Quantifying Entropy in Neuroscience Data

This lecture explores the concept of entropy in neuroscience data, focusing on quantifying randomness and information in biological data. The instructor discusses the response of a neuron to sensory input, statistical dependence, and inferring probability distributions from data. Through examples of binary representations of neuron activity and experimental setups, the lecture delves into how sequences of spikes convey information from stimuli. The process of discretizing signals to obtain binary counts of spikes is explained, along with the selection of time intervals for accurate representation. The lecture concludes with a discussion on the maximum entropy of sequences of binary digits and their implications in understanding sensory perception.