Exploration of the dynamics of spiking neural networks

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Authors
  1. Rhéaume, F.
  2. Grenier, D.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN)
Abstract
Liquid state machine is a technique that is well-suited for many spatiotemporal pattern recognition tasks found in defence applications. The technique exploits the complex dynamics of spiking neural networks. In general, Liquid state machines use biologically inspired neuron models such as the leaky-integrate- and fire model. When structured into a network, leaky-integrate-and-fire neurons interact in a complex and a non-linear manner, making it difficult to understand their behavior in response to an input stimulus. In this memorandum, the response of simple neural systems to input stimulus is studied. The goal is to provide a first exploration of the dynamical state of spiking neurons in reaction to their stimulation by continuous inputs. For this purpose, small neural systems made of, respectively, one single neuron, three serial neurons, and a three-by-three network are analyzed.

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Keywords
spiking neural networks;liquid state machine;leaky-integrate-and-fire;C-language-based simulator;CSIM
Report Number
DRDC-VALCARTIER-TM-2011-005 — Technical Memorandum
Date of publication
01 Mar 2013
Number of Pages
44
DSTKIM No
CA046804
CANDIS No
807159
Format(s):
Electronic Document(PDF)

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