This article examines the relationship between environmental and cognitive structure. One of

Abstract. There are many examples of intelligent and learning systems that are based either on the connectionist or the symbolic approach. Although the latter can be successfully combined with statistical learning to create a hybrid system, it is not so clear how symbolic processing can emerge from a connectionst system. Human mind is a living proof that such a transition must be possible. Inspired by biological cognition, our project explores the ways symbolic processing can emerge in a system of neural cell–assemblies (CAs). Here, we present the meta–process that regulates learning of associations between the CAs. The process is compared with the stochastic learning theory, and its outcome is a set of optimal rules. The paper concludes by an example of a working system and the discussion of it biological plausibility. 1

Abstract—Perception, prediction and generation of sequences is a fundamental aspect of human behavior and depends on the ability to detect serial order. This paper presents a plausible model of sequential memory at the neurological level based on the theory of cell assemblies. The basic idea is that sequences in the brain are represented by cell assemblies. Each item of the sequence and the sequential association between the items are represented by cell assemblies. Simulation results show that the model is capable of recognizing and discriminating multiple sequences stored in memory. The cell assemblies that represent the sequential association between two items are activated if these items occur in the input in the correct order. These sequence detecting cell assemblies form the basis of this model. A simulation presenting 100 stored sequences and 100 not stored recognizes perfectly 90 % of the time with no false positives.

An integrated representation of large-scale space, or cognitive map, called PLAN is presented that attempts to address a broader spectrum of issues than has previously been attempted in a single model. Rather than examining wayfinding as a process separate from the rest of cognition, one of the fundamental goals of this work is to examine how the wayfinding process is integrated into general cognition. One result of this approach is that the model is "heads-up", or scene-based, because it takes advantages of the properties of the human visual system and particularly the visual system's split into two pathways. The emphasis on the human location or "where " system is new to cognitive mapping and is part of an attempt to synthesize prototype theory, associative networks and location together in a connectionist system. Not all of PLAN is new, however. Many of its parts have analogues in one or another pre-existing theory. What makes PLAN unique is the integration the various components into a coherent whole, and the capacity of this resulting system to speak to a wide range of constraints. Our approach emphasizes adaptiveness; thus our focus on such issues such as the ease of use and the efficiency of learning. The

This paper presents a novel connectionist architecture to handle the "sensitivity-stability" problem and, in particular, an extreme manifestation of the problem, catastrophic interference. This architecture, called an interactive tandem-network (ITN) architecture, consists of two continually interacting networks, one --- the LTM network --- dynamically storing "prototypes" of the patterns learned, the other --- the STM network --- being responsible for "short-term" learning of new patterns. Prototypes stored in the LTM network influence hidden-layer representations in the STM network and, conversely, newly learned representations in the STM network gradually modify the more stable LTM prototypes. As prototypes are learned by the LTM network, they are dynamically constrained to maximize mutual orthogonality. This system of tandem networks performs particularly well on the problem of catastrophic interference. It also produces "long-term" representations that are stable ...

ymbolic AI had many problems with real language processing. Statistical methods, though more effective, also led to problems. This led to interaction with an interdisciplinary group, SESAME, at the University of Michigan headed by Steve Kaplan. This group attacks the problem of understanding human behaviour on multiple levels including, but not limited to, the neural, psychological and evolutionary levels. This group is a source of information and inspiration for Huyck and he remains in close contact with this group via email and occasional visits. After his PhD, Huyck went to the American University in Cairo for 18 months as a lecturer. While there, he published work on Arabic NLP [8, 10], and started preliminary work on an implementation of a Cell Assembly model. Huyck then moved to the University of Sheffield as a research fellow. There he continued his NLP work [4], and did some work on NLP evaluation [3]. Upon arriving at Middlesex University, Huyck began to focus on the Cell A

A connectionist architecture comprised of cell assemblies was developed and applied to the problem of speech perception at the phonemic and lexical levels. The problem addressed involved a disagreement amongst theorists over the possible sources of lexical priming effects. Speech was encoded in the model as the temporal activity of phoneme units that are connected to higher-level word assemblies. The lexical layer was topographically organized based upon similarity of phonemic structure. Lateral inhibition at the lexical level was shown to be both necessary and sufficient to support results from phonological priming experiments involving human participants.

In recall tasks, increased levels of arousal soon after presentation time leads to short-term performance that is contradictory to standard memory models. Despite the fact that long-term recall is excellent in such situations, short-term recall is poor, worse than in the long-term case. This article presents a model, based upon Hebb's cell assembly construct, to account for this puzzling data.

Abstract: Cell assemblies (CAs) were posited by Hebb almost 60 years ago as the unit of representation in the brain. Recent results in the field of neuroscience indicate that CAs are likely to exist, at least in the mammalian brain. The CABot project uses simulations of CAs formed from individual neurons as a basis for learning and behaviour. This paper proves that a network of CAs, as described by Hebb and as implemented in CABot, is complete with respect to structured program theory. It follows that such a network is capable of executing any procedure that can be written as an algorithm. * Manuscript Click here to view linked References

Abstract—Hebb considered the cell assembly to represent a concept in the brain and thus to be an underlying construct of human thought. He proposed that the cell assembly is a connected group of neurons whose pattern of firing is such that a reverberatory activity persists after the removal of a stimulus. Once a cell assembly is activated something must eventually cause it to decay. Clearly thoughts have to be extinguished to make way for others, the question is how. Various suggestions have been made concerning mechanisms that could cause an assembly to decay in the long term including inhibition by other assemblies and passive fatigue. In this paper two classes of models are used to implement this decay, the first is based on building cell assemblies with specific weights and connections that have a linear decay. The second class is based on manipulating variables within a cell assembly model, creating long term fatigue or activation decay. This class of models may be more biologically plausible than the first, and can produce the expected temporal dynamics in the presence of an ambiguous stimulus. However neither class can yet produce the correct prolongation of activation when the stimulus is re-presented. I.