Physiological and statistical approaches to modeling of synaptic responses
Transmission of signals across synapses is the central component of nervous system function (Bennett and Kearns, 2000; Manwani and Koch, 2000). Primarily, such synaptic transmission is mediated by chemical neurotransmitter substances that are released into the synapse by the presynaptic neuron and detected by receptors located upon the postsynaptic neuron. In many areas of the nervous system “TF1747_C004” - #2 (outside of the hippocampus and neocortex) there may also be dendritic neurotransmitter release, which appears to behave in a very different fashion from the more traditional axonal neurotransmitter release (Ludwig and Pittman, 2003). However, because synaptic structures are microscopic and inaccessible, direct measurement of synaptic properties is not often feasible experimentally. As a result, our understanding of the mechanisms underlying synaptic transmission and modulation of function derives from statistical inferences, which are made from observed effects on populations of synapses. Synapses in the central nervous system (CNS) have the distinct property of being unreliable and probabilistic, hence a statistical framework is critical to define function. A schematic of a typical synapse is shown in Figure 4.1, indicating that the information transfer is between a relatively secure presynaptic action potential (on the left) and a highly insecure neurotransmitter release and subthreshold postsynaptic response. For most CNS neurons, spatial and temporal integration within a neuron is an additional complication, often requiring hundreds of synaptic responses summing together to reach the action potential threshold.
