Multiple bumps can enhance robustness to noise in continuous attractor networks
Raymond Wang,Louis Kang · COSYNE 2022
A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells that maintain periodic representations of position and orientation, respectively. However, these representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We construct 1D ring a
Sat, Mar 19, 2022