Biology subjects

Singer, Y.

Publications and source records attributed to Singer, Y..

2 recordsLinked to original sources

bioRxiv2017

Learning a neural response metric for retinal prosthesis

Retinal prostheses for treating incurable blindness are designed to electrically stimulate surviving retinal neurons, causing them to send artificial visual signals to the brain. However, electrical stimulation generally cannot precisely reproduce typical patterns of neural activity in the retina. Therefore, an electrical stimulus must be selected so as to produce a neural response as close as possible to the desired response. This requires a technique for computing the distance between a desired response and an achievable response that is meaningful in terms of the visual signal being conveyed. We propose a method to learn a metric on neural responses directly from recorded light responses of a population of retinal ganglion cells (RGCs) in the primate retina. The learned metric produces a measure of similarity of RGC population responses that accurately reflects the similarity of visual inputs. Using data from electrical stimulation experiments, we demonstrate that the learned metric could produce improvements in the performance of a retinal prosthesis.

neuroscience↗

bioRxiv2017

Sensory cortex is optimised for prediction of future input

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimised to represent features in the recent past of sensory input that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few video or audio frames in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, in their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields tended to resemble those in the brain. This suggests that sensory processing is optimised to extract those features with the most capacity to predict future input.\n\nImpact statementPrediction of future input explains diverse neural tuning properties in sensory cortex.

neuroscience↗