The Virtual-Environment-Foraging Task enables rapid training and single-trial metrics of rule acquisition and reversal in head-fixed mice

What does the mean mean? A simple test for neuroscience

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

Differential optogenetic activation of the auditory midbrain in freely moving behaving mice

Introduction

In patients with severe auditory impairment, partial hearing restoration can be achieved by sensory prostheses for the electrical stimulation of the central nervous system. However, these state-of-the-art approaches suffer from limited spectral resolution: electrical field spread depends on the impedance of the surrounding medium, impeding spatially focused electrical stimulation in neural tissue. To overcome these limitations, optogenetic activation could be applied in such prostheses to achieve enhanced resolution through precise and differential stimulation of nearby neuronal ensembles. Previous experiments have provided a first proof for behavioral detectability of optogenetic activation in the rodent auditory system, but little is known about the generation of complex and behaviorally relevant sensory patterns involving differential activation.

Methods

In this study, we developed and behaviorally tested an optogenetic implant to excite two spatially separated points along the tonotopy of the murine inferior colliculus (ICc).

Results

Using a reward based operant Go/No-Go paradigm, we show that differential optogenetic activation of a sub-cortical sensory pathway is possible and efficient. We demonstrate how animals which were previously trained in a frequency discrimination paradigm (a) rapidly respond to either sound or optogenetic stimulation, (b) generally detect optogenetic stimulation of two different neuronal ensembles, and (c) discriminate between them.

Discussion

Our results demonstrate that optogenetic excitatory stimulation at different points of the ICc tonotopy elicits a stable response behavior over time periods of several months. With this study, we provide the first proof of principle for sub-cortical differential stimulation of sensory systems using complex artificial cues in freely moving animals.

Shared and specialized coding across posterior cortical areas for dynamic navigation decisions

Animals adaptively integrate sensation, planning, and action to navigate toward goal locations in ever-changing environments, but the functional organization of cortex supporting these processes remains unclear. We characterized encoding in approximately 90,000 neurons across the mouse posterior cortex during a virtual navigation task with rule switching. The encoding of task and behavioral variables was highly distributed across cortical areas but differed in magnitude, resulting in three spatial gradients for visual cue, spatial position plus dynamics of choice formation, and locomotion, with peaks respectively in visual, retrosplenial, and parietal cortices. Surprisingly, the conjunctive encoding of these variables in single neurons was similar throughout the posterior cortex, creating high-dimensional representations in all areas instead of revealing computations specialized for each area. We propose that, for guiding navigation decisions, the posterior cortex operates in parallel rather than hierarchically, and collectively generates a state representation of the behavior and environment, with each area specialized in handling distinct information modalities.