Activity-Driven Computational Strategies of a Dynamically Regulated Integrate-and-Fire Model Neuron

A neat paper from 1999 that I saw.

This post is mostly identical to the corresponding page on NeuroWiki. You may wish to read/discuss it there instead.

This paper presents a leaky integrate and fire model which adapts to the average rate of incoming spikes. The model has two modes, integration mode and coincidence detection mode.

Specifically, the model is an extension to the Morris-Lecar model in which maximal conductance changes over time according to a simple calcium dynamics model. This change allows the neuron to adapt to different average rates of input.

Interestingly, directly after you change the average rate of presynaptic input, the neuron may be transiently pushed into a different mode. Specifically, if you bump up the level of activity, the neuron is pushed towards coincidence detection mode, and if you suddenly decrease the level of activity, the neuron is pushed towards integration mode.

The paper also contains a bunch of citations to introduce the spike rate vs spike timing code debate.

I’m not quite sure if the neuron’s “default” mode (that is, when the average rate of incoming spikes is fixed) is always the integrator mode, or if you can change that by changing the parameters. I don’t think there’s any hysteresis (that is, I don’t think that the neuron gets “stuck” in one mode or another; I think it only switches modes transiently and then returns to its default slowly over time as it adapts), but I’m not sure. Anyone care to clarify?

Michele Giugliano, Marco Bove, Massimo Grattarola. Activity-Driven Computational Strategies of a Dynamically Regulated Integrate-and-Fire Model Neuron. Journal of Computational Neuroscience 7(3): 247-254 (1999)