A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing large-scale properties of short-term 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 short-term memory. Using the numerical algorithm of Very Fast Simulated Re-Annealing, it is demonstrated that SMNI can indeed fit this data within experimentally observed ranges of its underlying neuronal-synaptic parameters, and use the quantitative modeling results to examine physical neocortical mechanisms to discriminate between high-risk and low-risk 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

We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as hetero-associative memories. The framework is applicable to non-recursive, fully-connected systems with binary (0,1) Hebbian weights and hard-limited threshold. It can handle both full and partial matching of single or multiple data items in non-square 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 medium-sized memories and used to aid the design of larger systems. The results highlight the Matching Performance of CMMs 2 fact that correlation-based models can act as highly efficient memories provided a small probability of retrieval error is accepted. Keywords Neural Associative Memories, Co...

: 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, gamma-oscillations, 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...

Associative storage and retrieval of binary random patterns in various neural net models with one-step threshold-detection retrieval and local learning rules are the subject of this paper. For different heteroassociation and auto-association 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 signal-to-noise 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 auto-asso...

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.

Within the last decade there has been a considerable amount of interest in pseudo-inverse autoassociative neural networks (PINNs), which are networks designed with the pseudo-inverse 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 self-connections is taken into account. The fact that self-connections 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...

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

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 low-end videocameras and little computational power. In doing that we use the public domain associative memory code.

We study the diversity of complex spatio-temporal 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 limit-cycles Center for Biological Sequence Analysis, The Technical University of Denmark, Building 206, DK-2800 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...

The fact that reducing self-connections improves the performance of the autoassociative networks built by the pseudo-inverse learning rule is known already for quite a while, but is not studied completely yet. In particular, it is known that decreasing of self-connection increases the direct attraction radius of the network, but it also known that it increases the number of spurious dynamic attractors. Thus, it has been concluded that the optimal value of the coefficient of self-connection reduction D lies somewhere in the range (0;0.5). This paper gives an explicit answer to the question what is the optimal value of the self-connection reduction. It shows how the indirect attraction radius increases with the decrease of D. The summary of the results pertaining to the phenomenon is presented. I. Autoassociative Neural Networks An autoassociative neural network (AANN) is defined as a self-organizing network of N mutually interconnected two-state neurons [3, 30, 23], the evolution of ...