Most complex behaviors appear to be governed by internal motivational states or drives that modify an animal's responses to its environment. It is therefore of considerable interest to understand the neural basis of these motivational states. Drawing upon work on the neural basis of feeding in the marine mollusc Aplysia, we have developed a heterogeneous artificial neural network for controlling the feeding behavior of a simulated insect. We demonstrate that feeding in this artificial insect shares many characteristics with the motivated behavior of natural animals. 1 INTRODUCTION While an animal's external environment certainly plays an extremely important role in shaping its actions, the behavior of even simpler animals is by no means solely reactive. The response of an animal to food, for example, cannot be explained only in terms of the physical stimuli involved. On two different occasions, the very same animal may behave in completely different ways when presented with seemingly identical pieces of food (e.g.

The computational territory between the linearly summing McCulloch-Pitts neuron and the nonlinear differential equations of Hodgkin & Huxley is relatively sparsely populated. Connectionists use variants of the former and computational neuroscientists struggle with the exploding parameter spaces provided by the latter. However, evidence from biophysical simulations suggests that the voltage transfer properties of synapses, spines and dendritic membranes involve many detailed nonlinear interactions, not just 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. This contribution presents a simple learning principle for a binary tree with a logistic/quadratic transfer function at each node. These functions, though highly nested, are shown to be capable of changing their shape in concert. The resulting tree structure receives inputs at its leaves, and outputs an estimate of the probability that the input pattern is a member of one of two classes at the top.

The long-term goal of our laboratory is the development of analog resistive network-based VLSI implementations of early and intermediate vision algorithms. We demonstrate an experimental circuit for smoothing and segmenting noisy and sparse depth data using the resistive fuse and a 1-D edge-detection circuit for computing zero-crossings using two resistive grids with different spaceconstants. To demonstrate the robustness of our algorithms and of the fabricated analog CMOS VLSI chips, we are mounting these circuits onto small mobile vehicles operating in a real-time, laboratory environment.

These algorithms are also related to time-independent fonnalisms.

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.

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.