Abstract
Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.
Highlights
Short-term synaptic plasticity (STP) is a ubiquitous phenomenon occurring throughout the brain
In using rcrit to partition the STP parameter space, we found that requiring particular ranges of rcrit for the different synapse types greatly increases the chance of finding STP parameters that www.frontiersin.org result in SS-recurrent asynchronous irregular nonlinear (RAIN) networks
Through simulation, we studied the problem of estimating STP parameters that can induce SS-RAIN network activity
Summary
Short-term synaptic plasticity (STP) is a ubiquitous phenomenon occurring throughout the brain. A consequence of synapse physiology, STP produces temporary changes in synaptic efficacy. These facilitating or depressing modulations are driven by the recent synaptic activity—providing a history dependent transmission that may be important for short-term and working memory, as well as cognition. Because STP plays a wide reaching role in cortical computation, it is important to understand its precise nature. The authors developed a predictive characterization of that stable regime through control theoretic and computational analysis of the mean field model. Our characterization illuminates a method for selecting STP parameters to produced specific network properties—something that is useful for recreating empirical results
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