Learning shapes cortical dynamics to enhance integration of relevant sensory input.

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

Learning and attention increase visual response selectivity through distinct mechanisms.

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance and across seconds when animals switch attention. While both phenomena occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales.

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.

Visual intracortical and transthalamic pathways carry distinct information to cortical areas.

Sensory processing involves information flow between neocortical areas, assumed to rely on direct intracortical projections. However, cortical areas may also communicate indirectly via higher-order nuclei in the thalamus, such as the pulvinar or lateral posterior nucleus (LP) in the visual system of rodents. The fine-scale organization and function of these cortico-thalamo-cortical pathways remains unclear. We find that responses of mouse LP neurons projecting to higher visual areas likely derive from feedforward input from primary visual cortex (V1) combined with information from many cortical and subcortical areas, including superior colliculus. Signals from LP projections to different higher visual areas are tuned to specific features of visual stimuli and their locomotor context, distinct from the signals carried by direct intracortical projections from V1. Thus, visual transthalamic pathways are functionally specific to their cortical target, different from feedforward cortical pathways, and combine information from multiple brain regions, linking sensory signals with behavioral context.

Cerebellar Contribution to Preparatory Activity in Motor Neocortex.

In motor neocortex, preparatory activity predictive of specific movements is maintained by a positive feedback loop with the thalamus. Motor thalamus receives excitatory input from the cerebellum, which learns to generate predictive signals for motor control. The contribution of this pathway to neocortical preparatory signals remains poorly understood. Here, we show that, in a virtual reality conditioning task, cerebellar output neurons in the dentate nucleus exhibit preparatory activity similar to that in anterolateral motor cortex prior to reward acquisition. Silencing activity in dentate nucleus by photoactivating inhibitory Purkinje cells in the cerebellar cortex caused robust, short-latency suppression of preparatory activity in anterolateral motor cortex. Our results suggest that preparatory activity is controlled by a learned decrease of Purkinje cell firing in advance of reward under supervision of climbing fiber inputs signaling reward delivery. Thus, cerebellar computations exert a powerful influence on preparatory activity in motor neocortex.

Cerebellar learning using perturbations.

The cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory cannot solve the credit assignment problem of processing a global movement evaluation into multiple cell-specific error signals. We identify a possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create eligibility traces and signal error changes guiding plasticity. Error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors (SGDEGE), predicts synaptic plasticity rules that apparently contradict the current consensus but were supported by plasticity experiments in slices from mice under conditions designed to be physiological, highlighting the sensitivity of plasticity studies to experimental conditions. We analyse the algorithm's convergence and capacity. Finally, we suggest SGDEGE may also operate in the basal ganglia.

Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex.

How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR-PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR-SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.

Time-invariant feed-forward inhibition of Purkinje cells in the cerebellar cortex in vivo.

KEY POINTS: We performed extracellular recording of pairs of interneuron-Purkinje cells in vivo. A single interneuron produces a substantial, short-lasting, inhibition of Purkinje cells. Feed-forward inhibition is associated with characteristic asymmetric cross-correlograms. In vivo, Purkinje cell spikes only depend on the most recent synaptic activity. ABSTRACT: Cerebellar molecular layer interneurons are considered to control the firing rate and spike timing of Purkinje cells. However, interactions between these cell types are largely unexplored in vivo. Using tetrodes, we performed simultaneous extracellular recordings of neighbouring Purkinje cells and molecular layer interneurons, presumably basket cells, in adult rats in vivo. The high levels of afferent synaptic activity encountered in vivo yield irregular spiking and reveal discharge patterns characteristic of feed-forward inhibition, thus suggesting an overlap of the afferent excitatory inputs between Purkinje cells and basket cells. Under conditions of intense background synaptic inputs, interneuron spikes exert a short-lasting inhibitory effect, delaying the following Purkinje cell spike by an amount remarkably independent of the Purkinje cell firing cycle. This effect can be explained by the short memory time of the Purkinje cell potential as a result of the intense incoming synaptic activity. Finally, we found little evidence for any involvement of the interneurons that we recorded with the cerebellar high-frequency oscillations promoting Purkinje cell synchrony. The rapid interactions between interneurons and Purkinje cells might be of particular importance in fine motor control because the inhibitory action of interneurons on Purkinje cells leads to deep cerebellar nuclear disinhibition and hence increased cerebellar output.

Ultra-rapid axon-axon ephaptic inhibition of cerebellar Purkinje cells by the pinceau.

Excitatory synaptic activity in the brain is shaped and balanced by inhibition. Because inhibition cannot propagate, it is often recruited with a synaptic delay by incoming excitation. Cerebellar Purkinje cells are driven by long-range excitatory parallel fiber inputs, which also recruit local inhibitory basket cells. The axon initial segment of each Purkinje cell is ensheathed by basket cell axons in a structure called the pinceau, which is largely devoid of chemical synapses. In mice, we found at the single-cell level that the pinceau mediates ephaptic inhibition of Purkinje cell firing at the site of spike initiation. The reduction of firing rate was synchronous with the presynaptic action potential, eliminating a synaptic delay and allowing granule cells to inhibit Purkinje cells without a preceding phase of excitation. Axon-axon ephaptic intercellular signaling can therefore mediate near-instantaneous feedforward and lateral inhibition.