Population Models of Temporal Differentiation

Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. Here we systematically compare the performance of diverse networks, which calculate derivatives using 1) cell-intrinsic adaptation and synaptic depression dynamics, 2) feedforward network dynamics, and 3) recurrent network dynamics. Examples of each type of network are compared by quantifying the errors they introduce into the calculation, and their rejection of high-frequency input noise. This comparison is based on both analytical methods and numerical simulations with spiking leaky-integrate-and-fire (LIF) neurons. Both adapting and feedforward-network circuits provide good performance for signals with frequency bands that are well-matched to the time constants of post-synaptic current decay and adaptation, respectively. The synaptic-depression circuit performs similarly to the depression circuit, although strictly ...

• Type: Journal
• Year: 2010
• Where: NECO
• Authors: Bryan P. Tripp, Chris Eliasmith
• Keywords: Computer Networks, NECO 2010, Network Dynamics, Synaptic Depression, Synaptic Depression Dynamics