A number of studies have described neurons in the dorsal part of the medial superior temporal (MSTd) monkey cortex that respond best to large expanding/contracting, rotating, or shifting patterns (Tanaka et al., 1986; Duffy & Wurtz, 1991a). Recently Graziano et al. (1994) found that MSTd cell responses correspond to a point in a multidimensional space of spiral motions, where the dimensions are these motion types. Combinationsof these motions are generated as an animal moves through its environment, whichsuggests that area MSTd could playa role in optical flow analysis. When an observer moves through a static environment, a singularity in the flow field known as the focus of expansion may be used to determine the direction of heading (Gibson, 1950; Warren & Hannon, 1988). Previous computational models of MSTd (Lappe & Rauschecker, 1993; Perrone & Stone, 1994) have shown how navigational information related to heading may be encoded by these cells.

We implement and study a computational model of Stevens' [19921 theory of the pathogenesis of schizophrenia. This theory hypothesizes thatthe onset of schizophrenia is associated with reactive synaptic regeneration occurring in brain regions receiving degenerating temporallobe projections. Concentrating on one such area, the frontal cortex, we model a frontal module as an associative memory neural network whose input synapses represent incoming temporal projections. We analyze how, in the face of weakened external input projections, compensatory strengthening of internal synaptic connections and increased noise levels can maintain memory capacities(which are generally preserved in schizophrenia). However, These compensatory changes adversely lead to spontaneous, biasedretrieval of stored memories, which corresponds to the occurrence of schizophrenic delusions and hallucinations without anyapparent external trigger, and for their tendency to concentrate onjust few central themes. Our results explain why these symptoms tend to wane as schizophrenia progresses, and why delayed therapeuticalintervention leads to a much slower response.

The macaque lateral geniculate nucleus (LGN) exhibits an intricate lamination pattern, which changes midway through the nucleus at a point coincident with small gaps due to the blind spot in the retina. We present a three-dimensional model of morphogenesis in which local cell interactions cause a wave of development of neuronal receptive fieldsto propagate through the nucleus and establish two distinct lamination patterns. We examine the interactions between the wave and the localized singularities due to the gaps, and find that the gaps induce the change in lamination pattern. We explore critical factors which determine general LGN organization.

Songbirds learn to imitate a tutor song through auditory and motor learning. Wehave developed a theoretical framework for song learning that accounts for response properties of neurons that have been observed in many of the nuclei that are involved in song learning. Specifically, we suggest that the anteriorforebrain pathway, which is not needed for song production in the adult but is essential for song acquisition, provides synaptic perturbations and adaptive evaluations for syllable vocalization learning. A computer model based on reinforcement learning was constructed thatcould replicate a real zebra finch song with 90% accuracy based on a spectrographic measure.

A biological neuron can be viewed as a device that maps a multidimensional temporalevent signal (dendritic postsynaptic activations) into a unidimensional temporal event signal (action potentials). We have designed a network, the Spatio-Temporal Event Mapping (STEM) architecture, which can learn to perform this mapping for arbitrary biophysical modelsof neurons. Such a network appropriately trained, called a STEM cell, can be used in place of a conventional compartmental modelin simulations where only the transfer function is important, such as network simulations. The STEM cell offers advantages over compartmental models in terms of computational efficiency, analytical tractabili1ty, and as a framework for VLSI implementations of biological neurons.

The spatial distribution and time course of electrical signals in neurons have important theoretical and practical consequences. Because it is difficult to infer how neuronal form affects electrical signaling, we have developed a quantitative yet intuitive approach to the analysis of electrotonus. This approach transforms the architecture of the cell from anatomical to electrotonic space, using the logarithm of voltage attenuation as the distance metric. We describe the theory behind this approach and illustrate its use. 1 INTRODUCTION The fields of computational neuroscience and artificial neural nets have enjoyed a mutually beneficial exchange of ideas. This has been most evident at the network level, where concepts such as massive parallelism, lateral inhibition, and recurrent excitation have inspired both the analysis of brain circuits and the design of artificial neural net architectures.

Understanding the response of neural nets to structural/functional damage is important fora variety of reasons, e.g., in assessing the performance of neural network hardware, and in gaining understanding of the mechanisms underlying neurological andpsychiatric disorders. Recently, there has been a growing interest in constructing neuralmodels to study how specific pathological neuroanatomical and neurophysiological changes can result in various clinical manifestations, and to investigate thefunctional organization of the symptoms that result from specific brain pathologies (reviewed in [1, 2]). In the area of associative memory models specifically, earlystudies found an increase in memory impairment with increasing lesion severity (in accordance with Lashley's classical'mass action' principle), and showed that slowly developing lesions have less pronounced effects than equivalent acute lesions [3]. Recently, it was shown that the gradual pattern of clinical deterioration manifested in the majority of Alzheimer's patients can be accounted for, and that different synaptic compensation rates can account for the observed variation in the severity and progression rate of this disease [4]. However, this past work is limited in that model elements have no spatial relationships to one another (all elements are conceptually equidistant).

Almost all models of orientation and direction selectivity in visual cortex are based on feedforward connection schemes, where geniculate inputprovides all excitation to both pyramidal and inhibitory neurons. The latter neurons then suppress the response of the former fornon-optimal stimuli. However, anatomical studies show that up to 90 % of the excitatory synaptic input onto any cortical cellis provided by other cortical cells. The massive excitatory feedback nature of cortical circuits is embedded in the canonical microcircuit of Douglas &. Martin (1991). We here investigate analytically andthrough biologically realistic simulations the functioning of a detailed model of this circuitry, operating in a hysteretic mode. In the model, weak geniculate input is dramatically amplified byintracortical excitation, while inhibition has a dual role: (i) to prevent the early geniculate-induced excitation in the null direction and(ii) to restrain excitation and ensure that the neurons fire only when the stimulus is in their receptive-field.

A common difficulty in diagnosing failures within Pratt & Whitney's F100-PW-100/200 gas turbine engine occurs when a fault in one part of a system -- comprising an engine, an airframe, a test cell, and automated ground engine test set (AGETS) equipment -- is manifested as an out-of-bound parameter elsewhere in the system. However, because the self-diagnostics only test the specified local parameter, it will pass, leaving only the operators' experience and traditional fault-isolation manuals to locate the source of the problem in another part of the system. This article describes a diagnostic tool (that is, AGETS MBR), designed to overcome this problem by isolating failures using an overall system troubleshooting approach. AGETS MBR was developed jointly by personnel at Pratt & Whitney and United Technologies Research Center using an AI tool called the qualitative reasoning system (QRS).

The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed.