A Bayesian Attractor Model for Perceptual Decision Making
Fig 13
Network diagram for two-alternative Hopfield network (cf. Eqs 9, 10) with interpolated output that was used as generative model.
The network is driven by constant input g modulated by self and lateral inhibition between state variables z1 and z2. The strength of inhibition between state variables is determined by blat (note that self-inhibition is not linear, but moderated by a sigmoid function σ(z)) while the strength of self-inhibition and the strength of the constant input is controlled by blin. After passing through another sigmoid function σ(z) the state variables interpolate target positions (cf. description of single dot task above) stored in M and consequently produce the (mean) prediction μ.