Cortical-subcortical interactions in goal-directed behavior.

Flexibly selecting appropriate actions in response to complex, ever-changing environments requires both cortical and subcortical regions, which are typically described as participating in a strict hierarchy. In this traditional view, highly specialized subcortical circuits allow for efficient responses to salient stimuli, at the cost of adaptability and context specificity, which are attributed to the neocortex. Their interactions are often described as the cortex providing top-down command signals for subcortical structures to implement; however, as available technologies develop, studies increasingly demonstrate that behavior is represented by brainwide activity and that even subcortical structures contain early signals of choice, suggesting that behavioral functions emerge as a result of different regions interacting as truly collaborative networks. In this review, we discuss the field's evolving understanding of how cortical and subcortical regions in placental mammals interact cooperatively, not only via top-down cortical-subcortical inputs but through bottom-up interactions, especially via the thalamus. We describe our current understanding of the circuitry of both the cortex and two exemplar subcortical structures, the superior colliculus and striatum, to identify which information is prioritized by which regions. We then describe the functional circuits these regions form with one another, and the thalamus, to create parallel loops and complex networks for brainwide information flow. Finally, we challenge the classic view that functional modules are contained within specific brain regions; instead, we propose that certain regions prioritize specific types of information over others, but the subnetworks they form, defined by their anatomical connections and functional dynamics, are the basis of true specialization.

Spatiotemporal dynamics of noradrenaline during learned behaviour.

Noradrenaline released from the locus coeruleus (LC) is a ubiquitous neuromodulator1-4 that has been linked to multiple functions including arousal5-8, action and sensory gain9-11, and learning12-16. Whether and how activation of noradrenaline-expressing neurons in the LC (LC-NA) facilitates different components of specific behaviours is unknown. Here we show that LC-NA activity displays distinct spatiotemporal dynamics to enable two functions during learned behaviour: facilitating task execution and encoding reinforcement to improve performance accuracy. To examine these functions, we used a behavioural task in mice with graded auditory stimulus detection and task performance. Optogenetic inactivation of the LC demonstrated that LC-NA activity was causal for both task execution and optimization. Targeted recordings of LC-NA neurons using photo-tagging, two-photon micro-endoscopy and two-photon output monitoring showed that transient LC-NA activation preceded behavioural execution and followed reinforcement. These two components of phasic activity were heterogeneously represented in LC-NA cortical outputs, such that the behavioural response signal was higher in the motor cortex and facilitated task execution, whereas the negative reinforcement signal was widely distributed among cortical regions and improved response sensitivity on the subsequent trial. Modular targeting of LC outputs thus enables diverse functions, whereby some noradrenaline signals are segregated among targets, whereas others are broadly distributed.

Imprinted Maternally Expressed microRNAs Antagonize Paternally Driven Gene Programs in Neurons.

Imprinted genes with parental-biased allelic expression are frequently co-regulated and enriched in common biological pathways. Here, we functionally characterize a large cluster of microRNAs (miRNAs) expressed from the maternally inherited allele ("maternally expressed") to explore the molecular and cellular consequences of imprinted miRNA activity. Using an induced neuron (iN) culture system, we show that maternally expressed miRNAs from the miR-379/410 cluster direct the RNA-induced silencing complex (RISC) to transcriptional and developmental regulators, including paternally expressed transcripts like Plagl1. Maternal deletion of this imprinted miRNA cluster resulted in increased protein levels of several targets and upregulation of a broader transcriptional program regulating synaptic transmission and neuronal function. A subset of the transcriptional changes resulting from miR-379/410 deletion can be attributed to de-repression of Plagl1. These data suggest maternally expressed miRNAs antagonize paternally driven gene programs in neurons.

The role of GABAergic signalling in neurodevelopmental disorders.

GABAergic inhibition shapes the connectivity, activity and plasticity of the brain. A series of exciting new discoveries provides compelling evidence that disruptions in a number of key facets of GABAergic inhibition have critical roles in the aetiology of neurodevelopmental disorders (NDDs). These facets include the generation, migration and survival of GABAergic neurons, the formation of GABAergic synapses and circuit connectivity, and the dynamic regulation of the efficacy of GABAergic signalling through neuronal chloride transporters. In this Review, we discuss recent work that elucidates the functions and dysfunctions of GABAergic signalling in health and disease, that uncovers the contribution of GABAergic neural circuit dysfunction to NDD aetiology and that leverages such mechanistic insights to advance precision medicine for the treatment of NDDs.

Reflections on the past two decades of neuroscience.

The first issue of Nature Reviews Neuroscience was published 20 years ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selection of researchers from across the field who have authored pieces published in the journal in recent years for their thoughts on notable and interesting developments in neuroscience, and particularly in their areas of the field, over the past two decades. They also provide some thoughts on current lines of research and questions that excite them.

Atypical behaviour and connectivity in SHANK3-mutant macaques.

Mutation or disruption of the SH3 and ankyrin repeat domains 3 (SHANK3) gene represents a highly penetrant, monogenic risk factor for autism spectrum disorder, and is a cause of Phelan-McDermid syndrome. Recent advances in gene editing have enabled the creation of genetically engineered non-human-primate models, which might better approximate the behavioural and neural phenotypes of autism spectrum disorder than do rodent models, and may lead to more effective treatments. Here we report CRISPR-Cas9-mediated generation of germline-transmissible mutations of SHANK3 in cynomolgus macaques (Macaca fascicularis) and their F1 offspring. Genotyping of somatic cells as well as brain biopsies confirmed mutations in the SHANK3 gene and reduced levels of SHANK3 protein in these macaques. Analysis of data from functional magnetic resonance imaging revealed altered local and global connectivity patterns that were indicative of circuit abnormalities. The founder mutants exhibited sleep disturbances, motor deficits and increased repetitive behaviours, as well as social and learning impairments. Together, these results parallel some aspects of the dysfunctions in the SHANK3 gene and circuits, as well as the behavioural phenotypes, that characterize autism spectrum disorder and Phelan-McDermid syndrome.

Differential Effects of Astrocyte Manipulations on Learned Motor Behavior and Neuronal Ensembles in the Motor Cortex.

Although motor cortex is crucial for learning precise and reliable movements, whether and how astrocytes contribute to its plasticity and function during motor learning is unknown. Here, we report that astrocyte-specific manipulations in primary motor cortex (M1) during a lever push task alter motor learning and execution, as well as the underlying neuronal population coding. Mice that express decreased levels of the astrocyte glutamate transporter 1 (GLT1) show impaired and variable movement trajectories, whereas mice with increased astrocyte Gq signaling show decreased performance rates, delayed response times, and impaired trajectories. In both groups, which include male and female mice, M1 neurons have altered interneuronal correlations and impaired population representations of task parameters, including response time and movement trajectories. RNA sequencing further supports a role for M1 astrocytes in motor learning and shows changes in astrocytic expression of glutamate transporter genes, GABA transporter genes, and extracellular matrix protein genes in mice that have acquired this learned behavior. Thus, astrocytes coordinate M1 neuronal activity during motor learning, and our results suggest that this contributes to learned movement execution and dexterity through mechanisms that include regulation of neurotransmitter transport and calcium signaling.SIGNIFICANCE STATEMENT We demonstrate for the first time that in the M1 of mice, astrocyte function is critical for coordinating neuronal population activity during motor learning. We demonstrate that knockdown of astrocyte glutamate transporter GLT1 affects specific components of learning, such as smooth trajectory formation. Altering astrocyte calcium signaling by activation of Gq-DREADD upregulates GLT1 and affects other components of learning, such as response rates and reaction times as well as trajectory smoothness. In both manipulations, neuronal activity in motor cortex is dysregulated, but in different ways. Thus, astrocytes have a crucial role in motor learning via their influence on motor cortex neurons, and they do so by mechanisms that include regulation of glutamate transport and calcium signals.

Mixtures of strategies underlie rodent behavior during reversal learning.

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions.

Rett syndrome: insights into genetic, molecular and circuit mechanisms.

Rett syndrome (RTT) is a severe neurological disorder caused by mutations in the gene encoding methyl-CpG-binding protein 2 (MeCP2). Almost two decades of research into RTT have greatly advanced our understanding of the function and regulation of the multifunctional protein MeCP2. Here, we review recent advances in understanding how loss of MeCP2 impacts different stages of brain development, discuss recent findings demonstrating the molecular role of MeCP2 as a transcriptional repressor, assess primary and secondary effects of MeCP2 loss and examine how loss of MeCP2 can result in an imbalance of neuronal excitation and inhibition at the circuit level along with dysregulation of activity-dependent mechanisms. These factors present challenges to the search for mechanism-based therapeutics for RTT and suggest specific approaches that may be more effective than others.

Reliable Sensory Processing in Mouse Visual Cortex through Cooperative Interactions between Somatostatin and Parvalbumin Interneurons.

Intrinsic neuronal variability significantly limits information encoding in the primary visual cortex (V1). However, under certain conditions, neurons can respond reliably with highly precise responses to the same visual stimuli from trial to trial. This suggests that there exists intrinsic neural circuit mechanisms that dynamically modulate the intertrial variability of visual cortical neurons. Here, we sought to elucidate the role of different inhibitory interneurons (INs) in reliable coding in mouse V1. To study the interactions between somatostatin-expressing interneurons (SST-INs) and parvalbumin-expressing interneurons (PV-INs), we used a dual-color calcium imaging technique that allowed us to simultaneously monitor these two neural ensembles while awake mice, of both sexes, passively viewed natural movies. SST neurons were more active during epochs of reliable pyramidal neuron firing, whereas PV neurons were more active during epochs of unreliable firing. SST neuron activity lagged that of PV neurons, consistent with a feedback inhibitory SST→PV circuit. To dissect the role of this circuit in pyramidal neuron activity, we used temporally limited optogenetic activation and inactivation of SST and PV interneurons during periods of reliable and unreliable pyramidal cell firing. Transient firing of SST neurons increased pyramidal neuron reliability by actively suppressing PV neurons, a proposal that was supported by a rate-based model of V1 neurons. These results identify a cooperative functional role for the SST→PV circuit in modulating the reliability of pyramidal neuron activity.SIGNIFICANCE STATEMENT Cortical neurons often respond to identical sensory stimuli with large variability. However, under certain conditions, the same neurons can also respond highly reliably. The circuit mechanisms that contribute to this modulation remain unknown. Here, we used novel dual-wavelength calcium imaging and temporally selective optical perturbation to identify an inhibitory neural circuit in visual cortex that can modulate the reliability of pyramidal neurons to naturalistic visual stimuli. Our results, supported by computational models, suggest that somatostatin interneurons increase pyramidal neuron reliability by suppressing parvalbumin interneurons via the inhibitory SST→PV circuit. These findings reveal a novel role of the SST→PV circuit in modulating the fidelity of neural coding critical for visual perception.

Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas.

The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas defined by retinotopic mapping were examined using supervised classifiers applied to responses elicited by a range of stimuli. Using two distinct datasets obtained using wide-field and two-photon imaging, we show that the area labels predicted by the classifiers were highly consistent with the labels obtained using retinotopy. Furthermore, the classifiers were able to model the boundaries of visual areas using resting state cortical responses obtained without any overt stimulus, in both datasets. With the wide-field dataset, clustering neuronal responses using a constrained semi-supervised classifier showed graceful degradation of accuracy. The results suggest that responses from visual cortical areas can be classified effectively using data-driven models. These responses likely reflect unique circuits within each area that give rise to activity with stronger intra-areal than inter-areal correlations, and their responses to controlled visual stimuli across trials drive higher areal classification accuracy than resting state responses.

Signal-to-signal neural networks for improved spike estimation from calcium imaging data.

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson's correlation coefficient, Spearman's rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.

Astrocyte glutamate uptake coordinates experience-dependent, eye-specific refinement in developing visual cortex.

The uptake of glutamate by astrocytes actively shapes synaptic transmission, however its role in the development and plasticity of neuronal circuits remains poorly understood. The astrocytic glutamate transporter, GLT1 is the predominant source of glutamate clearance in the adult mouse cortex. Here, we examined the structural and functional development of the visual cortex in GLT1 heterozygous (HET) mice using two-photon microscopy, immunohistochemistry and slice electrophysiology. We find that though eye-specific thalamic axonal segregation is intact, binocular refinement in the primary visual cortex is disrupted. Eye-specific responses to visual stimuli in GLT1 HET mice show altered binocular matching, with abnormally high responses to ipsilateral compared to contralateral eye stimulation and a greater mismatch between preferred orientation selectivity of ipsilateral and contralateral eye responses. Furthermore, we observe an increase in dendritic spine density in the basal dendrites of layer 2/3 excitatory neurons suggesting aberrant spine pruning. Monocular deprivation induces atypical ocular dominance plasticity in GLT1 HET mice, with an unusual depression of ipsilateral open eye responses; however, this change in ipsilateral responses correlates well with an upregulation of GLT1 protein following monocular deprivation. These results demonstrate that a key function of astrocytic GLT1 function during development is the experience-dependent refinement of ipsilateral eye inputs relative to contralateral eye inputs in visual cortex.

Towards a better diagnosis and treatment of Rett syndrome: a model synaptic disorder.

With the recent 50th anniversary of the first publication on Rett syndrome, and the almost 20 years since the first report on the link between Rett syndrome and MECP2 mutations, it is important to reflect on the tremendous advances in our understanding and their implications for the diagnosis and treatment of this neurodevelopmental disorder. Rett syndrome features an interesting challenge for biologists and clinicians, as the disorder lies at the intersection of molecular mechanisms of epigenetic regulation and neurophysiological alterations in synapses and circuits that together contribute to severe pathophysiological endophenotypes. Genetic, clinical, and neurobiological evidences support the notion that Rett syndrome is primarily a synaptic disorder, and a disease model for both intellectual disability and autism spectrum disorder. This review examines major developments in both recent neurobiological and preclinical findings of Rett syndrome, and to what extent they are beginning to impact our understanding and management of the disorder. It also discusses potential applications of knowledge on synaptic plasticity abnormalities in Rett syndrome to its diagnosis and treatment.

Major Vault Protein, a Candidate Gene in 16p11.2 Microdeletion Syndrome, Is Required for the Homeostatic Regulation of Visual Cortical Plasticity.

Microdeletion of a region in chromosome 16p11.2 increases susceptibility to autism. Although this region contains exons of 29 genes, disrupting only a small segment of the region, which spans five genes, is sufficient to cause autistic traits. One candidate gene in this critical segment is MVP, which encodes for the major vault protein (MVP) that has been implicated in regulation of cellular transport mechanisms. MVP expression levels in MVP+/- mice closely phenocopy those of 16p11.2 mutant mice, suggesting that MVP+/- mice may serve as a model of MVP function in 16p11.2 microdeletion. Here we show that MVP regulates the homeostatic component of ocular dominance (OD) plasticity in primary visual cortex. MVP+/- mice of both sexes show impairment in strengthening of open-eye responses after several days of monocular deprivation (MD), whereas closed-eye responses are weakened as normal, resulting in reduced overall OD plasticity. The frequency of miniature EPSCs (mEPSCs) in pyramidal neurons is decreased in MVP+/- mice after extended MD, suggesting a reduction of functional synapses. Correspondingly, upregulation of surface GluA1 AMPA receptors is reduced in MVP+/- mice after extended MD, and is accompanied by altered expression of STAT1 and phosphorylated ERK, which have been previously implicated in OD plasticity. Normalization of STAT1 levels by introducing STAT1 shRNA rescues surface GluA1 and open-eye responses, implicating STAT1 as a downstream effector of MVP. These findings demonstrate a specific role for MVP as a key molecule influencing the homeostatic component of activity-dependent synaptic plasticity, and potentially the corresponding phenotypes of 16p11.2 microdeletion syndrome.SIGNIFICANCE STATEMENT Major vault protein (MVP), a candidate gene in 16p11.2 microdeletion syndrome, has been implicated in the regulation of several cellular processes including transport mechanisms and scaffold signaling. However, its role in brain function and plasticity remains unknown. In this study, we identified MVP as an important regulator of the homeostatic component of experience-dependent plasticity, via regulation of STAT1 and ERK signaling. This study helps reveal a new mechanism for an autism-related gene in brain function, and suggests a broader role for neuro-immune interactions in circuit level plasticity. Importantly, our findings might explain specific components of the pathophysiology of 16p11.2 microdeletion syndrome.

Neural mechanisms of sensorimotor transformation and action selection.

Ray Guillery made major contributions to our understanding of the development and function of the brain. One of his principal conceptual insights, developed together with Murray Sherman [S.M. Sherman & R.W. Guillery (2001) Exploring the Thalamus. Elsevier, Amstrerdam; S. Sherman & R. Guillery (2006) Exploring the Thalamus and Its Role in Cortical Functioning. Academic Press, New York, NY; S.M. Sherman & R.W. Guillery (2013) Functional Connections of Cortical Areas: A New View from the Thalamus. MIT Press, Cambridge, MA and then in his last book (R. Guillery (2017) The Brain as a Tool: A Neuroscientist's Account. Oxford University Press, Oxford, UK)], was that the brain is a 'tool' to understand the world. In this view, the brain does not passively process sensory information and use the result to inform motor outputs. Rather, sensory and motor signals are widely broadcast and inextricably linked, with ongoing sensorimotor transformations serving as the basis for interaction with the outside world. Here, we describe recent studies from our laboratory and others which demonstrate this astute framing of the link among sensation, perception, and action postulated by Guillery and others [G. Deco & E.T. Rolls (2005) Prog Neurobiol, 76, 236-256; P. Cisek & J.F. Kalaska (2010) Annu Rev Neurosci, 33, 269-298]. Guillery situated his understanding in the deeply intertwined relationship between the thalamus and cortex, and importantly in the feedback from cortex to thalamus which in turn influences feed-forward drive to cortex [S.M. Sherman & R.W. Guillery (2001) Exploring the Thalamus. Elsevier, Amstrerdam; S. Sherman & R. Guillery (2006) Exploring the Thalamus and Its Role in Cortical Functioning. Academic Press, New York, NY]. We extend these observations to argue that brain mechanisms for sensorimotor transformations involve cortical and subcortical circuits that create internal models as a substrate for action, that a key role of sensory inputs is to update such models, and that a major function of sensorimotor processing underlying cognition is to enable action selection and execution.

Jointly reduced inhibition and excitation underlies circuit-wide changes in cortical processing in Rett syndrome.

Rett syndrome (RTT) arises from loss-of-function mutations in methyl-CpG binding protein 2 gene (Mecp2), but fundamental aspects of its physiological mechanisms are unresolved. Here, by whole-cell recording of synaptic responses in MeCP2 mutant mice in vivo, we show that visually driven excitatory and inhibitory conductances are both reduced in cortical pyramidal neurons. The excitation-to-inhibition (E/I) ratio is increased in amplitude and prolonged in time course. These changes predict circuit-wide reductions in response reliability and selectivity of pyramidal neurons to visual stimuli, as confirmed by two-photon imaging. Targeted recordings reveal that parvalbumin-expressing (PV+) interneurons in mutant mice have reduced responses. PV-specific MeCP2 deletion alone recapitulates effects of global MeCP2 deletion on cortical circuits, including reduced pyramidal neuron responses and reduced response reliability and selectivity. Furthermore, MeCP2 mutant mice show reduced expression of the cation-chloride cotransporter KCC2 (K+/Cl- exporter) and a reduced KCC2/NKCC1 (Na+/K+/Cl- importer) ratio. Perforated patch recordings demonstrate that the reversal potential for GABA is more depolarized in mutant mice, but is restored by application of the NKCC1 inhibitor bumetanide. Treatment with recombinant human insulin-like growth factor-1 restores responses of PV+ and pyramidal neurons and increases KCC2 expression to normalize the KCC2/NKCC1 ratio. Thus, loss of MeCP2 in the brain alters both excitation and inhibition in brain circuits via multiple mechanisms. Loss of MeCP2 from a specific interneuron subtype contributes crucially to the cell-specific and circuit-wide deficits of RTT. The joint restoration of inhibition and excitation in cortical circuits is pivotal for functionally correcting the disorder.

Spike Estimation from Fluorescence Signals Using High-Resolution Property of Group Delay.

Spike estimation from calcium (Ca2+) fluorescence signals is a fundamental and challenging problem in neuroscience. Several models and algorithms have been proposed for this task over the past decade. Nevertheless, it is still hard to achieve accurate spike positions from the Ca2+ fluorescence signals. While existing methods rely on data-driven methods and the physiology of neurons for modelling the spiking process, this work exploits the nature of the fluorescence responses to spikes using signal processing. We first motivate the problem by a novel analysis of the high-resolution property of minimum-phase group delay (GD) functions for multi-pole resonators. The resonators could be connected either in series or in parallel. The Ca2+ indicator responds to a spike with a sudden rise, that is followed by an exponential decay. We interpret the Ca2+ signal as the response of an impulse train to the change in Ca2+ concentration, where the Ca2+ response corresponds to a resonator. We perform minimum-phase group delay-based filtering of the Ca2+ signal for resolving spike locations. The performance of the proposed algorithm is evaluated on nine datasets spanning various indicators, sampling rates and, mouse brain regions. The proposed approach: GDspike, is compared with other spike estimation methods including MLspike, Vogelstein de-convolution algorithm, and data-driven Spike Triggered Mixture (STM) model. The performance of GDSpike is superior to that of the Vogelstein algorithm and is comparable to that of MLSpike. It can also be used to post-process the output of MLSpike, which further enhances the performance.

The influence of astrocytes on the width of orientation hypercolumns in visual cortex: A computational perspective.

Orientation preference maps (OPMs) are present in carnivores (such as cats and ferrets) and primates but are absent in rodents. In this study we investigate the possible link between astrocyte arbors and presence of OPMs. We simulate the development of orientation maps with varying hypercolumn widths using a variant of the Laterally Interconnected Synergetically Self-Organizing Map (LISSOM) model, the Gain Control Adaptive Laterally connected (GCAL) model, with an additional layer simulating astrocytic activation. The synaptic activity of V1 neurons is given as input to the astrocyte layer. The activity of this astrocyte layer is now used to modulate bidirectional plasticity of lateral excitatory connections in the V1 layer. By simply varying the radius of the astrocytes, the extent of lateral excitatory neuronal connections can be manipulated. An increase in the radius of lateral excitatory connections subsequently increases the size of a single hypercolumn in the OPM. When these lateral excitatory connections become small enough the OPM disappears and a salt-and-pepper organization emerges.

Division and subtraction by distinct cortical inhibitory networks in vivo.

Brain circuits process information through specialized neuronal subclasses interacting within a network. Revealing their interplay requires activating specific cells while monitoring others in a functioning circuit. Here we use a new platform for two-way light-based circuit interrogation in visual cortex in vivo to show the computational implications of modulating different subclasses of inhibitory neurons during sensory processing. We find that soma-targeting, parvalbumin-expressing (PV) neurons principally divide responses but preserve stimulus selectivity, whereas dendrite-targeting, somatostatin-expressing (SOM) neurons principally subtract from excitatory responses and sharpen selectivity. Visualized in vivo cell-attached recordings show that division by PV neurons alters response gain, whereas subtraction by SOM neurons shifts response levels. Finally, stimulating identified neurons while scanning many target cells reveals that single PV and SOM neurons functionally impact only specific subsets of neurons in their projection fields. These findings provide direct evidence that inhibitory neuronal subclasses have distinct and complementary roles in cortical computations.