Elementary principles of nonlinear synaptic transmission

Authors:
Henry Markram
Affiliations:
Department of Neurobiology, Weizmann Institute for Science, Rehovot, ISRAEL
Venue:
Computational models for neuroscience
Year:
2003

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Abstract

The efficacy of synaptic transmission changes dynamically from one action potential to the next. This dynamic transmission results in nonlinear input-output functions at synapses. The lack of quantitative experimental data on nonlinear synaptic transfer has hindered the incorporation of these synapses into comprehensive theories of brain function. Recent experiments, however, between specific types of neurons in the neocortex provide quantitative data and novel insight into synaptic operations. Based on these data, elementary principles of nonlinear synaptic transmission are derived here and are invoked to propose a general theory of information processing, learning, and memory in recurrent neural networks. It is proposed that temporal features of the environment are processed by nonlinear synaptic transmission and by recurrent interactions between neurons and that recurrent circuitry serves to generate a continual neural representation of past information that is embedded in single neurons and in the connectivity structure of the network. In this theory, all memories are potentially instantly available from the current state of activity and a major component of learning is where the microcircuit learns about itself in order to align the functions of each of its components (molecules, synapses and neurons).