Inferring synaptic inputs from spikes with a conductance-based neural encoding model

eLife

Kenneth W Latimer¹  Fred Rieke¹  Jonathan W Pillow² 

[1] Department of Physiology and Biophysics, University of Washington, Seattle, United States;Princeton Neuroscience Institute, Department of Psychology, Princeton University, Princeton, United States;

关键词: retinal circuitry;    statistical modeling;    synaptic condutances;   

DOI  :  10.7554/eLife.47012

来源: DOAJ

【 摘 要 】

Descriptive statistical models of neural responses generally aim to characterize the mapping from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the conductance-based encoding model (CBEM), which describes a mapping from stimuli to excitatory and inhibitory synaptic conductances governing the dynamics of sub-threshold membrane potential. Remarkably, we show that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents. We validate these predictions with intracellular recordings from macaque retinal ganglion cells. Moreover, we offer a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced. This work forges a new link between statistical and biophysical models of neural encoding and sheds new light on the biophysical variables that underlie spiking in the early visual pathway.

【 授权许可】

Unknown