Interest centers on the nervous system, which provides the biological substrate for behavior and higher thought processes. Models are being constructed that attempt to provide a conceptual bridge between the physical phenomena occurring in the nervous system and the psychological level. A technique called synthetic neural modelling is used to construct computer-simulated organisms with senses, motor outputs, and a nervous system. Neurons in these models have biologically realistic properties based on experimental neurophysiology. They interact with each other and with the environment according to a comprehensive Darwinian view of population dynamics in the nervous system proposed by G. M. Edelman.
The ability of the model organisms to display adaptive behavior has been tested both in simulated worlds and in the real world, under conditions of both normal and impaired nervous system function. These models have shown how the ability to recognize objects and events in the environment can arise in the developing nervous system as a result of the operation of selective processes guided by innate value systems. There is no need for built-in representational codes or computational algorithms, nor for feedback of error signals from omniscient external teachers. These results call into question the popular theory that the brain is a kind of computer.
Areas of particular interest for exploration by synthetic modelling include sensory integration, perceptual categorization, control of locomotion, and aspects of memory. Current work focuses on neural mechanisms for recognition and recall of temporal patterns, which are of fundamental importance for language and music. To increase the realism of network simulations we have developed a composite approach to modelling neurons. In this approach, precalculated curves are used to model the time course of stereotypical responses, such as that of the fast sodium conductance, while a fuller treatment is used to model conductances whose responses are more dependent on instantaneous conditions in the the cell. Composite models are more realistic than simple "integrate-and-fire" models, yet can be simulated in a computer much more rapidly than full Hodgkin-Huxley models. We have employed this approach to explore the discharge responses of a model cerebellar Purkinje neuron to excitatory activation that exhibited a range of temporal correlations.