Abstract: Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the results about the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuron-like elements represent a large body of systematic knowledge and perform a range of inferences with such speed? We describe a computational model that is a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables, and perform a class of inferences in a few hundred msec. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model achieves this by i) representing dynamic bindings as the synchronous firing of appropriate nodes, ii) rules as interconnection patterns

Many researchers interested in connectionist models accept that such models are "neurally inspired " but do not worry too much about whether their models are biologically realistic. While such a position may be perfectly justifiable, the present paper attempts to illustrate how biological information can be used to constrain connectionist models. Two particular areas are discussed. The first section deals with visual information processing in the primate and human visual system. It is argued that speed with which visual information is processed imposes major constraints on the architecture and operation of the visual system. In particular, it seems that a great deal of processing must depend on a single bottum-up pass. The second section deals with biological aspects of learning algorithms. It is argued that although there is good evidence for certain coactivation related synaptic modification schemes, other learning mechanisms, including back-propagation, are not currently supported by experimental data.

Information in neuronal networks may be represented by the spatiotemporal patterns of spikes. Here we examined the temporal coordination of pyramidal cell spikes in the rat hippocampus during slow-wave sleep. In addition, rats were trained to run in a defined position in space (running wheel) to activate a selected group of pyramidal cells. A template-matching method and a joint probability map method were used for sequence search. Repeating spike sequences in excess of chance occurrence were examined by comparing the number of repeating sequences in the original spike trains and in surrogate trains after Monte Carlo shuffling of the spikes. Four different shuffling procedures were used to control for the population dynamics of Although it is a widely accepted notion that information is distributed in cell assemblies rather than encoded by single cells, the nature of coding in cell assembly has remained a major challenge for neuroscience research. Several explanations have been proposed on theoretical grounds, including frequency coding

We investigate the phase-space dynamics of a general model of local systems of biological neurons in order to deduce the salient dynamical characteristics of such systems. In this article, we present a detailed exposition of an abstract dynamical system that models systems of biological neurons. The abstract system is based on a limited set of realistic assumptions and thus accommodates a wide range of neuronal models. Simulation results are presented for several instantiations of the abstract system, each modeling a typical neocortical column to a different degree of accuracy. The results demonstrate that the dynamics of the systems are generally consistent with that observed in neurophysiological experiments. They reveal that the qualitative behavior of the class of systems can be classified into three distinct categories: quiescence, intense periodic activity resembling a state of seizure, and sustained chaos over the range of intrinsic activity typically associated with normal oper...

We review a neural network model based on chaotic dynamics [Babloyantz & Lourenco, 1994, 1996] and provide a detailed discussion of its biological and computational relevance. Chaos can be viewed as a "reservoir" containing an infinite number of unstable periodic orbits. In our approach, the periodic orbits are used as coding devices. By considering a large enough number of them, one can in principle expand the information processing capacity of small or moderate-size networks. The system is most of the time in an undetermined state characterized by a chaotic attractor. Depending on the type of an external stimulus, the dynamics is stabilized into one of the available periodic orbits, and the system is then ready to process information. This corresponds to the system being driven into an "attentive" state. We show that, apart from static pattern processing, the model is capable of dealing with moving stimuli. We especially consider in this paper the case of transient visual stimuli, which has a clear biological relevance. The advantages of chaos over more regular regimes are discussed.

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We studied changes in ensemble responses of striate cortical pairs for small (<10deg, 0.1c/deg) and large (>10deg, 0.1c/deg) differences in orientation and spatial frequency. Examination of temporal resolution and discharge history revealed advantages in discrimination from both dependent (connectivity) and independent (bursting) interspike interval properties. We found the average synergy (information greater than that summed from the individual neurons) was 50 % for fine discrimination of orientation and 25 % for spatial frequency and <10 % for gross discrimination of both orientation and spatial frequency. Dependency (Kullback-Leibler "distance " between the actual responses and two wholly independent responses) was measured between pairs of neurons while varying orientation, spatial frequency, and contrast. In general, dependency was more selective to spatial parameters than was firing rate. Variation of dependence against spatial frequency corresponded to variation of burst rate, and was even narrower than burst rate tuning for orientation. We also found a gradual decline (adaptation) of dependency overtime that is faster for lower contrasts and which is likely a result of the decrease in isolated (non-burst) spikes. The results suggest that salient information is more strongly represented in bursts, but that isolated spikes also have a role in transferring this information between neurons. The dramatic influence of burst length modulation on both synaptic efficacy and dependency around the peak orientation leads to substantial cooperation that can improve discrimination in this region.

We have examined the information contained in the coordinated activity of 22 to 25 cells recorded simultaneously using a 5x5 microelectrode array inserted into the primary visual cortex of anesthetized cats. The information difference in responses to drifting gratings of different orientations was quantified using the KL distance, which indicates the performance expected from an optimal classifier in discriminating two responses. When testing small differences (<10º) in grating orientation, the KL distance depended on the temporal resolution of response sampling and increased when including response history (up to about 10 ms). Joint-activity (i.e., cooperation) also increased KL distance for small orientation differences and cooperation increased as larger populations of cells were sampled jointly (up to 6 cells). The dependency or synchrony among cells was orientation-dependent and more selective than the average firing rates. Information on small angular differences is thus contained in fine structure of the spike train and is markedly enhanced by analysis across groups of cells. We also quantified response differences using ad-hoc distances based on a priori defined metrics. Metric distances were calculated based on the spike count, spike times, or spike-to-spike intervals. We calculated the information about orientation provided by