Figure 1

Fisher linear discrimination of neural activity in a population model. (A) Two neural populations (x and y) where the noise correlation is adjusted via a parameter ρ. Each population receives two distinct inputs (\(\nu _{1}\) and \(\nu _{2}\)) and a private source of noise whose gain is \(\beta _{\mathrm{x}}\) and \(\beta _{\mathrm{y}}\), respectively. The stimulus-driven response of each population is described by a tuning curve relating stimulus orientation to firing rate. (B) Activity for populations x and y is shown at discrete time-points (solid black circles). The optimal decision boundary (c) obtained by LDA discriminates amongst the neural activity generated by each of the two inputs. Neural responses follow a Gaussian distribution. The shaded area shows the proportion of discrimination error for stimulus 2