Abstract
Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between synapse type, and classify the complexity of the process. We find that the machines allow us to differentiate between processes in a way not possible by considering distributions alone. We are also able to understand these differences in terms of the dynamics of the model used to create the output response, bringing the analysis full circle. Hence this technique provides a complimentary description of the synaptic filtering process, and potentially expands the interpretation of future experimental results.
Highlights
Short term plasticity at the synapse level can have profound effects on functional connectivity of neurons
It is one of a long sequence of models developed from 1998 to the present, with notable contributions by Markram et al (1998) and Dittman et al (2000). The latter is a good exposition of the model as it pertains to various types of short term plasticity seen in the central nervous system, and the underlying dependence of the plasticity is based on physiologically relevant dynamics of calcium influx and decay within the presynaptic terminal
The probability of getting a large Pr value is quite large, and its epsilon machine captures that dynamic with one state
Summary
Short term plasticity at the synapse level can have profound effects on functional connectivity of neurons. In this paper Abbott and Regher state “The potential computational power of synapses is large because their basic signal transmission properties can be affected by the history of presynaptic and postsynaptic firing in so many different ways.”. They outline the basic function of a synapse as a signal filter as follows: Synapses with an initial low probability of release act as high pass filters through facilitation, while synapses with an initially high probability of release exhibit depression and subsequently serve as low pass filters. Identifying synapse-specific molecular mechanisms is currently an active area of research, involving subtle changes in expression of myriad calcium sensor isoforms (synaptotagmins), subtly configured to alter the microscopic rates of synaptic release, facilitation, depression, and vesicle replenishment (Fioravante and Regehr, 2011; Chen and Jonas, 2017; Jackman and Regehr, 2017)
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