The Simulation of Spiking Neural Networks

The Simulation of Spiking Neural Networks

David Gamez (Imperial College, UK)
DOI: 10.4018/978-1-60566-774-4.ch015
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Abstract

This chapter is an overview of the simulation of spiking neural networks that relates discrete event simulation to other approaches and includes a case study of recent work. The chapter starts with an introduction to the key components of the brain and sets out three neuron models that are commonly used in simulation work. After explaining discrete event, continuous and hybrid simulation, the performance of each method is evaluated and recent research is discussed. To illustrate the issues surrounding this work, the second half of this chapter presents a case study of the SpikeStream neural simulator that covers the architecture, performance and typical applications of this software along with some recent experiments. The last part of the chapter suggests some future trends for work in this area.
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Introduction

In recent years there has been a great deal of interest in the simulation of neural networks to test our theories about the brain, and these models are also being used in a wide variety of applications ranging from data mining to machine vision and robot control. In the past the majority of these simulations were based on the neurons’ average firing rate and there is now a growing interest in the development of more biologically realistic spiking models, which present their own challenges and are well suited to discrete event simulation.

This chapter starts with some background information about the operation of neurons and synapses in the brain and sets out some of the reasons why simulation plays an important role in neuroscience research. Next, some common neural models are examined and the chapter moves on to look at the differences between continuous simulation, discrete event simulation and the emerging hybrid approach. The issues in this area are then illustrated with a more detailed look at the SpikeStream neural simulator that covers its architecture, performance, typical applications and recent experiments. Finally, the chapter concludes with some likely future directions for work in this area.

Key Terms in this Chapter

Hybrid Simulation: An approach to simulation in which a continuous simulation clock is maintained and updates to the model are event-driven.

Spike: A pulse of electrical voltage sent from one neuron to another to communicate information.

Synapse: A junction between the axon of one neuron and the dendrite of another.

Dendrite: Each neuron has a large number of fibres called dendrites that receive spikes from other neurons. The axon of one neuron connects to the dendrite of another at a junction called a synapse.

Continuous Simulation: An approach to simulation in which updates to the model are driven by the advance of a simulation clock.

STDP (Spike Time Dependent Plasticity): A learning rule in which the weight of a synapse is increased if a spike arrives prior to the firing of a neuron, and decreased if the spike arrives after the firing of a neuron.

SpikeStream: Software for the hybrid simulation of spiking neural networks.

Discrete Event Simulation: An approach to simulation in which updates to the model are event-driven instead of clock-driven. This type of simulation typically works by maintaining a queue of events that are sorted by the time at which they are scheduled to occur.

Axon: When a neuron fires it sends a voltage spike along a fibre known as an axon, which connects to the dendrites of other neurons at a junction called a synapse.

Neuron: A cell in the brain that carries out information processing. There are approximately 80 billion neurons in the human brain.

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