Multiple Timescales Account for Adaptive Responses across Sensory Cortices

Abstract

Sensory systems encounter remarkably diverse stimuli in the external environment. Natural stimuli exhibit timescales and amplitudes of variation that span a wide range. Mechanisms of adaptation, a ubiquitous feature of sensory systems, allow for the accommodation of this range of scales. Are there common rules of adaptation across different sensory modalities? We measured the membrane potential responses of individual neurons in the visual, somatosensory, and auditory cortices of male and female mice to discrete, punctate stimuli delivered at a wide range of fixed and nonfixed frequencies. We find that the adaptive profile of the response is largely preserved across these three areas, exhibiting attenuation and responses to the cessation of stimulation, which are signatures of response to changes in stimulus statistics. We demonstrate that these adaptive responses can emerge from a simple model based on the integration of fixed filters operating over multiple time scales.

Inverted central auditory hierarchies for encoding local intervals and global temporal patterns

“…There is a long history of studying cortical adaptation to stimulus history in the auditory system, most notably with paradigms related to stimulus-specific adaptation (SSA), in which sound-evoked firing rates are modulated according to the probability of stimulus presentation. [8][9][10] Like the protocol we used here, SSA is characterized by contrasting the neural response to an identical stimulus that differs only according to global context. While SSA has historically been studied with tones of varying frequency, recent work has also relied on silent gaps in broadband stimuli, demonstrating that it cannot be explained solely as adaptation in narrowly tuned frequency channels.…”

Cortical Microcircuit Mechanisms of Mismatch Negativity and Its Underlying Subcomponents

“…Further, much of the SSA and DD work on which it is based, for instance, come from different sensory modalities (e.g., visual, auditory, and somatosensory), which may (Latimer et al, 2019) or may not (Kremlacek et al, 2016) exhibit distinct local circuitry for processing context. Further, distinct subpopulations exist within the SST-interneuron class which exhibit net inhibitory or net disinhibitory effects on PYRs (e.g., layer 4 × 94 cells; Muñoz et al, 2017).…”

Capturing Multiple Timescales of Adaptation to Second-Order Statistics With Generalized Linear Models: Gain Scaling and Fractional Differentiation

“…This approach shows that at least part of the spectrum of adaptive behaviors to stimuli with time-varying characteristics can be captured through a single linear spike history feedback. Effective alternative approaches have captured time-varying context with a multiplicity of filters acting on different timescales of the stimulus alone (Kass and Ventura, 2001;McFarland et al, 2013;Qian et al, 2018;Latimer et al, 2019a).…”

“…Previous work has shown the utility of finding linear features that can explain the spiking behavior of HH models (Agüera y Arcas and Weber and Pillow, 2017). Unlike simple linear/non-linear models, GLMs also incorporate a dependence on the history of activity, potentially providing a helpful interpretative framework for adaptation (Mease et al, 2014;Latimer et al, 2019a). We therefore fit GLMs to spike trains generated from HH neurons.…”