Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.

ABSTRACT: Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on 'Deep Retina,' a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this

SUBMITTER: Idrees S 

PROVIDER: S-EPMC11251147 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature