Lecture
This lecture by the instructor covers the topic of networks of spiking neurons, focusing on mean-field models of AI states and transfer functions. It delves into the dynamics of excitatory and inhibitory populations in sparse-recurrent-random-balanced networks, as well as the prediction of state space in conductance-based networks. The lecture also explores spiking models of irregular states and the calculation of transfer functions from real neurons, including examples from mouse V1 neurons. Various numerical simulations and model predictions are discussed, providing insights into the behavior of different neuron types.