TY - JOUR
T1 - Data driven models of short-term synaptic plasticity
AU - Mokhtari, Elham Bayat
AU - Josh Lawrence, J.
AU - Stone, Emily F.
N1 - Publisher Copyright:
© 2018 Bayat Mokhtari, Lawrence and Stone.
PY - 2018/5/22
Y1 - 2018/5/22
N2 - 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.
AB - 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.
KW - Causal state splitting reconstruction
KW - Epsilon machines
KW - Interneuron-pyramidal cell synapses
KW - Mutual information
KW - Short term plasticity
KW - Synaptic filtering
UR - http://www.scopus.com/inward/record.url?scp=85049529083&partnerID=8YFLogxK
U2 - 10.3389/fncom.2018.00032
DO - 10.3389/fncom.2018.00032
M3 - Article
AN - SCOPUS:85049529083
SN - 1662-5188
VL - 12
JO - Frontiers in Computational Neuroscience
JF - Frontiers in Computational Neuroscience
M1 - 32
ER -