The pyloric Central Pattern Generator of the crustacean stomatogastric ganglion is a well-defined biological neural network. This 14-neuron network is modulated by many inputs. These inputs reconfigure the network to produce multiple output patterns by three simple mechanisms: 1) detennining which cells are active; 2) modulating the synaptic efficacy; 3) changing the intrinsic response properties of individual neurons. The importance of modifiable intrinsic response properties of neurons for network function and modulation is discussed.

The input/output properties of a 2 compartment model neuron are systematically explored. Taken from the work of MacGregor (MacGregor, 1987), the model neuron compartments contain several active conductances, including a potassium conductance in the dendritic compartment driven by the accumulation of intradendritic calcium. Dynamics of the conductances and potentials are governed by a set of coupled first order differential equations which are integrated numerically. There are a set of 17 internal parameters to this model, specificying conductance rate constants, time constants, thresholds, etc. To study parameter sensitivity, a set of trials were run in which the input driving the neuron is kept fixed while each internal parameter is varied with all others left fixed. To study the input/output relation, the input to the dendrite (a square wave) was varied (in frequency and magnitude) while all internal parameters of the system were left flXed, and the resulting output firing rate and bursting rate was counted. The input/output relation of the model neuron studied turns out to be much more sensitive to modulation of certain dendritic potassium current parameters than to plasticity of synapse efficacy per se (the amount of current influx due to synapse activation). This would in turn suggest, as has been recently observed experimentally, that the potassium current may be as or more important a focus of neural plasticity than synaptic efficacy.

In this paper we compare regression and classification systems. A regression system can generate an output f for an input X, where both X and f are continuous and, perhaps, multidimensional. A classification system can generate an output class, C, for an input X, where X is continuous and multidimensional and C is a member of a finite alphabet. The statistical technique of Classification And Regression Trees (CART) was developed during the years 1973 (Meisel and Michalpoulos) through 1984 (Breiman el al).

The vestibulo-ocular reflex (VOR) is the primary mechanism that controls the compensatory eye movements that stabilize retinal images duringrapid head motion. The primary pathways of this system are feed-forward, with inputs from the semicircular canals and outputs to the oculomotor system. Since visual feedback is not used directly in the VOR computation, the system must exploit motor learning to perform correctly. Lisberger(1988) has proposed a model for adapting the VOR gain using image-slip information from the retina. We have designed and tested analog very largescale integrated(VLSI) circuitry that implements a simplified version of Lisberger's adaptive VOR model.

It is well-known that neural responses in particular brain regions are spatially organized, but no general principles have been developed thatrelate the structure of a brain map to the nature of the associated computation. On parallel computers, maps of a sort quite similar to brain maps arise when a computation is distributed across multiple processors. In this paper we will discuss the relationship betweenmaps and computations on these computers and suggest how similar considerations might also apply to maps in the brain. 1 INTRODUCTION A great deal of effort in experimental and theoretical neuroscience is devoted to recording and interpreting spatial patterns of neural activity. A variety of map patterns have been observed in different brain regions and, presumably, these patterns reflectsomething about the nature of the neural computations being carried out in these regions. To date, however, there have been no general principles for interpreting the structure of a brain map in terms of properties of the associated computation. In the field of parallel computing, analogous maps arise when a computation isdistributed across multiple processors and, in this case, the relationship Computational Eftkiency 61 between maps and computations is better understood. In this paper, we will attempt torelate some of the mapping principles from the field of parallel computing to the organization of brain maps.

It has been known for many years that specific regions of the working cerebralcortex display periodic variations in correlated cellular activity. While the olfactory system has been the focus of much of this work, similar behavior has recently been observed in primary visual cortex. We have developed models of both the olfactory and visual cortex which replicate the observed oscillatory properties ofthese networks. Using these models we have examined the dependence of oscillatory behavior on single cell properties and network architectures.We discuss the idea that the oscillatory events recorded from cerebral cortex may be intrinsic to the architecture of cerebral cortex as a whole, and that these rhythmic patterns may be important in coordinating neuronal activity during sensory processmg.

The goal in this work has been to identify the neuronal elements of the cortical column that are most likely to support the learning of nonlinear associative maps. We show that a particular style of network learning algorithm based on locally-tuned receptive fields maps naturally onto cortical hardware, and gives coherence to a variety of features of cortical anatomy, physiology, and biophysics whose relations to learning remain poorly understood.

The midbrain of the barn owl contains a map-like representation of sound source direction which is used to precisely orient the head toward targetsof interest. Elevation is computed from the interaural difference in sound level. We present models and computer simulations oftwo stages of level difference processing which qualitatively agree with known anatomy and physiology, and make several striking predictions. 1 INTRODUCTION

The computational territory between the linearly summing McCulloch-Pitts neuron and the nonlinear differential equations of Hodgkin & Huxley is relatively sparsely populated. Connectionistsuse variants of the former and computational neuroscientists struggle with the exploding parameter spaces provided by the latter. However, evidence frombiophysical simulations suggests that the voltage transfer properties of synapses, spines and dendritic membranes involve many detailed nonlinear interactions, notjust a squashing function at the cell body. Real neurons may indeed be higher-order nets. For the computationally-minded, higher order interactions means, first of all, quadratic terms.

In this paper, we discuss a current attempt at applying the organizational principleEdelman calls Neuronal Group Selection to the control of a real, two-link robotic manipulator. We begin by motivating theneed for an alternative to the position-control paradigm of classical robotics, and suggest that a possible avenue is to look at the primitive animal limb'neurologically ballistic' control mode. We have been considering a selectionist approach to coordinating a simple perception-action task. 1 MOTIVATION The majority of industrial robots in the world are mechanical manipUlators - often armlike devices consisting of some number of rigid links with actuators mounted where the links join that move adjacent links relative to each other, rotationally or translation ally. At the joints there are typically also sensors measuring the relative position of adjacent links, and it is in terms of position that manipulators are generally controlled (a desired motion is specified as a desired position of the end effector, from which can be derived the necessary positions of the links comprising the manipulator). Position control dominates largely for historical reasons, rooted in bang-bang control: manipulators bumped between mechanical stops placed so as to enforce a desired trajectory for the end effector.