How to implement the step math function when the membrane potential depends on the past spikes?


#1

I am trying to implement a learning model whose membrane potential function is dependent on past spiking behavior how would I implement such a function in the step math function?


#2

I can see that you’ve asked for help in the thread How do you create a neural model in Nengo? and received a link to the tutorial: https://www.nengo.ai/nengo/examples/usage/rectified_linear.html

At this point you might want to dig into the Nengo code for more reference. For example, here is how the Izhikevich model is implemented. Note the recovery parameter used to track additional state information between successive calls to step_math. In your case, such a parameter could be used to remember values such as ‘the time that each neuron last spiked’.

It may also help if you could give more detail about your model. Depending what information is needed by step_math, there could be several different ways of going about this (e.g., as a custom unsupervised learning rule, or by doing something similar to the below).