Topic: nonlinear computations

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

Sensory neurons can integrate their inputs nonlinearly, and this nonlinear pooling allows the extraction of complex features from the natural environment. Such nonlinear computations are ubiquitous in the visual system, and can be captured with encoding models that partition receptive fields into subunits whose outputs are nonlinearly combined. In the retina, the nonlinearities that transform subunit signals before integration by retinal ganglion cells affect the cells’ sensitivity to the spatial structure of natural scenes. Fitting subunit models to neural responses remains a challenge, as av

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