(A) Diagram illustrating novel biophysical interpretation of the generalized linear model (GLM). The stimulus is convolved with a conductance filter weighted by , the difference between excitatory and inhibitory current reversal potentials, resulting in total synaptic current . This current is injected into the linear RC circuit governing the membrane potential , which is subject to a leak current with conductance and reversal potential . The instantaneous probability of spiking is governed by a the conditional intensity , where is a nonlinear function with non-negative output. Spiking is conditionally Poisson with rate , and spikes gives rise to a post-spike current or filter that affects the subsequent membrane potential. (B) Conductance-based encoding model (CBEM). The stimulus is convolved with filters and , whose outputs are transformed by rectifying nonlinearity to produce excitatory and inhibitory synaptic conductances and . These time-varying conductances and the static leak conductance drive synaptic currents with reversal potentials , , and , respectively. The resulting membrane potential is added to a linear spike-history term, given by , and then transformed via rectifying nonlinearity to obtain the conditional intensity , which governs conditionally Poisson spiking as in the GLM. Figure 1—figure supplement 1 shows that the CBEM parameters can be recovered from simulated data.