this paper is to present such a reformulation. The cell assembly provides the cognitive system with flexibility far beyond the simple activation of concepts. Instead of viewing the assembly as simply active or latent we see the activation of the assembly as coming in a series of phases. Each phase of activity serves a different purpose, giving the theory the power and flexibility to handle a wide range of psychological data.

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs ” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous Chinese Room argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital

Variable binding is a difficult problem for neural networks. Two new mechanisms for binding by synaptic change are presented, and in both, bindings are erased and can be reused. The first is based on the commonly used learning mechanism of permanent change of synaptic weight, and the second on synaptic change which decays. Both are biologically motivated models. Simulations of binding on a paired association task are shown with the first mechanism succeeding with a 97.5 % F-Score, and the second performing perfectly. Further simulations show that binding by decaying synaptic change copes with cross talk, and can be used for compositional semantics. It can be inferred that binding by permanent change accounts for these, but it faces the stability plasticity dilemma. Two other existing binding mechanism, synchrony and active links, are compatible with these new mechanisms. All four mechanisms are compared and integrated in a Cell Assembly theory. 1 1

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

Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated action, and development. Particular attention is given to abstract concepts, language, and computational mechanisms.

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statistics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement recording systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The storage and reactivation of perceptual symbols operates at the level of perceptual components -- not at the level of holistic perceptual experiences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a common frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspection (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and abstract concepts, thereby implementing a fully functional conceptual sy...

A neural cognitive architecture would be an architecture based on simulated neurons, that provided a set of mechanisms for all cognitive behaviour. Moreover, this would be compatible with biological neural behaviour. As a result, such architectures can both form the basis of a fully-fledged AI and help to explain how cognition emerges from a collection of neurons in the human brain. The development of such a neural cognitive architecture is in its infancy, but a protoarchitecture in the form of behaving agents entirely based on simulated neurons is described. These agents take natural language commands, view the environment, plan and act. The development of these agents has led to a series of questions that need to be addressed to advance the development of neural cognitive architectures. These questions include long posed ones where progress has been made, such as the binding and symbol grounding problems; issues about biological architectures including neural models and brain topology; issues of emergent behaviour such as short and long-term Cell Assembly dynamics; and issues of learning such as the stability-plasticity dilemma. These questions can act as a road map for the development of neural cognitive architectures and AIs based on them.

Self-organization is one of the few theories that can explain significant aspects of developmental neuroscience. Within the brain itself, various spatially organized regions, or maps, exist that emerge dynamically. Theories and models that use self-organization have been successful at explaining such phenomena, and while these are not conclusive proof, they provide strong evidence in favor of self-organized mechanisms in the brain. Artificial Neural Networks have been developed that make use of these models to produce pattern recognition and classification mechanisms that have been used in widely diverse fields. This paper describes some of the models used to explain the emergence of various patterns and maps in the brain and their counterparts in the Neural Network domain. Widely used Neural Network algorithms include the Self-Organized Map and Adaptive Resonance Theory, that are discussed herein.