Spike trains of retinal ganglion cells (RGCs) are the sole source of visual information to the brain; and understanding how the ϳ20 RGC types in mammalian retinae respond to diverse visual features and events is fundamental to understanding vision. Suppressed-bycontrast (SbC) RGCs stand apart from all other RGC types in that they reduce rather than increase firing rates in response to light increments (ON) and decrements (OFF). Here, we genetically identify and morphologically characterize SbC-RGCs in mice, and target them for patch-clamp recordings under two-photon guidance. We find that strong ON inhibition (glycine Ͼ GABA) outweighs weak ON excitation, and that inhibition (glycine Ͼ GABA) coincides with decreases in excitation at light OFF. These input patterns explain the suppressive spike responses of SbC-RGCs, which are observed in dim and bright light conditions. Inhibition to SbC-RGC is driven by rectified receptive field subunits, leading us to hypothesize that SbC-RGCs could signal pattern-independent changes in the retinal image. Indeed, we find that shifts of random textures matching saccade-like eye movements in mice elicit robust inhibitory inputs and suppress spiking of SbC-RGCs over a wide range of texture contrasts and spatial frequencies. Similarly, stimuli based on kinematic analyses of mouse blinking consistently suppress SbC-RGC spiking. Receiver operating characteristics show that SbC-RGCs are reliable indicators of self-generated visual stimuli that may contribute to central processing of blinks and saccades.
Both generalized arousal and engagement in a specific task influence sensory neural processing. To isolate effects of these state variables in the auditory system, we recorded single-unit activity from primary auditory cortex (A1) and inferior colliculus (IC) of ferrets during a tone detection task, while monitoring arousal via changes in pupil size. We used a generalized linear model to assess the influence of task engagement and pupil size on sound-evoked activity. In both areas, these two variables affected independent neural populations. Pupil size effects were more prominent in IC, while pupil and task engagement effects were equally likely in A1. Task engagement was correlated with larger pupil; thus, some apparent effects of task engagement should in fact be attributed to fluctuations in pupil size. These results indicate a hierarchy of auditory processing, where generalized arousal enhances activity in midbrain, and effects specific to task engagement become more prominent in cortex.
The brain’s representation of sound is influenced by multiple aspects of internal behavioral state. Following engagement in an auditory discrimination task, both generalized arousal and task-specific control signals can influence auditory processing. To isolate effects of these state variables on auditory processing, we recorded single-unit activity from primary auditory cortex (A1) and the inferior colliculus (IC) of ferrets as they engaged in a go/no-go tone detection task while simultaneously monitoring arousal via pupillometry. We used a generalized linear model to isolate the contributions of task engagement and arousal on spontaneous and evoked neural activity. Fluctuations in pupil-indexed arousal were correlated with task engagement, but these two variables could be dissociated in most experiments. In both A1 and IC, individual units could be modulated by task and/or arousal, but the two state variables affected independent neural populations. Arousal effects were more prominent in IC, while arousal and engagement effects occurred with about equal frequency in A1. These results indicate that some changes in neural activity attributed to task engagement in previous studies should in fact be attributed to global fluctuations in arousal. Arousal effects also explain some persistent changes in neural activity observed in passive conditions post-behavior. Together, these results indicate a hierarchy in the auditory system, where generalized arousal enhances activity in the midbrain and cortex, while task-specific changes in neural coding become more prominent in cortex.
In addition to encoding sound stimulus features, activity in primary auditory cortex (A1) is modulated by non-sensory behavioral state variables, including arousal. Here, we investigated how arousal, measured by pupil size, influences stimulus discriminability in A1. To do this, we recorded from populations of A1 neurons in awake animals while presenting a diverse set of natural sound stimuli. In contrast to previous work, the large stimulus set allowed us to investigate effects of arousal across a wide range of sensory response space. Arousal consistently increased evoked response magnitude and reduced pairwise noise correlations. On average, these changes improved the accuracy of the neural code. However, effects varied across stimuli; neural coding was most improved for areas of the sensory space where noise correlations interfered with the sensory discrimination axis. We also found that first-order modulation of evoked responses and second-order modulation of correlated variability acted on distinct neural populations and timescales, suggesting that arousal interacts with multiple circuits underlying activity in A1.
Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.
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