Mechanisms underlying gain modulation in the cortex

Augmenting flexibility: mutual inhibition between inhibitory neurons expands functional diversity

Recent advances in microcircuit analysis of nervous systems have revealed a plethora of mutual connections between inhibitory interneurons across many different species and brain regions. The abundance of these mutual connections has not been fully explained. Strikingly, we show that neural circuits with mutually inhibitory connections are able to rapidly and flexibly switch between distinct functions. That is, multiple functions coexist for a single set of synaptic weights. Here, we develop a theoretical framework to explain how inhibitory recurrent circuits give rise to this flexibility and show that mutual inhibition doubles the number of cusp bifurcations in small neural circuits. As a concrete example, we study a class of functional motifs we call coupled recurrent inhibitory and recurrent excitatory loops (CRIRELs). These CRIRELs have the advantage of being both multi-functional and controllable, performing a plethora of functions, including decisions, memory, toggle, and so forth. Finally, we demonstrate how mutual inhibition maximizes storage capacity for larger networks.

Goal-directed action preparation in humans entails a mixture of corticospinal neural computations

The seemingly effortless ability of humans to transition from thinking about actions to initiating them relies on sculpting corticospinal (CS) output from the primary motor cortex. The present study tested whether canonical additive and multiplicative neural computations, well-described in sensory systems, generalize to the CS pathway during human action preparation. We used non-invasive brain stimulation to measure CS input-output across varying action preparation contexts during instructed-delay finger response tasks. Goal-directed action preparation was marked by increased multiplicative gain of CS projections to task-relevant muscles and additive suppression of CS projections to non-selected and task-irrelevant muscles. Individuals who modulated CS gain to a greater extent were faster to initiate prepared responses. Our findings provide physiological evidence of combined additive suppression and gain modulation in the human motor system. We propose that these computations support action preparation by enhancing the contrast between selected motor representations and surrounding background activity to facilitate response selection and execution. KEY POINTS: Neural computations determine what information is transmitted through brain circuits. We investigated whether the motor system uses computations similar to those observed in sensory systems by non-invasively stimulating the corticospinal pathway in humans during goal-directed action preparation. We discovered physiological evidence indicating that corticospinal projections to behaviourally relevant muscles exhibit non-linear gain computations, whereas projections to behaviourally irrelevant muscles exhibit linear suppression. Our findings suggest that certain computational principles generalize to the human motor system and serve to enhance the contrast between relevant and background neural activity. These results indicate that neural computations during goal-directed action preparation may support motor control by increasing signal-to-noise within the corticospinal pathway.

Goal-directed action preparation in humans entails a mixture of corticospinal neural computations

The seemingly effortless ability of humans to transition from thinking about actions to initiating them relies on sculpting corticospinal output from primary motor cortex. This study tested whether canonical additive and multiplicative neural computations, well-described in sensory systems, generalize to the corticospinal pathway during human action preparation. We used non-invasive brain stimulation to measure corticospinal input-output across varying action preparation contexts during instructed-delay finger response tasks. Goal-directed action preparation was marked by increased multiplicative gain of corticospinal projections to task-relevant muscles and additive suppression of corticospinal projections to non-selected and task-irrelevant muscles. Individuals who modulated corticospinal gain to a greater extent were faster to initiate prepared responses. Our findings provide physiological evidence of combined additive suppression and gain modulation in the human motor system. We propose these computations support action preparation by enhancing the contrast between selected motor representations and surrounding background activity to facilitate response selection and execution.

Interneuronal modulations as a functional switch for cortical computations: mechanisms and implication for disease

Understanding cortical inhibition and its diverse roles remains a key challenge in neurophysiological research. Traditionally, inhibition has been recognized for controlling the stability and rhythmicity of network dynamics, or refining the spatiotemporal properties of cortical representations. In this perspective, we propose that specific types of interneurons may play a complementary role, by modulating the computational properties of neural networks. We review experimental and theoretical evidence, mainly from rodent sensory cortices, that supports this view. Additionally, we explore how dysfunctions in these interneurons may disrupt the network's ability to switch between computational modes, impacting the flexibility of cortical processing and potentially contributing to various neurodevelopmental and psychiatric disorders.

Recurrent activity propagates through labile ensembles in macaque dorsolateral prefrontal microcircuits

Human and non-human primate studies clearly implicate the dorsolateral prefrontal cortex (dlPFC) as critical for advanced cognitive functions.1,2 It is thought that intracortical synaptic architectures within the dlPFC are the integral neurobiological substrate that gives rise to these processes.3,4,5,6,7 In the prevailing model, each cortical column makes up one fundamental processing unit composed of dense intrinsic connectivity, conceptualized as the "canonical" cortical microcircuit.3,8 Each cortical microcircuit receives sensory and cognitive information from upstream sources, which are represented by sustained activity within the microcircuit, referred to as persistent or recurrent activity.4,9 Via recurrent connections within the microcircuit, activity propagates for a variable length of time, thereby allowing temporary storage and computations to occur locally before ultimately passing a transformed representation to a downstream output.4,5,10 Competing theories regarding how microcircuit activity is coordinated have proven difficult to reconcile in vivo, where intercortical and intracortical computations cannot be fully dissociated.5,9,11,12 Here, using high-density calcium imaging of macaque dlPFC, we isolated intracortical computations by interrogating microcircuit networks ex vivo. Using peri-sulcal stimulation to evoke recurrent activity in deep layers, we found that activity propagates through stochastically assembled intracortical networks wherein orderly, predictable, low-dimensional collective dynamics arise from ensembles with highly labile cellular memberships. Microcircuit excitability covaried with individual cognitive performance, thus anchoring heuristic models of abstract cortical functions within quantifiable constraints imposed by the underlying synaptic architecture. Our findings argue against engram or localist architectures, together demonstrating that generation of high-fidelity population-level signals from distributed, labile networks is an intrinsic feature of dlPFC microcircuitry.

Glia-like taste cells mediate an intercellular mode of peripheral sweet adaptation

The sense of taste generally shows diminishing sensitivity to prolonged sweet stimuli, referred to as sweet adaptation. Yet, its mechanistic landscape remains incomplete. Here, we report that glia-like type I cells provide a distinct mode of sweet adaptation via intercellular crosstalk with chemosensory type II cells. Using the microfluidic-based intravital tongue imaging system, we found that sweet adaptation is facilitated along the synaptic transduction from type II cells to gustatory afferent nerves, while type I cells display temporally delayed and prolonged activities. We identified that type I cells receive purinergic input from adjacent type II cells via P2RY2 and provide inhibitory feedback to the synaptic transduction of sweet taste. Aligning with our cellular-level findings, purinergic activation of type I cells attenuated sweet licking behavior, and P2RY2 knockout mice showed decelerated adaptation behavior. Our study highlights a veiled intercellular mode of sweet adaptation, potentially contributing to the efficient encoding of prolonged sweetness.

Cross-education: motor unit adaptations mediate the strength increase in non-trained muscles following 8 weeks of unilateral resistance training

Introduction

Early increases in muscle strength following unilateral resistance training are typically accompanied by strength gains in the contralateral untrained muscles, a phenomenon known as cross-education. However, the specific motor unit adaptations responsible for this gain transfer remain poorly understood. To address this gap, we recorded myoelectrical activity from the biceps brachii using high-density electromyography.

Methods

Nine participants performed 8-week unilateral resistance training and were compared to nine control individuals who did no intervention. Discharge characteristics of longitudinally tracked motor units were assessed during maximal voluntary contractions and isometric ramp contractions at 35% and 70% of the maximal voluntary force (MVF) at baseline (T0), 4 weeks (T1), and 8 weeks (T2) post-intervention.

Results

MVF increased by 7% in untrained muscles at T1 and 10% at T2 (p < 0.05). These gains were accompanied by significant decreases in motor unit recruitment thresholds (p < 0.01) and higher net discharge rate (i.e., gain in discharge rate from recruitment to peak) following intervention (p < 0.05). Trained muscles presented greater MVF (+11%, T1; +19%, T2) with similar motor unit adaptations, including a lower recruitment threshold (p < 0.01) and a higher net discharge rate (p < 0.01).

Discussion

Our findings indicate that higher strength in untrained muscles is associated with a higher net discharge rate, implying a greater spinal motoneuron output to muscles. The present results underscore the role of motor unit adaptations in the transfer of strength gains to non-trained muscles, offering novel insights into the neural mechanisms underlying cross-education.

Rastermap: a discovery method for neural population recordings

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time and noticing patterns of activity related to ongoing stimuli or behaviors. With the advent of large-scale recordings, such close observation of data has become difficult. To find patterns in large-scale neural data, we developed 'Rastermap', a visualization method that displays neurons as a raster plot after sorting them along a one-dimensional axis based on their activity patterns. We benchmarked Rastermap on realistic simulations and then used it to explore recordings of tens of thousands of neurons from mouse cortex during spontaneous, stimulus-evoked and task-evoked epochs. We also applied Rastermap to whole-brain zebrafish recordings; to wide-field imaging data; to electrophysiological recordings in rat hippocampus, monkey frontal cortex and various cortical and subcortical regions in mice; and to artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

Reward and Efficacy Modulate the Rate of Anticipatory Pupil Dilation

Pupil size is a well-established marker of cognitive effort, with greater efforts leading to larger pupils. This is particularly true for pupil size during task performance, whereas findings on anticipatory effort triggered by a cue stimulus are less consistent. For example, a recent report by Frömer et al. found that in a cued-Stroop task, behavioral performance and electrophysiological markers of preparatory effort allocation were modulated by cued reward and 'efficacy' (the degree to which rewards depended on good performance), but pupil size did not show a comparable pattern. Here, we conceptually replicated this study, employing an alternative approach to the pupillometry analyses. In line with previous findings, we found no modulation of absolute pupil size in the cue-to-target interval. Instead, we observed a significant difference in the rate of pupil dilation in anticipation of the target: pupils dilated more rapidly for high-reward trials in which rewards depended on good performance. This was followed by a significant difference in absolute pupil size within the first hundreds of milliseconds following Stroop stimulus onset, likely reflecting a lagging effect of anticipatory effort allocation. Finally, the slope of pupil dilation was significantly correlated with behavioral response times, and this association was strongest for the high-reward, high-efficacy trials, further supporting that the rate of anticipatory pupil dilation reflects anticipatory effort. We conclude that pupil size is modulated by anticipatory effort, but in a highly temporally-specific manner, which is best reflected by the rate of dilation in the moments just prior to stimulus onset.

Autism spectrum disorder variation as a computational trade-off via dynamic range of neuronal population responses

Individuals diagnosed with autism spectrum disorder (ASD) show neural and behavioral characteristics differing from the neurotypical population. This may stem from a computational principle that relates inference and computational dynamics to the dynamic range of neuronal population responses, reflecting the signal levels for which the system is responsive. In the present study, we showed that an increased dynamic range (IDR), indicating a gradual response of a neuronal population to changes in input, accounts for neural and behavioral variations in individuals diagnosed with ASD across diverse tasks. We validated the model with data from finger-tapping synchronization, orientation reproduction and global motion coherence tasks. We suggested that increased heterogeneity in the half-activation point of individual neurons may be the biological mechanism underlying the IDR in ASD. Taken together, this model provides a proof of concept for a new computational principle that may account for ASD and generates new testable and distinct predictions regarding its behavioral, neural and biological foundations.

Increased visual alpha-band activity during self-paced finger tapping does not affect early visual stimulus processing

Alpha-band activity is thought to be involved in orchestrating neural processing within and across brain regions relevant to various functions such as perception, cognition, and motor activity. Across different studies, attenuated alpha-band activity has been linked to increased neural excitability. Yet, there have been conflicting results concerning the consequences of alpha-band modulations for early sensory processing. We here examined whether movement-related alterations in visual alpha-band activity affected the early sensory processing of visual stimuli. For this purpose, in an EEG experiment, participants were engaged in a voluntary finger-tapping task while passively viewing flickering dots. We found extensive and expected movement-related amplitude modulations of motor alpha- and beta-band activity with event-related-desynchronization (ERD) before and during, and event-related-synchronization (ERS) after single voluntary finger taps. Crucially, while a visual alpha-band ERS accompanied the motor alpha-ERD before and during each finger tap, flicker-evoked Steady-State-Visually-Evoked-Potentials (SSVEPs), as a marker of early visual sensory gain, were not modulated in amplitude. As early sensory stimulus processing was unaffected by amplitude-modulated visual alpha-band activity, this argues against the idea that alpha-band activity represents a mechanism by which early sensory gain modulation is implemented. The distinct neural dynamics of visual alpha-band activity and early sensory processing may point to distinct and multiplexed neural selection processes in visual processing.

If you please, draw me a neuron - linking evolutionary tinkering with human neuron evolution

Animal speciation often involves novel behavioral features that rely on nervous system evolution. Human-specific brain features have been proposed to underlie specialized cognitive functions and to be linked, at least in part, to the evolution of synapses, neurons, and circuits of the cerebral cortex. Here, we review recent results showing that, while the human cortex is composed of a repertoire of cells that appears to be largely similar to the one found in other mammals, human cortical neurons do display specialized features at many levels, from gene expression to intrinsic physiological properties. The molecular mechanisms underlying human species-specific neuronal features remain largely unknown but implicate hominid-specific gene duplicates that encode novel molecular modifiers of neuronal function. The identification of human-specific genetic modifiers of neuronal function brings novel insights on brain evolution and function and, could also provide new insights on human species-specific vulnerabilities to brain disorders.

GABAA receptors in epilepsy: Elucidating phenotypic divergence through functional analysis of genetic variants

Normal brain function requires a tightly regulated balance between excitatory and inhibitory neurotransmissions. γ-Aminobutyric acid type A (GABAA) receptors represent the major class of inhibitory ion channels in the mammalian brain. Dysregulation of these receptors and/or their associated pathways is strongly implicated in the pathophysiology of epilepsy. To date, hundreds of different GABAA receptor subunit variants have been associated with epilepsy, making them a prominent cause of genetically linked epilepsy. While identifying these genetic variants is crucial for accurate diagnosis and effective genetic counselling, it does not necessarily lead to improved personalised treatment options. This is because the identification of a variant does not reveal how the function of GABAA receptors is affected. Genetic variants in GABAA receptor subunits can cause complex changes to receptor properties resulting in various degrees of gain-of-function, loss-of-function or a combination of both. Understanding how variants affect the function of GABAA receptors therefore represents an important first step in the ongoing development of precision therapies. Furthermore, it is important to ensure that functional data are produced using methodologies that allow genetic variants to be classified using clinical guidelines such as those developed by the American College of Medical Genetics and Genomics. This article will review the current knowledge in the field and provide recommendations for future functional analysis of genetic GABAA receptor variants.

Serotonin Inhibition of Claustrum Projection Neurons: Ionic Mechanism, Receptor Subtypes and Consequences for Claustrum Computation

The claustrum is a small but densely interconnected brain structure that is innervated by axons containing serotonin (5-HT), a neuromodulator that has been implicated in control of sleep and in the actions of psychedelic drugs. However, little is known about how 5-HT influences the claustrum. We have combined whole-cell patch-clamp measurements of ionic currents, flash photolysis, and receptor pharmacology to characterize the 5-HT responses of individual claustral projection neurons (PNs) in mouse brain slices. Serotonin application elicited a long-lasting outward current in claustral PNs. This current was due to an increase in membrane permeability to K+ ions and was mediated mainly by the type 1A 5-HT receptor (5-HTR-1A). The 5-HT-induced K+ current hyperpolarized, and thereby inhibited, the PNs by reducing action potential firing. Focal uncaging of 5-HT revealed that inhibitory 5-HTR-1As were located at both the soma and dendrites of PNs. We conclude that 5-HT creates a net inhibition in the claustrum, an action that should decrease claustrum sensitivity to excitatory input from other brain areas and thereby contribute to 5-HT action in the brain.

More Than a Small Brain: The Importance of Studying Neural Function during Development

The nervous system contains complex circuits comprising thousands of cell types and trillions of connections. Here, we discuss how the field of "developmental systems neuroscience" combines the molecular and genetic perspectives of developmental neuroscience with the (typically adult-focused) functional perspective of systems neuroscience. This combination of approaches is critical to understanding how a handful of cells eventually produce the wide range of behaviors necessary for survival. Functional circuit development typically lags behind neural connectivity, leading to intermediate stages of neural activity that are either not seen in adults or, if present, are considered pathophysiological. Developmental systems neuroscience examines these intermediate stages of neural activity, mapping out the critical phases and inflection points of neural circuit function to understand how neural activity and behavior emerge across development. Beyond understanding typical development, this approach provides invaluable insight into the pathophysiology of neurodevelopmental disorders by identifying when and how functional development diverges between health and disease. We argue that developmental systems neuroscience will identify important periods of neural development, reveal novel therapeutic windows for treatment, and set the stage to answer fundamental questions about the brain in health and disease.

Running modulates primate and rodent visual cortex differently

When mice run, activity in their primary visual cortex (V1) is strongly modulated. This observation has altered conceptions of a brain region assumed to be a passive image processor. Extensive work has followed to dissect the circuits and functions of running-correlated modulation. However, it remains unclear whether visual processing in primates might similarly change during locomotion. We therefore measured V1 activity in marmosets while they viewed stimuli on a treadmill. In contrast to mouse, running-correlated modulations of marmoset V1 were small and tended to be slightly suppressive. Population-level analyses revealed trial-to-trial fluctuations of shared gain across V1 in both species, but while strongly correlated with running in mice, gain modulations were smaller and more often negatively correlated with running in marmosets. Thus, population-wide fluctuations of V1 may reflect a common feature of mammalian visual cortical function, but important quantitative differences point to distinct consequences for the relation between vision and action in primates versus rodents.

Noradrenergic alpha-2a receptor stimulation enhances prediction error signaling and updating of attention sets in anterior cingulate cortex and striatum

The noradrenergic system is believed to support behavioral flexibility. A possible source mediating improved flexibility are α2A adrenoceptors (α2AR) in prefrontal cortex (PFC) or the anterior cingulate cortex (ACC). We tested this hypothesis by stimulating α2ARs using Guanfacine during attentional set shifting in male nonhuman primates. We found that α2AR stimulation improved learning from errors and updating attention sets. Neural recordings in the ACC, dorsolateral PFC, and the striatum showed that α2AR stimulation selectively enhanced neural signaling of prediction errors in neurons of the ACC and the striatum, but not in dlPFC. This modulation was accompanied by enhanced encoding of attended target features and particularly apparent in putative fast-spiking interneurons, pointing to an interneuron mediated mechanism of α2AR action. These results reveal that α2A receptors are part of the causal chain of flexibly updating attention sets through an enhancement of outcomes and prediction error signaling in ACC and striatum.

Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation

Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.

Contrast gain control is a reparameterization of a population response curve

Neurons in primary visual cortex (area V1) adapt in varying degrees to the average contrast of the environment, suggesting that the representation of visual stimuli may interact with the state of cortical gain control in complex ways. To investigate this possibility, we measured and analyzed the responses of neural populations in mouse V1 to visual stimuli as a function of contrast in different environments, each characterized by a unique distribution of contrast values. Our findings reveal that, for a fixed stimulus, the population response can be described by a vector function r(gec), where the gain ge is a decreasing function of the mean contrast of the environment. Thus, gain control can be viewed as a reparameterization of a population response curve, which is invariant across environments. Different stimuli are mapped to distinct curves, all originating from a common origin, corresponding to a zero-contrast response. Altogether, our findings provide a straightforward, geometric interpretation of contrast gain control at the population level and show that changes in gain are well matched among members of a population.NEW & NOTEWORTHY The authors study the responses of neural populations in mouse primary visual cortex as a function of stimulus contrast. Measurements are performed in different "environments," each characterized by a different distribution of contrast values. They find that responses across environments can be viewed as a reparameterization of a single contrast-response curve, offering a simple, geometric account of contrast gain control in neural populations.

Differential Encoding of Two-Tone Harmonics in the Male and Female Mouse Auditory Cortex

Harmonics are an integral part of music, speech, and vocalizations of animals. Since the rest of the auditory environment is primarily made up of nonharmonic sounds, the auditory system needs to perceptually separate the above two kinds of sounds. In mice, harmonics, generally with two-tone components (two-tone harmonic complexes, TTHCs), form an important component of vocal communication. Communication by pups during isolation from the mother and by adult males during courtship elicits typical behaviors in female mice-dams and adult courting females, respectively. Our study shows that the processing of TTHC is specialized in mice providing neural basis for perceptual differences between tones and TTHCs and also nonharmonic sounds. Investigation of responses in the primary auditory cortex (Au1) from in vivo extracellular recordings and two-photon Ca2+ imaging of excitatory and inhibitory neurons to TTHCs exhibit enhancement, suppression, or no-effect with respect to tones. Irrespective of neuron type, harmonic enhancement is maximized, and suppression is minimized when the fundamental frequencies (F 0) match the neuron's best fundamental frequency (BF0). Sex-specific processing of TTHC is evident from differences in the distributions of neurons' best frequency (BF) and best fundamental frequency (BF0) in single units, differences in harmonic suppressed cases re-BF0, independent of neuron types, and from pairwise noise correlations among excitatory and parvalbumin inhibitory interneurons. Furthermore, TTHCs elicit a higher response compared with two-tone nonharmonics in females, but not in males. Thus, our study shows specialized neural processing of TTHCs over tones and nonharmonics, highlighting local network specialization among different neuronal types.

Brainstem inhibitory neurons enhance behavioral feature selectivity by sharpening the tuning of excitatory neurons

The brainstem is a hub for sensorimotor integration, which mediates crucial innate behaviors. This brain region is characterized by a rich population of GABAergic inhibitory neurons, required for the proper expression of these innate behaviors. However, what roles these inhibitory neurons play in innate behaviors and how they function are still not fully understood. Here, we show that inhibitory neurons in the nucleus of the optic tract and dorsal-terminal nuclei (NOT-DTN) of the mouse can modulate the innate eye movement optokinetic reflex (OKR) by shaping the tuning properties of excitatory NOT-DTN neurons. Specifically, we demonstrate that although these inhibitory neurons do not directly induce OKR, they enhance the visual feature selectivity of OKR behavior, which is mediated by the activity of excitatory NOT-DTN neurons. Moreover, consistent with the sharpening role of inhibitory neurons in OKR behavior, they have broader tuning relative to excitatory neurons. Last, we demonstrate that inhibitory NOT-DTN neurons directly provide synaptic inhibition to nearby excitatory neurons and sharpen their tuning in a sustained manner, accounting for the enhanced feature selectivity of OKR behavior. In summary, our findings uncover a fundamental principle underlying the computational role of inhibitory neurons in brainstem sensorimotor circuits.

Sensory gating and gaining in sleep: the balance between the protection of sleep and the safeness of life (a review)

Sleep is a brain state characterised by a low vigilance level and diminished consciousness. Reaction to and processing of external stimuli is attenuated in sleep. During sleep, the reticular thalamic nucleus reduces the flow of sensory activity to the cerebral cortex through inhibition of the thalamus. This sensory gating process facilitates sleep. After reaching the afferent layers of primary cortex, the reduced sensory flow is adjusted, gained, and processed within various cortical layers before being transferred by the corticofugal system back to appropriate subdivisions of the thalamus as feedback. Thalamic subdivisions then dispatch this sensory information to related areas of the cerebral cortex, where it is (sub)consciously perceived. When necessary, a sleeping individual can be awakened by a wake-up call, either by stimuli indicating danger, or by personally meaningful stimuli. It is safe for a sleeping individual that it can be aroused when necessary. Evidently, there are two processes by which the brain adjusts the response to sensory stimuli before entering (sub)consciousness. Firstly 'sensory gating', a process favourable to the maintenance of sleep by reducing the sensory input to the brain through the reticular thalamic nucleus and secondly 'sensory gaining', a process implying that the gained preserved sensory input is continuously analysed by the corticofugal system to detect dangerous and relevant environmental elements, indispensable for safeness and well-being of the sleeper.

Gain control of sensory input across polysynaptic circuitries in mouse visual cortex by a single G protein-coupled receptor type (5-HT2A)

Response gain is a crucial means by which modulatory systems control the impact of sensory input. In the visual cortex, the serotonergic 5-HT2A receptor is key in such modulation. However, due to its expression across different cell types and lack of methods that allow for specific activation, the underlying network mechanisms remain unsolved. Here we optogenetically activate endogenous G protein-coupled receptor (GPCR) signaling of a single receptor subtype in distinct mouse neocortical subpopulations in vivo. We show that photoactivation of the 5-HT2A receptor pathway in pyramidal neurons enhances firing of both excitatory neurons and interneurons, whereas 5-HT2A photoactivation in parvalbumin interneurons produces bidirectional effects. Combined photoactivation in both cell types and cortical network modelling demonstrates a conductance-driven polysynaptic mechanism that controls the gain of visual input without affecting ongoing baseline levels. Our study opens avenues to explore GPCRs neuromodulation and its impact on sensory-driven activity and ongoing neuronal dynamics.

State modulation in spatial networks with three interneuron subtypes

Several inhibitory interneuron subtypes have been identified as critical in regulating sensory responses. However, the specific contribution of each interneuron subtype remains uncertain. In this work, we explore the contributions of cell-type specific activity and synaptic connections to dynamics of a spatially organized spiking neuron network. We find that the firing rates of the somatostatin (SOM) interneurons align closely with the level of network synchrony irrespective of the target of modulatory input. Further analysis reveals that inhibition from SOM to parvalbumin (PV) interneurons must be limited to allow gradual transitions from asynchrony to synchrony and that the strength of recurrent excitation onto SOM neurons determines the level of synchrony achievable in the network. Our results are consistent with recent experimental findings on cell-type specific manipulations. Overall, our results highlight common dynamic regimes achieved across modulations of different cell populations and identify SOM cells as the main driver of network synchrony.

Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex

Visual landmarks provide powerful reference signals for efficient navigation by altering the activity of spatially tuned neurons, such as place cells, head direction cells, and grid cells. To understand the neural mechanism by which landmarks exert such strong influence, it is necessary to identify how these visual features gain spatial meaning. In this study, we characterized visual landmark representations in mouse retrosplenial cortex (RSC) using chronic two-photon imaging of the same neuronal ensembles over the course of spatial learning. We found a pronounced increase in landmark-referenced activity in RSC neurons that, once established, remained stable across days. Changing behavioral context by uncoupling treadmill motion from visual feedback systematically altered neuronal responses associated with the coherence between visual scene flow speed and self-motion. To explore potential underlying mechanisms, we modeled how burst firing, mediated by supralinear somatodendritic interactions, could efficiently mediate context- and coherence-dependent integration of landmark information. Our results show that visual encoding shifts to landmark-referenced and context-dependent codes as these cues take on spatial meaning during learning.

Prefrontal Cortex subregions provide distinct visual and behavioral feedback modulation to the Primary Visual Cortex

The mammalian Prefrontal Cortex (PFC) has been suggested to modulate sensory information processing across multiple cortical regions via long-range axonal projections. These axonal projections arise from PFC subregions with unique brain-wide connectivity and functional repertoires, which may provide the architecture for modular feedback intended to shape sensory processing. Here, we used axonal tracing, axonal and somatic 2-photon calcium imaging, and chemogenetic manipulations in mice to delineate how projections from the Anterior Cingulate Cortex (ACA) and ventrolateral Orbitofrontal Cortex (ORB) of the PFC modulate sensory processing in the primary Visual Cortex (VISp) across behavioral states. Structurally, we found that ACA and ORB have distinct patterning of projections across both cortical regions and layers. ACA axons in VISp had a stronger representation of visual stimulus information than ORB axons, but both projections showed non-visual, behavior-dependent activity. ACA input to VISp enhanced the encoding of visual stimuli by VISp neurons, and modulation of visual responses scaled with arousal. On the other hand, ORB input shaped movement and arousal related modulation of VISp visual responses, but specifically reduced the encoding of high-contrast visual stimuli. Thus, ACA and ORB feedback have separable projection patterns and encode distinct visual and behavioral information, putatively providing the substrate for their unique effects on visual representations and behavioral modulation in VISp. Our results offer a refined model of cortical hierarchy and its impact on sensory information processing, whereby distinct as opposed to generalized properties of PFC projections contribute to VISp activity during discrete behavioral states.

Characterising sex-related differences in lower- and higher-threshold motor unit behaviour through high-density surface electromyography

Emerging questions in neuromuscular physiology revolve around whether males and females share similar neural control in diverse tasks across a broad range of intensities. In order to explore these features, high-density electromyography was used to record the myoelectrical activity of biceps brachii during trapezoidal isometric contractions at 35% and 70% of maximal voluntary force (MVF) on 11 male and 13 female participants. Identified motor units were then classified as lower-threshold (recruited at ≤30%MVF) and higher-threshold (recruited at >30%MVF). The discharge rate, interspike interval variability, recruitment and derecruitment thresholds, and estimates of neural drive to motor neurons were assessed. Female lower-threshold motor units showed higher neural drive (P < 0.001), accompanied by higher discharge rate at recruitment (P = 0.006), plateau (P = 0.001) and derecruitment (P = 0.001). On the other hand, male higher-threshold motor units showed greater neural drive (P = 0.04), accompanied by higher discharge rate at recruitment (P = 0.005), plateau (P = 0.04) and derecruitment (P = 0.01). Motor unit discharge rate normalised by the recruitment threshold was significantly higher in female lower-threshold motor units (P < 0.001), while no differences were observed in higher-threshold motor units. Recruitment and derecruitment thresholds are higher in males across all intensities (P < 0.01). However, males and females have similar activation and deactivation strategies, as evidenced by similar recruitment-to-derecruitment ratios (P > 0.05). This study encompasses a broad intensity range to analyse motor unit sex-related differences, highlighting higher neural drive and discharge rates in female lower-threshold motor units, elevated recruitment and derecruitment thresholds in males, and convergences in activation and deactivation strategies. HIGHLIGHTS: What is the central question of the study? Do male and female motor units behave similarly in low- and high-intensity contractions? What is the main finding and its importance? Female motor units show higher discharge rates in low-intensity tasks and lower discharge rates in high-intensity tasks, with no differences in recruitment behaviour. A broader inter-spike interval variability was also observed in females. These findings underline that there are sex-specific differences concern the firing strategies based on task intensity.

Contrast gain control is a reparameterization of a population response curve

Neurons in primary visual cortex (area V1) adapt in different degrees to the average contrast of the environment, suggesting that the representation of visual stimuli may interact with the state of cortical gain control in complex ways. To investigate this possibility, we measured and analyzed the responses of neural populations to visual stimuli as a function of contrast in different environments, each characterized by a unique distribution of contrast. Our findings reveal that, for a given stimulus, the population response can be described by a vector functionr ( g e c ) , where the gaing e is a decreasing function of the mean contrast of the environment. Thus, gain control can be viewed as a reparameterization of a population response curve, which is invariant across environments. Different stimuli are mapped to distinct curves, all originating from a common origin, corresponding to a zero-contrast response. Altogether, our findings provide a straightforward, geometric interpretation of contrast gain control at the population level and show that changes in gain are well coordinated among members of a neural population.

Parkinsonian Tremor as Unstable Feedback in a Physiologically Consistent Control Framework

Parkinson's disease (PD) is characterized by decreased dopamine in the basal ganglia that causes excessive tonic inhibition of the thalamus. This excessive inhibition seems to explain inhibitory motor symptoms in PD, but the source of tremor remains unclear. This paper investigates how neural inhibition may change the closed-loop characteristics of the human motor control system to determine how this established pathophysiology could produce tremor. The rate-coding model of neural signals suggests increased inhibition decreases signal amplitude, which could create a mismatch between the closed-loop dynamics and the internal models that overcome proprioceptive feedback delays. This paper aims to identify a candidate model structure with decreased-amplitude-induced tremor in PD that also agrees with previously recorded movements of healthy and cerebellar patients. The optimal feedback control theory of human motor control forms the basis of the model. Key additional elements include gating of undesired movements via the basal ganglia-thalamus-motor cortex circuit and the treatment of the efferent copy of the control input as a measurement in the state estimator. Simulations confirm the model's ability to capture tremor in PD and also demonstrate how disease progression could affect tremor and other motor symptoms, providing insight into the existence of tremor and non-tremor phenotypes. Altogether, the physiological underpinnings of the model structure and the agreement of model predictions with clinical observations provides support for the hypothesis that unstable feedback produces parkinsonian tremor. Consequently, these results also support the associated framework for the neuroanatomy of human motor control.

Lateral inhibition in V1 controls neural & perceptual contrast sensitivity

Lateral inhibition is a central principle for sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. Much work on the role of inhibition in sensory systems has focused on visual cortex; however, the neurons, computations, and mechanisms underlying cortical lateral inhibition remain debated, and its importance for visual perception remains unknown. Here, we tested how lateral inhibition from PV or SST neurons in mouse primary visual cortex (V1) modulates neural and perceptual sensitivity to stimulus contrast. Lateral inhibition from PV neurons reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from SST neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model identified spatially extensive lateral projections from SST neurons as the key factor, and we confirmed this with anatomy and direct subthreshold measurements of a larger spatial footprint for SST versus PV lateral inhibition. Together, these results define cell-type specific computational roles for lateral inhibition in V1, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.

A latent pool of neurons silenced by sensory-evoked inhibition can be recruited to enhance perception

To investigate which activity patterns in sensory cortex are relevant for perceptual decision-making, we combined two-photon calcium imaging and targeted two-photon optogenetics to interrogate barrel cortex activity during perceptual discrimination. We trained mice to discriminate bilateral whisker deflections and report decisions by licking left or right. Two-photon calcium imaging revealed sparse coding of contralateral and ipsilateral whisker input in layer 2/3, with most neurons remaining silent during the task. Activating pyramidal neurons using two-photon holographic photostimulation evoked a perceptual bias that scaled with the number of neurons photostimulated. This effect was dominated by optogenetic activation of non-coding neurons, which did not show sensory or motor-related activity during task performance. Photostimulation also revealed potent recruitment of cortical inhibition during sensory processing, which strongly and preferentially suppressed non-coding neurons. Our results suggest that a pool of non-coding neurons, selectively suppressed by network inhibition during sensory processing, can be recruited to enhance perception.

Coordinated Response Modulations Enable Flexible Use of Visual Information

We use sensory information in remarkably flexible ways. We can generalize by ignoring task-irrelevant features, report different features of a stimulus, and use different actions to report a perceptual judgment. These forms of flexible behavior are associated with small modulations of the responses of sensory neurons. While the existence of these response modulations is indisputable, efforts to understand their function have been largely relegated to theory, where they have been posited to change information coding or enable downstream neurons to read out different visual and cognitive information using flexible weights. Here, we tested these ideas using a rich, flexible behavioral paradigm, multi-neuron, multi-area recordings in primary visual cortex (V1) and mid-level visual area V4. We discovered that those response modulations in V4 (but not V1) contain the ingredients necessary to enable flexible behavior, but not via those previously hypothesized mechanisms. Instead, we demonstrated that these response modulations are precisely coordinated across the population such that downstream neurons have ready access to the correct information to flexibly guide behavior without making changes to information coding or synapses. Our results suggest a novel computational role for task-dependent response modulations: they enable flexible behavior by changing the information that gets out of a sensory area, not by changing information coding within it.

A sensory-motor theory of the neocortex

Recent neurophysiological and neuroanatomical studies suggest a close interaction between sensory and motor processes across the neocortex. Here, I propose that the neocortex implements active predictive coding (APC): each cortical area estimates both latent sensory states and actions (including potentially abstract actions internal to the cortex), and the cortex as a whole predicts the consequences of actions at multiple hierarchical levels. Feedback from higher areas modulates the dynamics of state and action networks in lower areas. I show how the same APC architecture can explain (1) how we recognize an object and its parts using eye movements, (2) why perception seems stable despite eye movements, (3) how we learn compositional representations, for example, part-whole hierarchies, (4) how complex actions can be planned using simpler actions, and (5) how we form episodic memories of sensory-motor experiences and learn abstract concepts such as a family tree. I postulate a mapping of the APC model to the laminar architecture of the cortex and suggest possible roles for cortico-cortical and cortico-subcortical pathways.

Protocol for stimulating specific rodent limb receptive fields while recording in vivo somatosensory-evoked activity

Studies on sensory information processing typically focus on whisker-related tactile information, overlooking the question of how sensory inputs from other body areas are processed at cortical levels. Here, we present a protocol for stimulating specific rodent limb receptive fields while recording in vivo somatosensory-evoked activity. We describe steps for localizing cortical-hindlimb coordinates using acute peripheral stimulation, electrode placement, and the application of electrical stimulation. This protocol overcomes the challenge of inducing a reproducible and consistent stimulation of specific limbs. For complete details on the use and execution of this protocol, please refer to Miguel-Quesada et al.1.

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.

Click train elicited local gamma synchrony: differing performance and pharmacological responsivity of primary auditory and prefrontal cortices

Auditory neural networks in the brain naturally entrain to rhythmic stimuli. Such synchronization is an accessible index of local network performance as captured by EEG. Across species, click trains delivered ∼ 40 Hz show strong entrainment with primary auditory cortex (Actx) being a principal source. Imaging studies have revealed additional cortical sources, but it is unclear if they are functionally distinct. Since auditory processing evolves hierarchically, we hypothesized that local synchrony would differ between between primary and association cortices. In female SD rats (N = 12), we recorded 40 Hz click train-elicited gamma oscillations using epidural electrodes situated at two distinct sites; one above the prefrontal cortex (PFC) and another above the Actx, after dosing with saline (1 ml/kg, sc) or the NMDA antagonist, MK801 (0.025, 0.05 or 0.1 mpk), in a blocked crossover design. Post-saline, both regions showed a strong 40 Hz auditory steady state response (ASSR). The latencies for the N1 response were ∼ 16 ms (Actx) and ∼ 34 ms (PFC). Narrow band (38-42 Hz) gamma oscillations appeared rapidly (<40 ms from stim onset at Actx but in a more delayed fashion (∼200 ms) at PFC. MK801 augmented gamma synchrony at Actx while dose-dependently disrupting at the PFC. Event-related gamma (but not beta) coherence, an index of long-distance connectivity, was disrupted by MK801. In conclusion, local network gamma synchrony in a higher order association cortex performs differently from that of the primary auditory cortex. We discuss these findings in the context of evolving sound processing across the cortical hierarchy.

Cell-type-specific cholinergic control of granular retrosplenial cortex with implications for angular velocity coding across brain states

Cholinergic receptor activation enables the persistent firing of cortical pyramidal neurons, providing a key cellular basis for theories of spatial navigation involving working memory, path integration, and head direction encoding. The granular retrosplenial cortex (RSG) is important for spatially-guided behaviors, but how acetylcholine impacts RSG neurons is unknown. Here, we show that a transcriptomically, morphologically, and biophysically distinct RSG cell-type - the low-rheobase (LR) neuron - has a very distinct expression profile of cholinergic muscarinic receptors compared to all other neighboring excitatory neuronal subtypes. LR neurons do not fire persistently in response to cholinergic agonists, in stark contrast to all other principal neuronal subtypes examined within the RSG and across midline cortex. This lack of persistence allows LR neuron models to rapidly compute angular head velocity (AHV), independent of cholinergic changes seen during navigation. Thus, LR neurons can consistently compute AHV across brain states, highlighting the specialized RSG neural codes supporting navigation.

Common and distinct neural mechanisms of attention

Despite a constant deluge of sensory stimulation, only a fraction of it is used to guide behavior. This selective processing is generally referred to as attention, and much research has focused on the neural mechanisms controlling it. Recently, research has broadened to include more ways by which different species selectively process sensory information, whether due to the sensory input itself or to different behavioral and brain states. This work has produced a complex and disjointed body of evidence across different species and forms of attention. However, it has also provided opportunities to better understand the breadth of attentional mechanisms. Here, we summarize the evidence that suggests that different forms of selective processing are supported by mechanisms both common and distinct.

Prediction of neural activity in connectome-constrained recurrent networks

We develop a theory of connectome-constrained neural networks in which a "student" network is trained to reproduce the activity of a ground-truth "teacher," representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, here the two networks have the same connectivity but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We find that a connectome is often insufficient to constrain the dynamics of networks that perform a specific task, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory can also prioritize which neurons to record from to most efficiently predict unmeasured network activity. Our analysis shows that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and provides a framework to determine when such models yield consistent dynamics.

Dopamine Pharmacodynamics: New Insights

Dopamine is a key neurotransmitter involved in physiological processes such as motor control, motivation, reward, cognitive function, and maternal and reproductive behaviors. Therefore, dysfunctions of the dopaminergic system are related to a plethora of human diseases. Dopamine, via different circuitries implicated in compulsive behavior, reward, and habit formation, also represents a key player in substance use disorder and the formation and perpetuation of mechanisms leading to addiction. Here, we propose dopamine as a model not only of neurotransmission but also of neuromodulation capable of modifying neuronal architecture. Abuse of substances like methamphetamine, cocaine, and alcohol and their consumption over time can induce changes in neuronal activities. These modifications lead to synaptic plasticity and finally to morphological and functional changes, starting from maladaptive neuro-modulation and ending in neurodegeneration.

Double Dissociation of Spontaneous Alpha-Band Activity and Pupil-Linked Arousal on Additive and Multiplicative Perceptual Gain

Perception is a probabilistic process dependent on external stimulus properties and one's internal state. However, which internal states influence perception and via what mechanisms remain debated. We studied how spontaneous alpha-band activity (8-13 Hz) and pupil fluctuations impact visual detection and confidence across stimulus contrast levels (i.e., the contrast response function, CRF). In human subjects of both sexes, we found that low prestimulus alpha power induced an "additive" shift in the CRF, whereby stimuli were reported present more frequently at all contrast levels, including contrast of zero (i.e., false alarms). Conversely, prestimulus pupil size had a "multiplicative" effect on detection such that stimuli occurring during large pupil states (putatively corresponding to higher arousal) were perceived more frequently as contrast increased. Signal detection modeling reveals that alpha power changes detection criteria equally across the CRF but not detection sensitivity (d'), whereas pupil-linked arousal modulated sensitivity, particularly for higher contrasts. Interestingly, pupil size and alpha power were positively correlated, meaning that some of the effect of alpha on detection may be mediated by pupil fluctuations. However, pupil-independent alpha still induced an additive shift in the CRF corresponding to a criterion effect. Our data imply that low alpha boosts detection and confidence by an additive factor, rather than by a multiplicative scaling of contrast responses, a profile which captures the effect of pupil-linked arousal. We suggest that alpha power and arousal fluctuations have dissociable effects on behavior. Alpha reflects the baseline level of visual excitability, which can vary independent of arousal.

Multiple processes of vocal sensory-motor interaction in primate auditory cortex

Sensory-motor interactions in the auditory system play an important role in vocal self-monitoring and control. These result from top-down corollary discharges, relaying predictions about vocal timing and acoustics. Recent evidence suggests such signals may be two distinct processes, one suppressing neural activity during vocalization and another enhancing sensitivity to sensory feedback, rather than a single mechanism. Single-neuron recordings have been unable to disambiguate due to overlap of motor signals with sensory inputs. Here, we sought to disentangle these processes in marmoset auditory cortex during production of multi-phrased 'twitter' vocalizations. Temporal responses revealed two timescales of vocal suppression: temporally-precise phasic suppression during phrases and sustained tonic suppression. Both components were present within individual neurons, however, phasic suppression presented broadly regardless of frequency tuning (gating), while tonic was selective for vocal frequencies and feedback (prediction). This suggests that auditory cortex is modulated by concurrent corollary discharges during vocalization, with different computational mechanisms.

A primary sensory cortical interareal feedforward inhibitory circuit for tacto-visual integration

Tactile sensation and vision are often both utilized for the exploration of objects that are within reach though it is not known whether or how these two distinct sensory systems combine such information. Here in mice, we used a combination of stereo photogrammetry for 3D reconstruction of the whisker array, brain-wide anatomical tracing and functional connectivity analysis to explore the possibility of tacto-visual convergence in sensory space and within the circuitry of the primary visual cortex (VISp). Strikingly, we find that stimulation of the contralateral whisker array suppresses visually evoked activity in a tacto-visual sub-region of VISp whose visual space representation closely overlaps with the whisker search space. This suppression is mediated by local fast-spiking interneurons that receive a direct cortico-cortical input predominantly from layer 6 neurons located in the posterior primary somatosensory barrel cortex (SSp-bfd). These data demonstrate functional convergence within and between two primary sensory cortical areas for multisensory object detection and recognition.

Dopamine D1-Receptor Organization Contributes to Functional Brain Architecture

Recent work has recognized a gradient-like organization in cortical function, spanning from primary sensory to transmodal cortices. It has been suggested that this axis is aligned with regional differences in neurotransmitter expression. Given the abundance of dopamine D1-receptors (D1DR), and its importance for modulation and neural gain, we tested the hypothesis that D1DR organization is aligned with functional architecture, and that inter-regional relationships in D1DR co-expression modulate functional cross talk. Using the world's largest dopamine D1DR-PET and MRI database (N = 180%, 50% female), we demonstrate that D1DR organization follows a unimodal-transmodal hierarchy, expressing a high spatial correspondence to the principal gradient of functional connectivity. We also demonstrate that individual differences in D1DR density between unimodal and transmodal regions are associated with functional differentiation of the apices in the cortical hierarchy. Finally, we show that spatial co-expression of D1DR primarily modulates couplings within, but not between, functional networks. Together, our results show that D1DR co-expression provides a biomolecular layer to the functional organization of the brain.

Non-uniform contextual interactions in the visual cortex place fundamental limits on spatial vision

A prevailing assumption in our understanding of how neurons in the primary visual cortex (V1) integrate contextual information is that such processes are spatially uniform. Conversely, perceptual phenomena such as visual crowding, the impaired ability to accurately recognize a target stimulus among distractors, suggest that interactions among stimuli are distinctly non-uniform. Prior studies have shown flankers at specific spatial geometries exert differential effects on target perception. To resolve this discrepancy, we investigated how flanker geometry impacted the representation of a target stimulus in the laminar microcircuits of V1. Our study reveals flanker location differentially impairs stimulus representation in excitatory neurons in the superficial and input layers of V1 by tuned suppression and untuned facilitation of orientation responses. Mechanistically, this effect can be explained by asymmetrical spatial kernels in a normalization model of cortical activity. Strikingly, these non-uniform modulations of neural representation mirror perceptual anisotropies. These results establish the non-uniform spatial integration of information in the earliest stages of cortical processing as a fundamental limitation of spatial vision.

Developmental trajectory of cortical somatostatin interneuron function

GABAergic inhibition is critical to the proper development of neocortical circuits. However, GABAergic interneurons are highly diverse and the developmental roles of distinct inhibitory subpopulations remain largely unclear. Dendrite-targeting, somatostatin-expressing interneurons (SST-INs) in the mature cortex regulate synaptic integration and plasticity in excitatory pyramidal neurons (PNs) and exhibit unique feature selectivity. Relatively little is known about early postnatal SST-IN activity or impact on surrounding local circuits. We examined juvenile SST-INs and PNs in mouse primary visual cortex. PNs exhibited stable visual responses and feature selectivity from eye opening onwards. In contrast, SST-INs developed visual responses and feature selectivity during the third postnatal week in parallel with a rapid increase in excitatory synaptic innervation. SST-INs largely exerted a multiplicative effect on nearby PN visual responses at all ages, but this impact increased over time. Our results identify a developmental window for the emergence of an inhibitory circuit mechanism for normalization.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex

We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed space-time receptive fields similar to those of simple cells in the primary visual cortex while the higher-level responses spanned longer timescales, mimicking temporal response hierarchies in the cortex. Additionally, the network's hierarchical sequence representation exhibited both predictive and postdictive effects resembling those observed in visual motion processing in humans (e.g., in the flash-lag illusion). When coupled with an associative memory emulating the role of the hippocampus, the model allowed episodic memories to be stored and retrieved, supporting cue-triggered recall of an input sequence similar to activity recall in the visual cortex. When extended to three hierarchical levels, the model learned progressively more abstract temporal representations along the hierarchy. Taken together, our results suggest that cortical processing and learning of sequences can be interpreted as dynamic predictive coding based on a hierarchical spatiotemporal generative model of the visual world.

Distinguishing examples while building concepts in hippocampal and artificial networks

The hippocampal subfield CA3 is thought to function as an auto-associative network that stores experiences as memories. Information from these experiences arrives directly from the entorhinal cortex as well as indirectly through the dentate gyrus, which performs sparsification and decorrelation. The computational purpose for these dual input pathways has not been firmly established. We model CA3 as a Hopfield-like network that stores both dense, correlated encodings and sparse, decorrelated encodings. As more memories are stored, the former merge along shared features while the latter remain distinct. We verify our model's prediction in rat CA3 place cells, which exhibit more distinct tuning during theta phases with sparser activity. Finally, we find that neural networks trained in multitask learning benefit from a loss term that promotes both correlated and decorrelated representations. Thus, the complementary encodings we have found in CA3 can provide broad computational advantages for solving complex tasks.

Information representation in an oscillating neural field model modulated by working memory signals

We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity-a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point-a stimulus feature-on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation-the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.