: 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...

For the recognition of objects there are a number of computational requirements that go beyond the detection of simple geometric features like oriented lines. When there are several partially occluded objects present in a visual scene one has to have an internal knowledge about the object to be identified, e.g. using associative memories.

Ever since the discovery of neural networks, there has been a controversy between two modes of information processing. On the one hand, symbolic systems have proven indispensable for our understanding of higher intelligence, especially when cognitive domains like language and reasoning are examined. On the other hand, it is a matter of fact that intelligence resides in the brain, where computation appears to be organized by numerical and statistical principles and where a parallel distributed architecture is appropriate. The present claim is in line with researchers like Paul Smolensky and Peter Gärdenfors and suggests that this controversy can be resolved by a unified theory of cognition – one that integrates both aspects of cognition and assigns the proper roles to symbolic computation and numerical neural computation. The overall goal in this contribution is to discuss formal systems that are suitable for grounding the formal basis for such a unified theory. It is suggested that the instruments of modern logic and model theoretic semantics are appropriate for analyzing certain aspects of dynamical systems like inferring and learning in neural networks. Hence, I suggest that an active dialogue between the traditional symbolic approaches to logic, information and language and the connectionist paradigm is possible and fruitful. An essential component of this dialogue refers to Optimality Theory (OT) – taken as a theory that likewise aims to overcome the gap between symbolic and neuronal systems. In the light of the proposed logical analysis notions like recoverability and bidirection are explained, and likewise the problem of founding a strict constraint hierarchy is discussed. Moreover, a claim is made for developing an “embodied ” OT closing the gap between symbolic representation and embodied cognition. 1

This thesis starts from the assumption that individual neuronal action potentials (spikes) have computational and dynamical significance. Two of the types of activity that networks of spiking neurons can engage in are sequences and synchrony. The first part of the work reviews the role spikes, sequences and synchrony play in coding, dynamics and learning in the nervous system and models of the nervous system. Models of spiking neurons, especially the spike response model (SRM), feature strongly, as do synfire chains, a form of spatiotemporal sequence. A methodology chapter

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