Topic: Nonlinear computations

A major challenge in sensory systems is to understand how neurons extract information from the natural environment. Models derived from their responses to artificial stimuli often have a hard time to generalize and predict responses to natural scenes. However, models directly learned on the responses to natural scenes can be hard to interpret. To address this issue, we have recently developed an approach where we add small perturbations to natural scenes and measure how these perturbations change neuronal responses, to better understand the features extracted by sensory neurons. I will show se

The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While recurrent spiking networks implementing linear computations can be directly derived and easily understood (e.g., in the spike coding network (SCN) framework), the connectivity required for nonlinear computations can be harder to interpret, as they require additional non-linearities (e.g., dendritic or synaptic) weighted through supervised training. Here we extend the SCN framework to directly implement any polynomial dynamical system. This results

Characterizing the function of a neuron in a sensory neural network is fundamental in sensory systems neuroscience. Systems theory offers an unbiased approach to characterize the function of neural systems by their response to unstructured white noise stimuli (WN)[1,2]. Although effective in early sensory stages[3,4], it yields limited insights into neurons that perform nonlinear computations such as motion detection. Determining where the WN approach fails in the processing hierarchy constitutes an experimental question. We recorded spiking activity from mouse retinal ganglion cells (RGCs) in