Learning enhances encoding of time and temporal surprise in mouse primary sensory cortex

Ex vivo cortical circuits learn to predict and spontaneously replay temporal patterns

It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two temporal patterns using dual-optical stimulation. After 24-h of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors as revealed by larger responses when the expected stimulus was omitted compared to when it was present. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.

Stability of cross-sensory input to primary somatosensory cortex across experience

Merging information across sensory modalities is key to forming robust percepts, yet how the brain achieves this feat remains unclear. Recent studies report cross-modal influences in the primary sensory cortex, suggesting possible multisensory integration in the early stages of cortical processing. We test several hypotheses about the function of auditory influences on mouse primary somatosensory cortex (S1) using in vivo two-photon calcium imaging. We found sound-evoked spiking activity in an extremely small fraction of cells, and this sparse activity did not encode auditory stimulus identity. Moreover, S1 did not encode information about specific audio-tactile feature conjunctions. Auditory and audio-tactile stimulus encoding remained unchanged after both passive experience and reinforcement. These results suggest that while primary sensory cortex is plastic within its own modality, the influence of other modalities is remarkably stable and stimulus nonspecific.

Modulation of cortical representations of sensory and contextual information underlies aversive associative learning

Cortical neurons encode both sensory and contextual information, yet it remains unclear how experiences modulate these cortical representations. Here, we demonstrate that trace eyeblink conditioning (TEC), an aversive associative-learning paradigm linking conditioned (CS) with unconditioned stimuli (US), finely tunes cortical coding at both population and single-neuron levels. Initially, we show that the primary somatosensory cortex (S1) is necessary for TEC acquisition, as evidenced by local muscimol administration. At the population level, TEC enhances activity in a small subset (∼20%) of CS- or US-responsive primary neurons (rPNs) while diminishing activity in non-rPNs, including locomotion-tuned or unresponsive PNs. Crucially, TEC learning modulates the encoding of sensory versus contextual information in single rPNs: CS-responsive neurons become less responsive, while US-responsive neurons gain responses to CS. Moreover, we find that the cholinergic pathway, via nicotinic receptors, underlies TEC-induced modulations. These findings suggest that experiences dynamically tune cortical representations through cholinergic pathways.

Prediction-error signals in anterior cingulate cortex drive task-switching

Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states.

Stability of cross-sensory input to primary somatosensory cortex across experience

Merging information from across sensory modalities is key to forming robust, disambiguated percepts of the world, yet how the brain achieves this feat remains unclear. Recent observations of cross-modal influences in primary sensory cortical areas have suggested that multisensory integration may occur in the earliest stages of cortical processing, but the role of these responses is still poorly understood. We address these questions by testing several hypotheses about the possible functions served by auditory influences on the barrel field of mouse primary somatosensory cortex (S1) using in vivo 2-photon calcium imaging. We observed sound-evoked spiking activity in a small fraction of cells overall, and moreover that this sparse activity was insufficient to encode auditory stimulus identity; few cells responded preferentially to one sound or another, and a linear classifier trained to decode auditory stimuli from population activity performed barely above chance. Moreover S1 did not encode information about specific audio-tactile feature conjunctions that we tested. Our ability to decode auditory audio-tactile stimuli from neural activity remained unchanged after both passive experience and reinforcement. Collectively, these results suggest that while a primary sensory cortex is highly plastic with regard to its own modality, the influence of other modalities are remarkably stable and play a largely stimulus-non-specific role.

Goal-directed learning is multidimensional and accompanied by diverse and widespread changes in neocortical signaling

New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of response-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.

Dynamic gating of perceptual flexibility by non-classically responsive cortical neurons

The ability to flexibly respond to sensory cues in dynamic environments is essential to adaptive auditory-guided behaviors. Cortical spiking responses during behavior are highly diverse, ranging from reliable trial-averaged responses to seemingly random firing patterns. While the reliable responses of 'classically responsive' cells have been extensively studied for decades, the contribution of irregular spiking 'non-classically responsive' cells to behavior has remained underexplored despite their prevalence. Here, we show that flexible auditory behavior results from interactions between local auditory cortical circuits comprised of heterogeneous responses and inputs from secondary motor cortex. Strikingly, non-classically responsive neurons in auditory cortex were preferentially recruited during learning, specifically during rapid learning phases when the greatest gains in behavioral performance occur. Population-level decoding revealed that during rapid learning mixed ensembles comprised of both classically and non-classically responsive cells encode significantly more task information than homogenous ensembles of either type and emerge as a functional unit critical for learning. Optogenetically silencing inputs from secondary motor cortex selectively modulated non-classically responsive cells in the auditory cortex and impaired reversal learning by preventing the remapping of a previously learned stimulus-reward association. Top-down inputs orchestrated highly correlated non-classically responsive ensembles in sensory cortex providing a unique task-relevant manifold for learning. Thus, non-classically responsive cells in sensory cortex are preferentially recruited by top-down inputs to enable neural and behavioral flexibility.

Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

Transient enhancement of stimulus-evoked activity in neocortex during sensory learning

Synaptic potentiation has been linked to learning in sensory cortex, but the connection between this potentiation and increased sensory-evoked neural activity is not clear. Here, we used longitudinal in vivo Ca2+ imaging in the barrel cortex of awake mice to test the hypothesis that increased excitatory synaptic strength during the learning of a whisker-dependent sensory-association task would be correlated with enhanced stimulus-evoked firing. To isolate stimulus-evoked responses from dynamic, task-related activity, imaging was performed outside of the training context. Although prior studies indicate that multiwhisker stimuli drive robust subthreshold activity, we observed sparse activation of L2/3 pyramidal (Pyr) neurons in both control and trained mice. Despite evidence for excitatory synaptic strengthening at thalamocortical and intracortical synapses in this brain area at the onset of learning-indeed, under our imaging conditions thalamocortical axons were robustly activated-we observed that L2/3 Pyr neurons in somatosensory (barrel) cortex displayed only modest increases in stimulus-evoked activity that were concentrated at the onset of training. Activity renormalized over longer training periods. In contrast, when stimuli and rewards were uncoupled in a pseudotraining paradigm, stimulus-evoked activity in L2/3 Pyr neurons was significantly suppressed. These findings indicate that sensory-association training but not sensory stimulation without coupled rewards may briefly enhance sensory-evoked activity, a phenomenon that might help link sensory input to behavioral outcomes at the onset of learning.

Ex Vivo Cortical Circuits Learn to Predict and Spontaneously Replay Temporal Patterns

It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two specific temporal patterns using dual-optical stimulation. After 24-hours of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.

Expression characteristics and potential function of non-coding RNA in mouse cortical cells

Non-coding RNAs (ncRNAs) play essential regulatory functions in various physiological and pathological processes in the brain. To systematically characterize the ncRNA profile in cortical cells, we downloaded single-cell SMART-Seq v4 data of mouse cerebral cortex. Our results revealed that the ncRNAs alone are sufficient to define the identity of most cortical cell types. We identified 1,600 ncRNAs that exhibited cell type specificity, even yielding to distinguish microglia from perivascular macrophages with ncRNA. Moreover, we characterized cortical layer and region specific ncRNAs, in line with the results by spatial transcriptome (ST) data. By constructing a co-expression network of ncRNAs and protein-coding genes, we predicted the function of ncRNAs. By integrating with genome-wide association studies data, we established associations between cell type-specific ncRNAs and traits related to neurological disorders. Collectively, our study identified differentially expressed ncRNAs at multiple levels and provided the valuable resource to explore the functions and dysfunctions of ncRNAs in cortical cells.

Direct contribution of the sensory cortex to the judgment of stimulus duration

Decision making frequently depends on monitoring the duration of sensory events. To determine whether, and how, the perception of elapsed time derives from the neuronal representation of the stimulus itself, we recorded and optogenetically modulated vibrissal somatosensory cortical activity as male rats judged vibration duration. Perceived duration was dilated by optogenetic excitation. A second set of rats judged vibration intensity; here, optogenetic excitation amplified the intensity percept, demonstrating sensory cortex to be the common gateway both to time and to stimulus feature processing. A model beginning with the membrane currents evoked by vibrissal and optogenetic drive and culminating in the representation of perceived time successfully replicated rats' choices. Time perception is thus as deeply intermeshed within the sensory processing pathway as is the sense of touch itself, suggesting that the experience of time may be further investigated with the toolbox of sensory coding.

Precise sound characteristics drive plasticity in the primary auditory cortex with VNS-sound pairing

Introduction

Repeatedly pairing a tone with vagus nerve stimulation (VNS) alters frequency tuning across the auditory pathway. Pairing VNS with speech sounds selectively enhances the primary auditory cortex response to the paired sounds. It is not yet known how altering the speech sounds paired with VNS alters responses. In this study, we test the hypothesis that the sounds that are presented and paired with VNS will influence the neural plasticity observed following VNS-sound pairing.

Methods

To explore the relationship between acoustic experience and neural plasticity, responses were recorded from primary auditory cortex (A1) after VNS was repeatedly paired with the speech sounds 'rad' and 'lad' or paired with only the speech sound 'rad' while 'lad' was an unpaired background sound.

Results

Pairing both sounds with VNS increased the response strength and neural discriminability of the paired sounds in the primary auditory cortex. Surprisingly, pairing only 'rad' with VNS did not alter A1 responses.

Discussion

These results suggest that the specific acoustic contrasts associated with VNS can powerfully shape neural activity in the auditory pathway. Methods to promote plasticity in the central auditory system represent a new therapeutic avenue to treat auditory processing disorders. Understanding how different sound contrasts and neural activity patterns shape plasticity could have important clinical implications.

Learning induced neuronal identity switch in the superficial layers of the primary somatosensory cortex

During learning, multi-dimensional inputs are integrated within the sensory cortices. However, the strategies by which the sensory cortex employs to achieve learning remains poorly understood. We studied the sensory cortical neuronal coding of trace eyeblink conditioning (TEC) in head-fixed, freely running mice, where whisker deflection was used as a conditioned stimulus (CS) and an air puff to the cornea delivered after an interval was used as unconditioned stimulus (US). After training, mice learned the task with a set of stereotypical behavioral changes, most prominent ones include prolonged closure of eyelids, and increased reverse running between CS and US onset. The local blockade of the primary somatosensory cortex (S1) activities with muscimol abolished the behavior learning suggesting that S1 is required for the TEC. In naive animals, based on the response properties to the CS and US, identities of the small proportion (~20%) of responsive primary neurons (PNs) were divided into two subtypes: CR (i.e. CS-responsive) and UR neurons (i.e. US-responsive). After animals learned the task, identity of CR and UR neurons changed: while the CR neurons are less responsive to CS, UR neurons gain responsiveness to CS, a new phenomenon we defined as 'learning induced neuronal identity switch (LINIS)'. To explore the potential mechanisms underlying LINIS, we found that systemic and local (i.e. in S1) administration of the nicotinic receptor antagonist during TEC training blocked the LINIS, and concomitantly disrupted the behavior learning. Additionally, we monitored responses of two types of cortical interneurons (INs) and observed that the responses of the somatostatin-expressing (SST), but not parvalbumin-expressing (PV) INs are negatively correlated with the learning performance, suggesting that SST-INs contribute to the LINIS. Thus, we conclude that L2/3 PNs in S1 encode perceptual learning by LINIS like mechanisms, and cholinergic pathways and cortical SST interneurons are involved in the formation of LINIS.

Optogenetic manipulation of inhibitory interneurons can be used to validate a model of spatiotemporal sequence learning

The brain uses temporal information to link discrete events into memory structures supporting recognition, prediction, and a wide variety of complex behaviors. It is still an open question how experience-dependent synaptic plasticity creates memories including temporal and ordinal information. Various models have been proposed to explain how this could work, but these are often difficult to validate in a living brain. A recent model developed to explain sequence learning in the visual cortex encodes intervals in recurrent excitatory synapses and uses a learned offset between excitation and inhibition to generate precisely timed "messenger" cells that signal the end of an instance of time. This mechanism suggests that the recall of stored temporal intervals should be particularly sensitive to the activity of inhibitory interneurons that can be easily targeted in vivo with standard optogenetic tools. In this work we examined how simulated optogenetic manipulations of inhibitory cells modifies temporal learning and recall based on these mechanisms. We show that disinhibition and excess inhibition during learning or testing cause characteristic errors in recalled timing that could be used to validate the model in vivo using either physiological or behavioral measurements.

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.