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• W2801359411 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 (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." @default.
• W2801359411 created "2018-05-17" @default.
• W2801359411 creator A5025952046 @default.
• W2801359411 creator A5040263460 @default.
• W2801359411 creator A5078578099 @default.
• W2801359411 date "2018-05-22" @default.
• W2801359411 modified "2023-09-26" @default.
• W2801359411 title "Data Driven Models of Short-Term Synaptic Plasticity" @default.
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• W2801359411 doi "https://doi.org/10.3389/fncom.2018.00032" @default.
• W2801359411 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5972196" @default.
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• W2801359411 hasPublicationYear "2018" @default.
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