In "Supervised Learning in Spiking Neural Networks with FORCE Training" by Wilten Nicola and Claudia Clopath. The authors create a learning rule for learning non-linear dynamics from populations of spiking neurons. The learning rule only seems to depend on:
- an error signal
- the firing rates of each neurons
- an approximate inverse correlation matrix calculated from the firing rates of each neuron
Is it possible to calculate the inverse correlation matrix of using another population of neurons or is there some biological mechanism that could be used to explain this? Additionally, is this calculation sensitive to delay? Often, forgetting to take delay into account is the undoing of many learning rules and cognitive architectures.