Analog-digital simulations of full conductance-based networks of
spiking neurons with spike timing dependent plasticity.
Quan Zou, Yannick Bornat, Sylvain Saïghi, Jean Tomas, Sylvie
Renaud and Alain Destexhe
Network 17: 211-233, 2006.
We introduce and test a system for simulating networks of
conductance-based neuron models using analog circuits. At the
single-cell level, we use custom-designed analog circuits (ASICs)
that simulate two types of spiking neurons based on Hodgkin-Huxley
like dynamics: ``regular spiking'' excitatory neurons with
spike-frequency adaptation, and ``fast spiking'' inhibitory neurons.
Synaptic interactions are mediated by conductance-based synaptic
currents described by kinetic models. Connectivity and plasticity
rules are implemented digitally through a real time interface
between a computer and a PCI board containing the ASICs. We show a
prototype system of a few neurons interconnected with synapses
undergoing spike-timing dependent plasticity (STDP), and compare
this system with numerical simulations. We use this system to
evaluate the effect of parameter dispersion on the behavior of small
circuits of neurons. It is shown that, although the exact spike
timings are not precisely emulated by the ASIC neurons, the behavior
of small networks with STDP matches that of numerical simulations.
Thus, this mixed analog-digital architecture provides a valuable
tool for real-time simulations of network of neurons with STDP.
They should be useful for any real-time application, such as hybrid
systems interfacing network models with biological neurons.
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