The accuracy of membrane potential reconstruction based on spiking receptive fields

Abstract

Mohanty D, Scholl B, Priebe NJ. The accuracy of membrane potential reconstruction based on spiking receptive fields. J Neurophysiol 107: 2143-2153, 2012. First published January 25, 2011 doi:10.1152/jn.01176.2011.-A common technique used to study the response selectivity of neurons is to measure the relationship between sensory stimulation and action potential responses. Action potentials, however, are only indirectly related to the synaptic inputs that determine the underlying, subthreshold, response selectivity. We present a method to predict membrane potential, the measurable result of the convergence of synaptic inputs, based on spike rate alone and then test its utility by comparing predictions to actual membrane potential recordings from simple cells in primary visual cortex. Using a noise stimulus, we found that spike rate receptive fields were in precise correspondence with membrane potential receptive fields (R 2 ϭ 0.74). On average, spike rate alone could predict 44% of membrane potential fluctuations to dynamic noise stimuli, demonstrating the utility of this method to extract estimates of subthreshold responses. We also found that the nonlinear relationship between membrane potential and spike rate could also be extracted from spike rate data alone by comparing predictions from the noise stimulus with the actual spike rate. Our analysis reveals that linear receptive field models extracted from noise stimuli accurately reflect the underlying membrane potential selectivity and thus represent a method to generate estimates of the underlying average membrane potential from spike rate data alone. primary visual cortex; spike threshold; intracellular recording; simple cells THE PRIMARY MODE OF COMMUNICATION between neurons in cortex is synaptic transmission triggered by action potentials. Action potentials, or spikes, result when a neuron's many synaptic inputs combine to cause it to reach a critical membrane potential threshold. Whereas a neuron's spiking response to a sensory stimulus defines the information it is communicating to downstream neurons, the selectivity of its response is determined by the convergence of its synaptic inputs via upstream neurons. Understanding the basis for sensory selectivity, therefore, requires knowledge of the selectivity of the afferent synaptic inputs.We know that the aggregate synaptic input, and therefore the response selectivity of such input, to a neuron is represented by its membrane potential fluctuations in response to sensory stimulation; however, direct measurement of membrane potential requires intracellular recordings, which are difficult to obtain relative to extracellular (spiking) recordings. Therefore, a method of estimating the underlying membrane potential selectivity from extracellular recordings of spikes alone would be important for providing visibility into the selectivity of synaptic inputs in cortex.Such estimation, however, has historically been challenged by the presence of the intervening action potential threshold. Threshold acts as a filter ...

Functional characterization of spikelet activity in the primary visual cortex

“…One‐dimensional noise sequences were presented to measure linear and nonlinear receptive field components (Mohanty et al . ).…”

“…All binocular and monocular stimuli were presented during the same block and pseudo-randomly interleaved. One-dimensional noise sequences were presented to measure linear and nonlinear receptive field components (Mohanty et al 2012).…”

“…To further test simple cells, we measured receptive fields (RFs) using a one-dimensional noise stimulus (see Methods) (McLean & Palmer, 1989;DeAngelis et al 1993;McLean et al 1994;Conway & Livingstone, 2003;Priebe, 2005;Mohanty et al 2012;Park et al 2013). Simple cells are characterized by linear filters, whereas complex cells exhibit dominant nonlinear filters.…”

Mechanisms of Neuronal Computation in Mammalian Visual Cortex

“…In this case, locations that on their own evoke only subthreshold membrane potential responses (were they to start from rest) can still influence the ongoing spike rate and be detected in spike rate recordings. Thus, a noise stimulus circumvents the threshold nonlinearity, resulting in a spiking receptive field map that is comparable to that recorded directly from V m responses (Mohanty et al, 2012). …”

Multiple timescales account for adaptive responses across sensory cortices

“…We assayed the model performance by examining how well it could account for the average response to a separate (held-out) Poisson sequence that had been repeated many times (Fig 2D.) Across V1, S1 and A1, models composed of 4 filters accounted for a significant amount of the explainable response variance to the repeated Poisson sequence (R 2 : V1 = .47 +/-.04; S1 = .4 +/-.06; A1 = .50 +/-..07; values indicate mean +/-SE across neurons), demonstrating that this modeling framework can largely account for sensory responses in cortex. To estimate the fraction of explainable variance the model accounts for, we employed a method developed by Sahani and Linden (2003) which takes into account the number of stimulus repeats and the variation between trials (see Sahani & Linden, 2003;Mohanty et al 2012). This correction factor only modestly increased the variance accounted for by our model (Fig 2E; corrected R 2 : V1 = .56 +/-.04; S1 = .61 +/-.07; A1 = .68 +/-.06).…”