive modeling. We describe cell assembly models that integrate more neurobiological constraints and review results from simulations of a simple nonlocal associative network formed by a reciprocal topographic projection. Impacts of nonlocal associative projections in the brain are discussed
Using Associative Memory Principles to Enhance Perceptual Ability of Vision Systems
 in Proc. of CVPR Workshop on Face Processing in Video (FPIV’04), Washington DC
, 2004
"... The so called associative thinking, which humans are known to perform on every day basis, is attributed to the fact that human brain memorizes information using the dynamical system made of interconnected neurons. Retrieval of information in such a system is accomplished in associative sense; starti ..."
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Cited by 4 (0 self)
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The so called associative thinking, which humans are known to perform on every day basis, is attributed to the fact that human brain memorizes information using the dynamical system made of interconnected neurons. Retrieval of information in such a system is accomplished in associative sense; starting from an arbitrary state, which might be an encoded representation of a visual image, the brain activity converges to another state, which is stable and which is what the brain remembers. In this paper we explore the possibility of using an associative memory for the purpose of enhancing the interactive capability of perceptual vision systems. By following the biological memory principles, we show how vision systems can be designed to recognize faces, facial gestures and orientations, using lowend videocameras and little computational power. In doing that we use the public domain associative memory code.
Threshold Noise As A Source Of Volatility In Random Synchronous Asymmetric Neural Networks
, 2000
"... We study the diversity of complex spatiotemporal patterns of random synchronous asymmetric neural networks (RSANNs). Specifically, we investigate the impact of noisy thresholds on network performance and find that there is an interesting region of noise parameters where RSANNs display specific feat ..."
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Cited by 3 (1 self)
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We study the diversity of complex spatiotemporal patterns of random synchronous asymmetric neural networks (RSANNs). Specifically, we investigate the impact of noisy thresholds on network performance and find that there is an interesting region of noise parameters where RSANNs display specific features of behavior desired for rapidly responding processing systems: accessibility to a large set of distinct, complex patterns. 1 Introduction Random Synchronous Asymmetric Neural Networks (RSANNs) with fixed synaptic coupling strengths and fixed neuronal thresholds have been found to have access to a very limited set of different limitcycles Center for Biological Sequence Analysis, The Technical University of Denmark, Building 206, DK2800 Lyngby, Denmark, EMAIL: hbohr@cbs.dtu.dk y Center for Astronomical Adaptive Optics, Steward Observatory, University of Arizona, Tucson AZ 85721, EMAIL: mcguire@as.arizona.edu z EMAIL: chris@physics.arizona.edu x EMAIL: rafelski@physics.arizona.e...
Columnar EEG magnetic influences on molecular development of shortterm memory
"... For sev eral decades the stated Holy Grail of chemical, biological and biophysical research into neocortical information processing has been to reduce such neocortical phenomena into specific bottomup molecular and smallerscale processes. Over the past three decades, with regard to shortterm memo ..."
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Cited by 3 (3 self)
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For sev eral decades the stated Holy Grail of chemical, biological and biophysical research into neocortical information processing has been to reduce such neocortical phenomena into specific bottomup molecular and smallerscale processes. Over the past three decades, with regard to shortterm memory (STM) and longterm memory (LTM) phenomena, which themselves are likely components of other phenomena like attention and consciousness, a statistical mechanics of neocortical interactions (SMNI) approach has yielded specific details of STM capacity, duration and stability not present in molecular approaches, but it is clear that most molecular approaches consider it inevitable that their reductionist approaches at molecular and possibly even quantum scales will yet prove to becausal explanations of such phenomena. The SMNI approach is a bottomup aggregation from synaptic scales to columnar and regional scales of neocortex, and has been merged with larger noninvasive EEG scales with other colleagues all at scales much coarser than molecular scales. As with many Crusades for some truths, other truths can be trampled. It is proposed that an SMNI vector potential (SMNIVP) constructed from magnetic fields induced by neuronal electrical firings, at thresholds of collective minicolumnar activity with laminar specification, can give rise to causal topdown mechanisms that effect molecular excitatory and inhibitory processes in STM and LTM. A specific example might be causal influences on momentum p of Ca 2+ ions by the SMNIVP A, ascalculated by the canonical momentum q, q = p − eA, where e is the electron coulomb charge, which may be applied either classically or quantummechanically. Such a smoking gun for topdown effects awaits forensic in vivo experimental verification, requiring appreciating the necessity and due diligence of including true multiplescale interactions across orders of magnitude in the complex neocortical environment.