A novel analytical characterization for short-term plasticity parameters in spiking neural networks

作者：O'Brien Michael J^*; Thibeault Corey M; Srinivasa Narayan

来源：Frontiers in Computational Neuroscience, 2014, 8: 148.

DOI：10.3389/fncom.2014.00148

• STP
• recurrent networks
• short term plasticity
• self-sustaining
• neural networks

Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used SIP model, we develop an analytical characterization of the SIP 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 SIP 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 SIP model. We conclude the work by integrating our characterization into a framework for finding suitable SIP 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.

• 出版日期2014-11-19