A spiking neural network for active efficient coding.

ABSTRACT: Biological vision systems simultaneously learn to efficiently encode their visual inputs and to control the movements of their eyes based on the visual input they sample. This autonomous joint learning of visual representations and actions has previously been modeled in the Active Efficient Coding (AEC) framework and implemented using traditional frame-based cameras. However, modern event-based cameras are inspired by the retina and offer advantages in terms of acquisition rate, dynamic range, and power consumption. Here, we propose a first AEC system that is fully implemented as a Spiking Neural Network (SNN) driven by inputs from an event-based camera. This input is efficiently encoded by a two-layer SNN, which in turn feeds into a spiking reinforcement learner that learns motor commands

SUBMITTER: Barbier T 

PROVIDER: S-EPMC11775837 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature