The computation represented by a sensory neuron's response to stimuli is constructed from
an array of physiological processes both belonging to that neuron and inherited from its …

The stimulus-response relationship of many sensory neurons is nonlinear, but fully
quantifying this relationship by a complex nonlinear model may require too much data to be …

In this paper, we review several lines of recent work aimed at developing practical methods
for adaptive on-line stimulus generation for sensory neurophysiology. We consider various …

Computational modeling is increasingly used to understand the function of neural circuits in
systems neuroscience. These studies require models of individual neurons with realistic …

We describe a class of models that predict how the instantaneous firing rate of a neuron
depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of …

It has recently been shown that networks of spiking neurons with noise can emulate simple
forms of probabilistic inference through “neural sampling”, ie, by treating spikes as samples …

A central challenge in sensory neuroscience involves understanding how neural circuits
shape computations across cascaded cell layers. Here we attempt to reconstruct the …

Neural activity is often described in terms of population-level factors extracted from the
responses of many neurons. Factors provide a lower-dimensional description with the aim of …

A few distinct cortical operations have been postulated over the past few years, suggested
by experimental data on nonlinear neural response across different areas in the cortex …

The brain represents and reasons probabilistically about complex stimuli and motor actions
using a noisy, spike-based neural code. A key building block for such neural computations …