Shifts in Coding Properties and Maintenance of Information Transmission during Adaptation in Barrel Cortex

Abstract

Neuronal responses to ongoing stimulation in many systems change over time, or “adapt.” Despite the ubiquity of adaptation, its effects on the stimulus information carried by neurons are often unknown. Here we examine how adaptation affects sensory coding in barrel cortex. We used spike-triggered covariance analysis of single-neuron responses to continuous, rapidly varying vibrissa motion stimuli, recorded in anesthetized rats. Changes in stimulus statistics induced spike rate adaptation over hundreds of milliseconds. Vibrissa motion encoding changed with adaptation as follows. In every neuron that showed rate adaptation, the input–output tuning function scaled with the changes in stimulus distribution, allowing the neurons to maintain the quantity of information conveyed about stimulus features. A single neuron that did not show rate adaptation also lacked input–output rescaling and did not maintain information across changes in stimulus statistics. Therefore, in barrel cortex, rate adaptation occurs on a slow timescale relative to the features driving spikes and is associated with gain rescaling matched to the stimulus distribution. Our results suggest that adaptation enhances tactile representations in primary somatosensory cortex, where they could directly influence perceptual decisions.

Adaptation of the simple or complex nature of V1 receptive fields to visual statistics

“…This would improve information transmission by avoiding the over-representation of second-order correlations among cell population responses in dense visual contexts while increasing their detectability in sparse contexts, thus adapting the neuron's dynamic range to the level of correlation present in the visual input. We finally propose that the stimulus dependence of V1 RF Simpleness reflects a general rule of functional homeostasis common to many sensory systems [44][45][46][47]33 , which would ensure the adaptation of the network nonlinearities to ongoing changes in the statistical structure of the sensory input, according to optimal encoding principles 48 . …”

Tactile perception and working memory in rats and humans

“…Another distinction is the structure of the vibration. Although the studies in monkeys typically use regular, periodic skin deflections in the form of either a sinusoid or a pulse train (35), we opted for a stochastic stimulus composed of filtered noise (36). The choice was motivated by several factors.…”

An invariance principle for maintaining the operating point of a neuron

“…This relationship between the gain of a neuron and the standard deviation of the input is a well-established experimental observation (see, e.g. Kvale and Schreiner 2004;Bonin et al 2006;Maravall et al 2007; and references therein). Many standard distributions may be written in the form defined by Equation 7, including the normal distribution, the exponential, Laplace, doubly exponential distributions and the logistic distribution, and in general an infinity of forms for the function g is available.…”

“…Neurons early in sensory pathways are believed to adapt their responses to the statistics of their inputs in order to maximise their coding efficiency, output entropy or information rate (Atteave 1954;Barlow 1961;Laughlin 1981;Atick, 1992;van Hatteren 1992;DeWeese 1996;Dan et al 1996;Baddeley et al 1997;Smirnakis et al 1997;Wainwright 1999;Brenner et al 2000;Fairhall et al 2001;Maravall et al 2007). Other adaptive strategies have also been proposed for neurons later in sensory pathways (see, e.g.…”