Notes

Learning represents another type of nonlinearity. By learning we mean the change in neuronal response based on the experience that the neuron has had. In neuroscience, we often discuss about Hebbian Learning. This concept postulates that the synapses between two neurons are plastic, dynamic. "Neurons that fire together, wire together". The fact that we have dynamic synapses represents a non-linearity, as the response to the same stimulus could potentially vary in time. Is Hebbian Learning happening in V1 simple cells?

Evidence for Hebbian Learning in V1 The figure below shows that the orientation tuning of simple cells in cats V1. In the experiment, the visual response of a single cell to a light bar was drove to a "high" level (via electrode stimulation) when presented with an initially nonpreferred orientation (S+), and alternately reducing it to a "low" level when presented with the preferred orientation (S). The goal was to test the possible role of neuronal coactivity in controlling the plasticity of orientation selectivity. Approx. 40% of the tested cells showed significant long-lasting changes in their relative orientation preference. The results support the hypothesis that covariance levels between pre- and post-synaptic activity determine the sign and the amplitude of the modification of efficacy or cortical synapses. Thus, meaning that V1 simple cells feature Hebbian learning nonlinearities.