Numerical values of the parameters in a computational scheme in which visual inputs are combined to mimic personal space coding (Fig. 11B). Following the widely used framework of the firing rate neural models (Wilson and Cowan, 1972), the firing rates of excitatory and inhibitory populations were modeled as the following: drEdt=−rE + WEEf(rE) + WEIf(rI) + θE + WEI drIdt=−rI + WIEf(rE) + WIIf(rI) + θI + WII, where rE and rI are the firing rate of excitatory and inhibitory populations, respectively. WEE is the synaptic strength of excitatory-to-excitatory connections, WEI is the strength of inhibitory-to-excitatory connections, WIE is the strength of excitatory-to-inhibitory connections, and WII is the strength of inhibitory-to-inhibitory connections. Each population has a baseline activity represented by θE and θI, respectively. Finally, each population receives the external input I weighted by synaptic strengths WE and WI, respectively. Each population receives the firing rate of the other population as input through a sigmoid nonlinear function f(x)=1/(1+exp(−(x+θ))), where θ represents the intrinsic soft threshold of the population activity. The variable of interest in these simulations was the stable steady-state firing rate of the excitatory population as a function of the strength of the external input I.