| |
Abstract:
stimulus and context on perception. According to this law
choice probability ratios factorize into components independently
controlled by stimulus and context. It has been argued that this
pattern of results is incompatible with feedback models of
perception. In this paper we examine this claim using neural
network models defined via stochastic differential equations. We
show that the law is related to a condition named channel
separability and has little to do with the existence of feedback
connections. In essence, channels are separable if they converge
into the response units without direct lateral connections to other
channels and if their sensors are not directly contaminated by
external inputs to the other channels. Implications of the analysis
for cognitive and computational neurosicence are discussed.
|
