If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Neu.·al Network Analysis of Distributed Representations of Dynamical Sensory-Motor rrransformations in the Leech Shawn R. LockerYt Van Fangt and Terrence J. Sejnowski Computational Neurobiology Laboratory Salk Institute for Biological Studies Box 85800, San Diego, CA 92138 ABSTRACT Interneurons in leech ganglia receive multiple sensory inputs and make synaptic contacts with many motor neurons. These "hidden" units coordinate several different behaviors. We used physiological and anatomical constraints to construct a model of the local bending reflex. Dynamical networks were trained on experimentally derived input-output patterns using recurrent back-propagation. Units in the model were modified to include electrical synapses and multiple synaptic time constants.
A generic model of oscillating cortex, which assumes "minimal" coupling justified by known anatomy, is shown to function as an associative memory,using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long range excitatofy connections. Using a local Hebb-like learning rule for primary and higher order synapses at the ends of the long range connections, the system learns to store the kinds of oscillation amplitudepatterns observed in olfactory and visual cortex. This rule is derived from a more general "projection algorithm" for recurrent analog networks, that analytically guarantees content addressable memory storage of continuous periodic sequences - capacity: N/2 Fourier components for an N node network - no "spurious" attractors. 1 Introduction This is a sketch of recent results stemming from work which is discussed completely in [1, 2, 3]. Patterns of 40 to 80 hz oscillation have been observed in the large scale activity of olfactory cortex  and visual neocortex , and shown to predict the olfactory and visual pattern recognition responses of a trained animal.
The brain represents the skin surface as a topographic map in the somatosensory cortex. This map has been shown experimentally to be modifiable in a use-dependent fashion throughout life. We present a neural network simulation of the competitive dynamics underlying this cortical plasticity by detailed analysis of receptive field properties of model neurons during simulations of skin coactivation, corticallesion, digit amputation and nerve section. 1 INTRODUCTION Plasticity of adult somatosensory cortical maps has been demonstrated experimentally in a variety of maps and species (Kass, et al., 1983; Wall, 1988). This report focuses on modelling primary somatosensory cortical plasticity in the adult monkey. We model the long-term consequences of four specific experiments, taken in pairs.
We show that localized activity patterns in a layer of cells, collective excitations, can induce the formation of modular structures in the anatomical connections via a Hebbian learning mechanism. The networks are spatially homogeneous before learning, but the spontaneous emergenceof localized collective excitations and subsequently modularity in the connection patterns breaks translational symmetry. This spontaneous symmetry breaking phenomenon is similar to those which drive pattern formation in reaction-diffusion systems. We have identified requirements on the patterns of lateral connections and on the gains of internal units which are essential for the development of modularity. These essential requirements will most likely remain operative when more complicated (and biologically realistic)models are considered.
In this paper we develop a Bayes criterion which includes the Rissanen complexity, for inferring regular grammar models. We develop two methods for regular grammar Bayesian inference. The fIrst method is based on treating the regular grammar as a I-dimensional Markov source, and the second is based on the combinatoric characteristics of the regular grammar itself. We apply the resulting Bayes criteria to a particular example in order to show the efficiency of each method.
U niv. of Edinburgh ABSTRACT We announce new CMOS synapse circuits using only three and four MOSFETsisynapse. Neural states are asynchronous pulse streams, upon which arithmetic is performed directly. Chips implementing over 100 fully programmable synapses are described and projections to networks of hundreds of neurons are made. 1 OVERVIEW OF PULSE FIRING NEURAL VLSI The inspiration for the use of pulse firing in silicon neural networks is clearly the electrical/chemical pulse mechanism in "real" biological neurons. Neurons fire voltage pulses of a frequency determined by their level of activity but of a constant magnitude (usually 5 Volts) [Murray,1989a]. As indicated in Figure 1, synapses perform arithmetic directly on these asynchronous pulses, to increment or decrement the receiving neuron's activity.
A new learning algorithm, Learning by Choice of Internal Represetations (CHIR),was recently introduced. Whereas many algorithms reduce the learning process to minimizing a cost function over the weights, our method treats the internal representations as the fundamental entities to be determined. The algorithm applies a search procedure in the space of internal representations, and a cooperative adaptation of the weights (e.g. by using the perceptron learning rule). Since the introduction of its basic, single output version, theCHIR algorithm was generalized to train any feed forward network of binary neurons. Here we present the generalised version of the CHIR algorithm, and further demonstrate its versatility by describing how it can be modified in order to train networks with binary ( 1) weights. Preliminary tests of this binary version on the random teacher problem are also reported.
In the mammalian visual cortex, orientation-selective'simple cells' which detect straight lines may be adapted to detect curved lines instead. We test a biologically plausible, Hebbian, single-neuron model, which learns oriented receptive fields upon exposure to unstructured (noise)input and maintains orientation selectivity upon exposure to edges or bars of all orientations and positions. This model can also learn arc-shaped receptive fields upon exposure to an environment of only circular rings. Thus, new experiments which try to induce an abnormal (curved) receptive field may provide insightinto the plasticity of simple cells. The model suggests that exposing cells to only a single spatial frequency may induce more striking spatial frequency and orientation dependent effects than heretofore observed.
Purdue University W. Lafayette, IN. 47907 ABSTRACT A nonlinear neural framework, called the Generalized Hopfield network, is proposed, which is able to solve in a parallel distributed manner systems of nonlinear equations. The method is applied to the general nonlinear optimization problem. We demonstrate GHNs implementing the three most important optimization algorithms, namely the Augmented Lagrangian, Generalized Reduced Gradient and Successive Quadratic Programming methods. The study results in a dynamic view of the optimization problem and offers a straightforward model for the parallelization of the optimization computations, thus significantly extending the practical limits of problems that can be formulated as an optimization problem and which can gain from the introduction of nonlinearities in their structure (eg. The ability of networks of highly interconnected simple nonlinear analog processors (neurons) to solve complicated optimization problems was demonstrated in a series of papers by Hopfield and Tank (Hopfield, 1984), (Tank, 1986).