Figure 4

Selective processing of baseline and modulation. (A) General reduced circuit model for the operation of a network on its inputs. (B) Reduced circuit model for selective operation of the network on baseline and modulation components of an input vector. (C) Top: Eigenvalue distribution of the weight matrix, $\overrightarrow{W}$, shown for EPSP = 0.1 mV (J = τ_syn e EPSP = 0.136 mV). For normalization, each entry is divided by the reset potential, V_reset = 20 mV. The ‘exceptional eigenvalue’ (green) corresponds to the uniform eigenmode, i.e. the baseline, and the bulk of the spectrum (orange) determines the response of the network to perturbations of a uniform input. Bottom, left: Eigenvalue distribution for the matrix ${(\overrightarrow{\mathbb{1}}-\overrightarrow{W})}^{-1}$, which gives the stationary firing rates. Bottom, right: Sorted magnitudes of eigenvalues of $\overrightarrow{W}$ and ${(\overrightarrow{\mathbb{1}}-\overrightarrow{W})}^{-1}$. (D) Baseline and modulation components for individual neurons in the network with EPSP=0.1 mV. Scatter plot (center) shows the modulation vs. baseline component of output tuning curves for all neurons of the network. Baseline and modulation are taken as the mean (F0) and the second Fourier component (F2) of individual tuning curves, respectively. The markers (cyan crosses) show the center of mass of each cloud. The histograms indicate the marginal distributions of baseline (green, top) and modulation (orange, right) components, respectively.