Theoretical models for describing neural signal transduction

Hanna Parnas, Ehud Sivan

Research output: Contribution to journalArticlepeer-review

Abstract

The example presented here, i.e. the auditory communication system of the cricket, is only one of many which demonstrate the importance of considering real physiological synapses in the framework of neuronal network analysis. Aspects such as synaptic delay, facilitatioi, depression, and duration were theoretically confirmed to play an important role in the overall behavior of neuronal networks. These being inherent features of all biological synapses, it is only natural to try to ensure that the model synapse faithfully captures them. On the other hand, when developing a model synapse, care must be taken to ensure that only key processes are considered, for otherwise, further study becomes impractical. Discussion in the present study was limited to the presynaptic nerve terminal. Obviously, many of the features shown to be important could have also been demonstrated via the complementary postsynaptic counterpart. Moreover, for a complete synapse to be efficiently yet faithfully represented, an approximate expression for the postsynaptic component must eventually be derived. Indeed, SO” includes Eqns. (12) and (14) in its TERMINAL unit, and different analytical expression in its POSTSYNAPSE unit. The latter expression has been derived using methodology similar to that described here. Based on a large number of experimental results a fully detailed model has been formulated. Simulations of the detailed model were then used as guidelines to what approximations are permissible. By this method, H. Parnas et al. (1989a) and Buchman and Parnas (1992) arrived at a (single) manageable analytical expression to replace the set of differential equations associated with the postsynaptic processes. As a result, in SO”, the complete synapse is represented by two analytical expressions, one summarizing presynaptic processes and the other postsynaptic ones. We suggest that these two rather simple expressions should be employed in simulations of neuronal networks. These expressions are as simple and efficient as the arbitrary LY function but, in contrast to the a and like functions, they possess all the inherent characteristics of a real biological synapse.

Original languageEnglish
Pages (from-to)181-193
Number of pages13
JournalProgress in Brain Research
Volume102
Issue numberC
DOIs
StatePublished - 1 Jan 1994

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