Division and subtraction by distinct cortical inhibitory networks in vivo

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.

Deprivation of visual input alters specific subset of inhibitory neurons and affect thalamic afferent terminals in V1 of rd1 mouse

Retinitis Pigmentosa (RP) is a heterogenous group of inherited disorder, and its progression not only affects the retina but also the primary visual cortex. This manifests imbalances in the excitatory and inhibitory neurotransmission. Here, we investigated if changes in cortical functioning is linked to alterations in GABAergic population of neurons and its two important subsets, somatostatin (SST) and parvalbumin (PV) neuron in rd1 model of retinal degeneration (RD). We demonstrate marked decrease in the proportion of SST neurons in different layers of cortex whereas PV neurons were less affected. Moreover, we found reduced expression of glutamatergic thalamic afferents (VGLUT2) due to lack of visual activity. These results suggest PV neurons are likely recruited by the cortical circuitry to increase the inhibitory drive and compensate the disrupted inhibition-excitation balance. However, reduced SST expression perhaps results in weakening of stimulus selectivity. Delineating their functional role during RD will provide insights for acquisition of high-resolution vision thereby improving current state of vision restoration.

Local stimulation of pyramidal neurons in deep cortical layers of anesthetized rats enhances cortical visual information processing

In the primary visual cortex area V1 activation of inhibitory interneurons, which provide negative feedback for excitatory pyramidal neurons, can improve visual response reliability and orientation selectivity. Moreover, optogenetic activation of one class of interneurons, parvalbumin (PV) positive cells, reduces the receptive field (RF) width. These data suggest that in V1 the negative feedback improves visual information processing. However, according to information theory, noise can limit information content in a signal, and to the best of our knowledge, in V1 signal-to-noise ratio (SNR) has never been estimated following either pyramidal or inhibitory neuron activation. Therefore, we optogenetically activated pyramidal or PV neurons in the deep layers of cortical area V1 and measured the SNR and RF area in nearby pyramidal neurons. Activation of pyramidal or PV neurons increased the SNR by 267% and 318%, respectively, and reduced the RF area to 60.1% and 77.5%, respectively, of that of the control. A simple integrate-and-fire neuron model demonstrated that an improved SNR and a reduced RF area can increase the amount of information encoded by neurons. We conclude that in V1 activation of pyramidal neurons improves visual information processing since the location of the visual stimulus can be pinpointed more accurately (via a reduced RF area), and more information is encoded by neurons (due to increased SNR).

"Distinct inhibitory neurons differently shape neuronal codes for sound intensity in the auditory cortex"

Cortical circuits contain multiple types of inhibitory neurons which shape how information is processed within neuronal networks. Here, we asked whether somatostatin-expressing (SST) and vasoactive intestinal peptide-expressing (VIP) inhibitory neurons have distinct effects on population neuronal responses to noise bursts of varying intensities. We optogenetically stimulated SST or VIP neurons while simultaneously measuring the calcium responses of populations of hundreds of neurons in the auditory cortex of male and female awake, head-fixed mice to sounds. Upon SST neuronal activation, noise bursts representations became more discrete for different intensity levels, relying on cell identity rather than strength. By contrast, upon VIP neuronal activation, noise bursts of different intensity level activated overlapping neuronal populations, albeit at different response strengths. At the single-cell level, SST and VIP neuronal activation differentially modulated the response-level curves of monotonic and nonmonotonic neurons. SST neuronal activation effects were consistent with a shift of the neuronal population responses toward a more localist code with different cells responding to sounds of different intensity. By contrast, VIP neuronal activation shifted responses towards a more distributed code, in which sounds of different intensity level are encoded in the relative response of similar populations of cells. These results delineate how distinct inhibitory neurons in the auditory cortex dynamically control cortical population codes. Different inhibitory neuronal populations may be recruited under different behavioral demands, depending on whether categorical or invariant representations are advantageous for the task.

Simultaneous interneuron labeling reveals cell type-specific, population-level interactions in cortex

Cortical interneurons shape network activity in cell type-specific ways, and interact with other cell types. These interactions are understudied, as current methods typically restrict in vivo labeling to one neuron type. Although post-hoc identification of many cell types has been accomplished, the method is not available to many labs. We present a method to distinguish two red fluorophores in vivo, allowing imaging of activity in somatostatin (SOM), parvalbumin (PV), and the rest of the neural population in mouse cortex. We compared population events in PV and SOM neurons and observed that local network states reflected the ratio of SOM to PV neuron activity, demonstrating the importance of simultaneous labeling to explain dynamics. Activity became sparser and less correlated when the ratio between SOM and PV activity was high. Our simple method can be flexibly applied to study interactions among any combination of distinct cell type populations across brain areas.

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.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Model of a striatal circuit exploring biological mechanisms underlying decision-making during normal and disordered states

Decision-making requires continuous adaptation to internal and external contexts. Changes in decision-making are reliable transdiagnostic symptoms of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a mathematical space for decision-making computations depending on context, and how the matrix compartment defines action value depending on the space. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presentation. The model reframes the role of the striosomal circuit in neuroeconomic and disorder-affected decision-making.

Highlights

Striosomes prioritize decision-related data used by matrix to set action values. Striosomes and matrix have different roles in the direct and indirect pathways. Abnormal information organization/valuation alters disorder presentation. Variance in data prioritization may explain individual differences in disorders.

eTOC

Beck et al. developed a computational model of how a striatal circuit functions during decision-making. The model unifies and extends theories about the direct versus indirect pathways. It further suggests how aberrant circuit function underlies decision-making phenomena observed in neuropsychiatric disorders.

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.

Distinct feedforward and feedback pathways for cell-type specific attention effects

Selective attention is thought to depend on enhanced firing activity in extrastriate areas. Theories suggest that this enhancement depends on selective inter-areal communication via gamma (30-80 Hz) phase-locking. To test this, we simultaneously recorded from different cell types and cortical layers of macaque V1 and V4. We find that while V1-V4 gamma phase-locking between local field potentials increases with attention, the V1 gamma rhythm does not engage V4 excitatory-neurons, but only fast-spiking interneurons in L4 of V4. By contrast, attention enhances V4 spike-rates in both excitatory and inhibitory cells, most strongly in L2/3. The rate increase in L2/3 of V4 precedes V1 in time. These findings suggest enhanced signal transmission with attention does not depend on inter-areal gamma phase-locking and show that the endogenous gamma rhythm has cell-type- and layer-specific effects on downstream target areas. Similar findings were made in the mouse visual system, based on opto-tagging of identified interneurons.

Inter and intralaminar excitation of parvalbumin interneurons in mouse barrel cortex

Parvalbumin (PV) interneurons are inhibitory fast-spiking cells with essential roles in directing the flow of information through cortical circuits. These neurons set the balance between excitation and inhibition and control rhythmic activity. PV interneurons differ between cortical layers in their morphology, circuitry, and function, but how their electrophysiological properties vary has received little attention. Here we investigate responses of PV interneurons in different layers of primary somatosensory barrel cortex (BC) to different excitatory inputs. With the genetically-encoded hybrid voltage sensor, hVOS, we recorded voltage changes in many L2/3 and L4 PV interneurons simultaneously, with stimulation applied to either L2/3 or L4. A semi-automated procedure was developed to identify small regions of interest corresponding to single responsive PV interneurons. Amplitude, half-width, and rise-time were greater for PV interneurons residing in L2/3 compared to L4. Stimulation in L2/3 elicited responses in both L2/3 and L4 with longer latency compared to stimulation in L4. These differences in latency between layers could influence their windows for temporal integration. Thus, PV interneurons in different cortical layers of BC respond in a layer specific and input specific manner, and these differences have potential roles in cortical computations.

Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex

How do biological neural systems efficiently encode, transform and propagate information between the sensory periphery and the sensory cortex about sensory features evolving at different time scales? Are these computations efficient in normative information processing terms? While previous work has suggested that biologically plausible models of of such neural information processing may be implemented efficiently within a single processing layer, how such computations extend across several processing layers is less clear. Here, we model propagation of multiple time-varying sensory features across a sensory pathway, by extending the theory of efficient coding with spikes to efficient encoding, transformation and transmission of sensory signals. These computations are optimally realized by a multilayer spiking network with feedforward networks of spiking neurons (receptor layer) and recurrent excitatory-inhibitory networks of generalized leaky integrate-and-fire neurons (recurrent layers). Our model efficiently realizes a broad class of feature transformations, including positive and negative interaction across features, through specific and biologically plausible structures of feedforward connectivity. We find that mixing of sensory features in the activity of single neurons is beneficial because it lowers the metabolic cost at the network level. We apply the model to the somatosensory pathway by constraining it with parameters measured empirically and include in its last node, analogous to the primary somatosensory cortex (S1), two types of inhibitory neurons: parvalbumin-positive neurons realizing lateral inhibition, and somatostatin-positive neurons realizing winner-take-all inhibition. By implementing a negative interaction across stimulus features, this model captures several intriguing empirical observations from the somatosensory system of the mouse, including a decrease of sustained responses from subcortical networks to S1, a non-linear effect of the knock-out of receptor neuron types on the activity in S1, and amplification of weak signals from sensory neurons across the pathway.

Whole-brain Mapping of Inputs and Outputs of Specific Orbitofrontal Cortical Neurons in Mice

The orbitofrontal cortex (ORB), a region crucial for stimulus-reward association, decision-making, and flexible behaviors, extensively connects with other brain areas. However, brain-wide inputs to projection-defined ORB neurons and the distribution of inhibitory neurons postsynaptic to neurons in specific ORB subregions remain poorly characterized. Here we mapped the inputs of five types of projection-specific ORB neurons and ORB outputs to two types of inhibitory neurons. We found that different projection-defined ORB neurons received inputs from similar cortical and thalamic regions, albeit with quantitative variations, particularly in somatomotor areas and medial groups of the dorsal thalamus. By counting parvalbumin (PV) or somatostatin (SST) interneurons innervated by neurons in specific ORB subregions, we found a higher fraction of PV neurons in sensory cortices and a higher fraction of SST neurons in subcortical regions targeted by medial ORB neurons. These results provide insights into understanding and investigating the function of specific ORB neurons.

Pyramidal and parvalbumin neurons modulate the process of electroacupuncture stimulation for stroke rehabilitation

Electroacupuncture (EA) stimulation has been shown to be beneficial in stroke rehabilitation; however, little is known about the neurological mechanism by which this peripheral stimulation approach treats for stroke. This study showed that both pyramidal and parvalbumin (PV) neuronal activity increased in the contralesional primary motor cortex forelimb motor area (M1FL) after ischemic stroke induced by focal unilateral occlusion in the M1FL. EA stimulation reduced pyramidal neuronal activity and increased PV neuronal activity. These results were obtained by a combination of fiber photometry recordings, in vivo and in vitro electrophysiological recordings, and immunofluorescence. Moreover, EA was found to regulate the expression/function of N-methyl-D-aspartate receptors (NMDARs) altered by stroke pathology. In summary, our findings suggest that EA could restore disturbed neuronal activity through the regulation of the activity of pyramidal and PV neurons. Furthermore, NMDARs we shown to play an important role in EA-mediated improvements in sensorimotor ability during stroke rehabilitation.

Brightness illusions drive a neuronal response in the primary visual cortex under top-down modulation

Brightness illusions are a powerful tool in studying vision, yet their neural correlates are poorly understood. Based on a human paradigm, we presented illusory drifting gratings to mice. Primary visual cortex (V1) neurons responded to illusory gratings, matching their direction selectivity for real gratings, and they tracked the spatial phase offset between illusory and real gratings. Illusion responses were delayed compared to real gratings, in line with the theory that processing illusions requires feedback from higher visual areas (HVAs). We provide support for this theory by showing a reduced V1 response to illusions, but not real gratings, following HVAs optogenetic inhibition. Finally, we used the pupil response (PR) as an indirect perceptual report and showed that the mouse PR matches the human PR to perceived luminance changes. Our findings resolve debates over whether V1 neurons are involved in processing illusions and highlight the involvement of feedback from HVAs.

Physical and functional convergence of the autism risk genes Scn2a and Ank2 in neocortical pyramidal cell dendrites

Dysfunction in sodium channels and their ankyrin scaffolding partners have both been implicated in neurodevelopmental disorders, including autism spectrum disorder (ASD). In particular, the genes SCN2A, which encodes the sodium channel NaV1.2, and ANK2, which encodes ankyrin-B, have strong ASD association. Recent studies indicate that ASD-associated haploinsufficiency in Scn2a impairs dendritic excitability and synaptic function in neocortical pyramidal cells, but how NaV1.2 is anchored within dendritic regions is unknown. Here, we show that ankyrin-B is essential for scaffolding NaV1.2 to the dendritic membrane of mouse neocortical neurons and that haploinsufficiency of Ank2 phenocopies intrinsic dendritic excitability and synaptic deficits observed in Scn2a+/- conditions. These results establish a direct, convergent link between two major ASD risk genes and reinforce an emerging framework suggesting that neocortical pyramidal cell dendritic dysfunction can contribute to neurodevelopmental disorder pathophysiology.

Local origin of excitatory-inhibitory tuning equivalence in a cortical network

The interplay between excitation and inhibition determines the fidelity of cortical representations. The receptive fields of excitatory neurons are often finely tuned to encoded features, but the principles governing the tuning of inhibitory neurons remain elusive. In this study, we recorded populations of neurons in the mouse postsubiculum (PoSub), where the majority of excitatory neurons are head-direction (HD) cells. We show that the tuning of fast-spiking (FS) cells, the largest class of cortical inhibitory neurons, was broad and frequently radially symmetrical. By decomposing tuning curves using the Fourier transform, we identified an equivalence in tuning between PoSub-FS and PoSub-HD cell populations. Furthermore, recordings, optogenetic manipulations of upstream thalamic populations and computational modeling provide evidence that the tuning of PoSub-FS cells has a local origin. These findings support the notion that the equivalence of neuronal tuning between excitatory and inhibitory cell populations is an intrinsic property of local cortical networks.

Emergence of co-tuning in inhibitory neurons as a network phenomenon mediated by randomness, correlations, and homeostatic plasticity

Cortical excitatory neurons show clear tuning to stimulus features, but the tuning properties of inhibitory interneurons are ambiguous. While inhibitory neurons have been considered to be largely untuned, some studies show that some parvalbumin-expressing (PV) neurons do show feature selectivity and participate in co-tuned subnetworks with pyramidal neurons. In this study, we first use mean-field theory to demonstrate that a combination of homeostatic plasticity governing the synaptic dynamics of the connections from PV to excitatory neurons, heterogeneity in the excitatory postsynaptic potentials that impinge on PV neurons, and shared correlated input from layer 4 results in the functional and structural self-organization of PV subnetworks. Second, we show that structural and functional feature tuning of PV neurons emerges more clearly at the network level, i.e., that population-level measures identify functional and structural co-tuning of PV neurons that are not evident in pairwise individual-level measures. Finally, we show that such co-tuning can enhance network stability at the cost of reduced feature selectivity.

Astrocyte CCN1 stabilizes neural circuits in the adult brain

Neural circuits in many brain regions are refined by experience. Sensory circuits support higher plasticity at younger ages during critical periods - times of circuit refinement and maturation - and limit plasticity in adulthood for circuit stability. The mechanisms underlying these differing plasticity levels and how they serve to maintain and stabilize the properties of sensory circuits remain largely unclear. By combining a transcriptomic approach with ex vivo electrophysiology and in vivo imaging techniques, we identify that astrocytes release cellular communication network factor 1 (CCN1) to maintain synapse and circuit stability in the visual cortex. By overexpressing CCN1 in critical period astrocytes, we find that it promotes the maturation of inhibitory circuits and limits ocular dominance plasticity. Conversely, by knocking out astrocyte CCN1 in adults, binocular circuits are destabilized. These studies establish CCN1 as a novel astrocyte-secreted factor that stabilizes neuronal circuits. Moreover, they demonstrate that the composition and properties of sensory circuits require ongoing maintenance in adulthood, and that these maintenance cues are provided by astrocytes.

Cortical Zinc Signaling Is Necessary for Changes in Mouse Pupil Diameter That Are Evoked by Background Sounds with Different Contrasts

Luminance-independent changes in pupil diameter (PD) during wakefulness influence and are influenced by neuromodulatory, neuronal, and behavioral responses. However, it is unclear whether changes in neuromodulatory activity in a specific brain area are necessary for the associated changes in PD or whether some different mechanisms cause parallel fluctuations in both PD and neuromodulation. To answer this question, we simultaneously recorded PD and cortical neuronal activity in male and female mice. Namely, we measured PD and neuronal activity during adaptation to sound contrast, which is a well-described adaptation conserved in many species and brain areas. In the primary auditory cortex (A1), increases in the variability of sound level (contrast) induce a decrease in the slope of the neuronal input-output relationship, neuronal gain, which depends on cortical neuromodulatory zinc signaling. We found a previously unknown modulation of PD by changes in background sensory context: high stimulus contrast sounds evoke larger increases in evoked PD compared with low-contrast sounds. To explore whether these changes in evoked PD are controlled by cortical neuromodulatory zinc signaling, we imaged single-cell neural activity in A1, manipulated zinc signaling in the cortex, and assessed PD in the same awake mouse. We found that cortical synaptic zinc signaling is necessary for increases in PD during high-contrast background sounds compared with low-contrast sounds. This finding advances our knowledge about how cortical neuromodulatory activity affects PD changes and thus advances our understanding of the brain states, circuits, and neuromodulatory mechanisms that can be inferred from pupil size fluctuations.

Simultaneous interneuron labeling reveals population-level interactions among parvalbumin, somatostatin, and pyramidal neurons in cortex

Cortical interneurons shape network activity in cell type-specific ways, and are also influenced by interactions with other cell types. These specific cell-type interactions are understudied, as transgenic labeling methods typically restrict labeling to one neuron type at a time. Although recent methods have enabled post-hoc identification of cell types, these are not available to many labs. Here, we present a method to distinguish between two red fluorophores in vivo, which allowed imaging of activity in somatostatin (SOM), parvalbumin (PV), and putative pyramidal neurons (PYR) in mouse association cortex. We compared population events of elevated activity and observed that the PYR network state corresponded to the ratio between mean SOM and PV neuron activity, demonstrating the importance of simultaneous labeling to explain dynamics. These results extend previous findings in sensory cortex, as activity became sparser and less correlated when the ratio between SOM and PV activity was high.

Neuronal composition of processing modules in human V1: laminar density for neuronal and non-neuronal populations and a comparison with macaque

The neuronal composition of homologous brain regions in different primates is important for understanding their processing capacities. Primary visual cortex (V1) has been widely studied in different members of the catarrhines. Neuronal density is considered to be central in defining the structure-function relationship. In human, there are large variations in the reported neuronal density from prior studies. We found the neuronal density in human V1 was 79,000 neurons/mm3, which is 35% of the neuronal density previously determined in macaque V1. Laminar density was proportionally similar between human and macaque. In V1, the ocular dominance column (ODC) contains the circuits for the emergence of orientation preference and spatial processing of a point image in many mammalian species. Analysis of the total neurons in an ODC and of the full number of neurons in macular vision (the central 15°) indicates that humans have 1.3× more neurons than macaques even though the density of neurons in macaque is 3× the density in human V1. We propose that the number of neurons in a functional processing unit rather than the number of neurons under a mm2 of cortex is more appropriate for cortical comparisons across species.

Synaptic configuration and reconfiguration in the neocortex are spatiotemporally selective

Brain computation relies on the neural networks. Neurons extend the neurites such as dendrites and axons, and the contacts of these neurites that form chemical synapses are the biological basis of signal transmissions in the central nervous system. Individual neuronal outputs can influence the other neurons within the range of the axonal spread, while the activities of single neurons can be affected by the afferents in their somatodendritic fields. The morphological profile, therefore, binds the functional role each neuron can play. In addition, synaptic connectivity among neurons displays preference based on the characteristics of presynaptic and postsynaptic neurons. Here, the author reviews the "spatial" and "temporal" connection selectivity in the neocortex. The histological description of the neocortical circuitry depends primarily on the classification of cell types, and the development of gene engineering techniques allows the cell type-specific visualization of dendrites and axons as well as somata. Using genetic labeling of particular cell populations combined with immunohistochemistry and imaging at a subcellular spatial resolution, we revealed the "spatial selectivity" of cortical wirings in which synapses are non-uniformly distributed on the subcellular somatodendritic domains in a presynaptic cell type-specific manner. In addition, cortical synaptic dynamics in learning exhibit presynaptic cell type-dependent "temporal selectivity": corticocortical synapses appear only transiently during the learning phase, while learning-induced new thalamocortical synapses persist, indicating that distinct circuits may supervise learning-specific ephemeral synapse and memory-specific immortal synapse formation. The selectivity of spatial configuration and temporal reconfiguration in the neural circuitry may govern diverse functions in the neocortex.

Normalization in mouse primary visual cortex

When multiple stimuli appear together in the receptive field of a visual cortical neuron, the response is typically close to the average of that neuron's response to each individual stimulus. The departure from a linear sum of each individual response is referred to as normalization. In mammals, normalization has been best characterized in the visual cortex of macaques and cats. Here we study visually evoked normalization in the visual cortex of awake mice using imaging of calcium indicators in large populations of layer 2/3 (L2/3) V1 excitatory neurons and electrophysiological recordings across layers in V1. Regardless of recording method, mouse visual cortical neurons exhibit normalization to varying degrees. The distributions of normalization strength are similar to those described in cats and macaques, albeit slightly weaker on average.

Selective plasticity of fast and slow excitatory synapses on somatostatin interneurons in adult visual cortex

Somatostatin-positive (SOM) interneurons are integral for shaping cortical processing and their dynamic recruitment is likely necessary for adaptation to sensory experience and contextual information. We found that excitatory synapses on SOMs in layer 2/3 (L2/3) of primary visual cortex (V1) of mice can be categorized into fast (F)- and slow (S)-Types based on the kinetics of the AMPA receptor-mediated current. Each SOM contains both types of synapses in varying proportions. The majority of local pyramidal neurons (PCs) make unitary connections with SOMs using both types, followed by those utilizing only S-Type, and a minority with only F-Type. Sensory experience differentially regulates synapses on SOMs, such that local F-Type synapses change with visual deprivation and S-Type synapses undergo plasticity with crossmodal auditory deprivation. Our results demonstrate that the two types of excitatory synapses add richness to the SOM circuit recruitment and undergo selective plasticity enabling dynamic adaptation of the adult V1.

Stimulus-Specific Prediction Error Neurons in Mouse Auditory Cortex

Comparing expectation with experience is an important neural computation performed throughout the brain and is a hallmark of predictive processing. Experiments that alter the sensory outcome of an animal's behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors (PEs), mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through nonpredictive mechanisms, such as the movement-based facilitation of a neuron's inherent sound responses. If sensory prediction error neurons exist in sensory cortex, it is unknown whether they manifest as general error responses, or respond with specificity to errors in distinct stimulus dimensions. To answer these questions, we trained mice of either sex to expect the outcome of a simple sound-generating behavior and recorded auditory cortex activity as mice heard either the expected sound or sounds that deviated from expectation in one of multiple distinct dimensions. Our data reveal that the auditory cortex learns to suppress responses to self-generated sounds along multiple acoustic dimensions simultaneously. We identify a distinct population of auditory cortex neurons that are not responsive to passive sounds or to the expected sound but that encode prediction errors. These prediction error neurons are abundant only in animals with a learned motor-sensory expectation, and encode one or two specific violations rather than a generic error signal. Together, these findings reveal that cortical predictions about self-generated sounds have specificity in multiple simultaneous dimensions and that cortical prediction error neurons encode specific violations from expectation.SIGNIFICANCE STATEMENT Audette et. al record neural activity in the auditory cortex while mice perform a sound-generating forelimb movement and measure neural responses to sounds that violate an animal's expectation in different ways. They find that predictions about self-generated sounds are highly specific across multiple stimulus dimensions and that a population of typically nonsound-responsive neurons respond to sounds that violate an animal's expectation in a specific way. These results identify specific prediction error (PE) signals in the mouse auditory cortex and suggest that errors may be calculated early in sensory processing.

Generalizing biological surround suppression based on center surround similarity via deep neural network models

Sensory perception is dramatically influenced by the context. Models of contextual neural surround effects in vision have mostly accounted for Primary Visual Cortex (V1) data, via nonlinear computations such as divisive normalization. However, surround effects are not well understood within a hierarchy, for neurons with more complex stimulus selectivity beyond V1. We utilized feedforward deep convolutional neural networks and developed a gradient-based technique to visualize the most suppressive and excitatory surround. We found that deep neural networks exhibited a key signature of surround effects in V1, highlighting center stimuli that visually stand out from the surround and suppressing responses when the surround stimulus is similar to the center. We found that in some neurons, especially in late layers, when the center stimulus was altered, the most suppressive surround surprisingly can follow the change. Through the visualization approach, we generalized previous understanding of surround effects to more complex stimuli, in ways that have not been revealed in visual cortices. In contrast, the suppression based on center surround similarity was not observed in an untrained network. We identified further successes and mismatches of the feedforward CNNs to the biology. Our results provide a testable hypothesis of surround effects in higher visual cortices, and the visualization approach could be adopted in future biological experimental designs.

Role of interneuron subtypes in controlling trial-by-trial output variability in the neocortex

Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo which shapes the stimulus response. Computational models have revealed how local network structure and feedforward inputs shape the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in this process is less understood. To address this, we investigate the dynamics of stimulus response in a cortical microcircuit model with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). Our findings demonstrate that the balance of inputs to different neuron populations and input covariances are the primary determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input balances. In general, the network exhibits smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Importantly, our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.

Dynamics of cortical contrast adaptation predict perception of signals in noise

Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background. The observed changes in cortical gain and behavioral detection followed the dynamics of a normative model of efficient contrast gain control; specifically, target detection and sensitivity improved slowly in low contrast, but degraded rapidly in high contrast. Auditory cortex was required for this task, and cortical responses were not only similarly affected by contrast but predicted variability in behavioral performance. Combined, our results demonstrate that dynamic gain adaptation supports efficient coding in auditory cortex and predicts the perception of sounds in noise.

The computational power of the human brain

At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.

Laminar mechanisms of saccadic suppression in primate visual cortex

Saccadic eye movements are known to cause saccadic suppression, a temporary reduction in visual sensitivity and visual cortical firing rates. While saccadic suppression has been well characterized at the level of perception and single neurons, relatively little is known about the visual cortical networks governing this phenomenon. Here we examine the effects of saccadic suppression on distinct neural subpopulations within visual area V4. We find subpopulation-specific differences in the magnitude and timing of peri-saccadic modulation. Input-layer neurons show changes in firing rate and inter-neuronal correlations prior to saccade onset, and putative inhibitory interneurons in the input layer elevate their firing rate during saccades. A computational model of this circuit recapitulates our empirical observations and demonstrates that an input-layer-targeting pathway can initiate saccadic suppression by enhancing local inhibitory activity. Collectively, our results provide a mechanistic understanding of how eye movement signaling interacts with cortical circuitry to enforce visual stability.

Effective and efficient neural networks for spike inference from in vivo calcium imaging

Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a supervised data-driven approach to extract spike information from calcium signals. We propose the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system for spike-rate and spike-event predictions using ΔF/F0 calcium inputs based on a U-Net deep neural network. When testing on a large, ground-truth public database, it consistently outperformed state-of-the-art algorithms in both spike-rate and spike-event predictions with reduced computational load. We further demonstrated that ENS2 can be applied to analyses of orientation selectivity in primary visual cortex neurons. We conclude that it would be a versatile inference system that may benefit diverse neuroscience studies.

Stimulus-specific prediction error neurons in mouse auditory cortex

Comparing expectation with experience is an important neural computation performed throughout the brain and is a hallmark of predictive processing. Experiments that alter the sensory outcome of an animal's behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors - mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through non-predictive mechanisms, such as the movement-based facilitation of a neuron's inherent sound responses. If sensory prediction error neurons exist in sensory cortex, it is unknown whether they manifest as general error responses, or respond with specificity to errors in distinct stimulus dimensions. To answer these questions, we trained mice to expect the outcome of a simple sound-generating behavior and recorded auditory cortex activity as mice heard either the expected sound or sounds that deviated from expectation in one of multiple distinct dimensions. Our data reveal that the auditory cortex learns to suppress responses to self-generated sounds along multiple acoustic dimensions simultaneously. We identify a distinct population of auditory cortex neurons that are not responsive to passive sounds or to the expected sound but that explicitly encode prediction errors. These prediction error neurons are abundant only in animals with a learned motor-sensory expectation, and encode one or two specific violations rather than a generic error signal.

Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex

Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.

Voltage imaging reveals that hippocampal interneurons tune memory-encoding pyramidal sequences

Hippocampal spiking sequences encode and link behavioral information across time. How inhibition sculpts these sequences remains unknown. We performed longitudinal voltage imaging of CA1 parvalbumin- and somatostatin-expressing interneurons in mice during an odor-cued working memory task, before and after training. During this task, pyramidal odor-specific sequences encode the cue throughout a delay period. In contrast, most interneurons encoded odor delivery, but not odor identity, nor delay time. Population inhibition was stable across days, with constant field turnover, though some cells retained odor-responses for days. At odor onset, a brief, synchronous burst of parvalbumin cells was followed by widespread membrane hyperpolarization and then rebound theta-paced spiking, synchronized across cells. Two-photon calcium imaging revealed that most pyramidal cells were suppressed throughout the odor. Positive pyramidal odor-responses coincided with interneuronal rebound spiking; otherwise, they had weak odor-selectivity. Therefore, inhibition increases the signal-to-noise ratio of cue representations, which is crucial for entraining downstream targets.

Normalization in mouse primary visual cortex

When multiple stimuli appear together in the receptive field of a visual cortical neuron, the response is typically close to the average of that neuron's response to each individual stimulus. The departure from a linear sum of each individual response is referred to as normalization. In mammals, normalization has been best characterized in the visual cortex of macaques and cats. Here we study visually evoked normalization in the visual cortex of awake mice using optical imaging of calcium indicators in large populations of layer 2/3 (L2/3) V1 excitatory neurons and electrophysiological recordings across layers in V1. Regardless of recording method, mouse visual cortical neurons exhibit normalization to varying degrees. The distributions of normalization strength are similar to those described in cats and macaques, albeit slightly weaker on average.

Moving from phenomenological to predictive modelling: Progress and pitfalls of modelling brain stimulation in-silico

Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies.

Quantitative models of auditory cortical processing

To generate insight from experimental data, it is critical to understand the inter-relationships between individual data points and place them in context within a structured framework. Quantitative modeling can provide the scaffolding for such an endeavor. Our main objective in this review is to provide a primer on the range of quantitative tools available to experimental auditory neuroscientists. Quantitative modeling is advantageous because it can provide a compact summary of observed data, make underlying assumptions explicit, and generate predictions for future experiments. Quantitative models may be developed to characterize or fit observed data, to test theories of how a task may be solved by neural circuits, to determine how observed biophysical details might contribute to measured activity patterns, or to predict how an experimental manipulation would affect neural activity. In complexity, quantitative models can range from those that are highly biophysically realistic and that include detailed simulations at the level of individual synapses, to those that use abstract and simplified neuron models to simulate entire networks. Here, we survey the landscape of recently developed models of auditory cortical processing, highlighting a small selection of models to demonstrate how they help generate insight into the mechanisms of auditory processing. We discuss examples ranging from models that use details of synaptic properties to explain the temporal pattern of cortical responses to those that use modern deep neural networks to gain insight into human fMRI data. We conclude by discussing a biologically realistic and interpretable model that our laboratory has developed to explore aspects of vocalization categorization in the auditory pathway.

Circadian time- and sleep-dependent modulation of cortical parvalbumin-positive inhibitory neurons

Parvalbumin-positive neurons (PVs) are the main class of inhibitory neurons in the mammalian central nervous system. By examining diurnal changes in synaptic and neuronal activity of PVs in the supragranular layer of the mouse primary visual cortex (V1), we found that both PV input and output are modulated in a time- and sleep-dependent manner throughout the 24-h day. We first show that PV-evoked inhibition is stronger by the end of the light cycle (ZT12) relative to the end of the dark cycle (ZT0), which is in line with the lower inhibitory input of PV neurons at ZT12 than at ZT0. Interestingly, PV inhibitory and excitatory synaptic transmission slowly oscillate in opposite directions during the light/dark cycle. Although excitatory synapses are predominantly regulated by experience, inhibitory synapses are regulated by sleep, via acetylcholine activating M1 receptors. Consistent with synaptic regulation of PVs, we further show in vivo that spontaneous PV activity displays daily rhythm mainly determined by visual experience, which negatively correlates with the activity cycle of surrounding pyramidal neurons and the dorsal lateral geniculate nucleus-evoked responses in V1. These findings underscore the physiological significance of PV's daily modulation.

Pharmacological Signature and Target Specificity of Inhibitory Circuits Formed by Martinotti Cells in the Mouse Barrel Cortex

In the neocortex, fast synaptic inhibition orchestrates both spontaneous and sensory-evoked activity. GABAergic interneurons (INs) inhibit pyramidal neurons (PNs) directly, modulating their output activity and thus contributing to balance cortical networks. Moreover, several IN subtypes also inhibit other INs, forming specific disinhibitory circuits, which play crucial roles in several cognitive functions. Here, we studied a subpopulation of somatostatin-positive INs, the Martinotti cells (MCs) in layer 2/3 of the mouse barrel cortex (both sexes). MCs inhibit the distal portion of PN apical dendrites, thus controlling dendrite electrogenesis and synaptic integration. Yet, it is poorly understood whether MCs inhibit other elements of the cortical circuits, and the connectivity properties with non-PN targets are unknown. We found that MCs have a strong preference for PN dendrites, but they also considerably connect with parvalbumin-positive, vasoactive intestinal peptide-expressing, and layer 1 (L1) INs. Remarkably, GABAergic synapses from MCs exhibited clear cell type-specific short-term plasticity. Moreover, whereas the biophysical properties of MC-PN synapses were consistent with distal dendritic inhibition, MC-IN synapses exhibited characteristics of fast perisomatic inhibition. Finally, MC-PN connections used α5-containing GABA_A receptors (GABA_ARs), but this subunit was not expressed by the other INs targeted by MCs. We reveal a specialized connectivity blueprint of MCs within different elements of superficial cortical layers. In addition, our results identify α5-GABA_ARs as the molecular fingerprint of MC-PN dendritic inhibition. This is of critical importance, given the role of α5-GABA_ARs in cognitive performance and their involvement in several brain diseases.SIGNIFICANCE STATEMENT Martinotti cells (MCs) are a prominent, broad subclass of somatostatin-expressing GABAergic interneurons, specialized in controlling distal dendrites of pyramidal neurons (PNs) and taking part in several cognitive functions. Here we characterize the connectivity pattern of MCs with other interneurons in the superficial layers (L1 and L2/3) of the mouse barrel cortex. We found that the connectivity pattern of MCs with PNs as well as parvalbumin, vasoactive intestinal peptide, and L1 interneurons exhibit target-specific plasticity and biophysical properties. The specificity of α5-GABA_ARs at MC-PN synapses and the lack or functional expression of this subunit by other cell types define the molecular identity of MC-PN connections and the exclusive involvement of this inhibitory circuits in α5-dependent cognitive tasks.

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.

Disinhibitory circuit mediated by connections from vasoactive intestinal polypeptide to somatostatin interneurons underlies the paradoxical decrease in spike synchrony with increased border ownership selective neuron firing rate

The activity of border ownership selective (BOS) neurons in intermediate-level visual areas indicates which side of a contour owns a border relative to its classical receptive field and provides a fundamental component of figure-ground segregation. A physiological study reported that selective attention facilitates the activity of BOS neurons with a consistent border ownership preference, defined as two neurons tuned to respond to the same visual object. However, spike synchrony between this pair is significantly suppressed by selective attention. These neurophysiological findings are derived from a biologically-plausible microcircuit model consisting of spiking neurons including two subtypes of inhibitory interneurons, somatostatin (SOM) and vasoactive intestinal polypeptide (VIP) interneurons, and excitatory BOS model neurons. In our proposed model, BOS neurons and SOM interneurons cooperate and interact with each other. VIP interneurons not only suppress SOM interneuron responses but also are activated by feedback signals mediating selective attention, which leads to disinhibition of BOS neurons when they are directing selective attention toward an object. Our results suggest that disinhibition arising from the synaptic connections from VIP to SOM interneurons plays a critical role in attentional modulation of neurons in intermediate-level visual areas.

A kinetic-optimized CoChR variant with enhanced high-frequency spiking fidelity

Channelrhodopsins are a promising toolset for noninvasive optical manipulation of genetically identifiable neuron populations. Existing channelrhodopsins have generally suffered from a trade-off between two desired properties: fast channel kinetics and large photocurrent. Such a trade-off hinders spatiotemporally precise optogenetic activation during both one-photon and two-photon photostimulation. Furthermore, the simultaneous use of spectrally separated genetically encoded indicators and channelrhodopsins has generally suffered from non-negligible crosstalk in photocurrent or fluorescence. These limitations have hindered crosstalk-free dual-channel experiments needed to establish relationships between multiple neural populations. Recent large-scale transcriptome sequencing revealed one potent optogenetic actuator, the channelrhodopsin from species Chloromonas oogama (CoChR), which possessed high cyan light-driven photocurrent but slow channel kinetics. We rationally designed and engineered a kinetic-optimized CoChR variant that was faster than native CoChR while maintaining large photocurrent amplitude. When expressed in cultured hippocampal pyramidal neurons, our CoChR variant improved high-frequency spiking fidelity under one-photon illumination. Our CoChR variant's blue-shifted excitation spectrum enabled simultaneous cyan photostimulation and red calcium imaging with negligible photocurrent crosstalk.

Decoding of attentional state using local field potentials

We review recent efforts to decode visual spatial attention from different types of brain signals, such as spikes and local field potentials (LFPs). Combining signals from more electrodes improves decoding, but the pattern of improvement varies considerably depending on the signal as well as the task (for example, decoding of sensory stimulus/motor intention versus location of attention). We argue that this pattern of results conveys important information not only about the usefulness of a particular brain signal for decoding attention, but also about the spatial scale over which attention operates in the brain. The spatial scale, in turn, likely depends on the extent of underlying mechanisms such as normalization, gain control via excitation-inhibition interactions, and neuromodulatory regulation of attention.

A microcircuit model involving parvalbumin, somatostatin, and vasoactive intestinal polypeptide inhibitory interneurons for the modulation of neuronal oscillation during visual processing

Various subtypes of inhibitory interneurons contact one another to organize cortical networks. Most cortical inhibitory interneurons express 1 of 3 genes: parvalbumin (PV), somatostatin (SOM), or vasoactive intestinal polypeptide (VIP). This diversity of inhibition allows the flexible regulation of neuronal responses within and between cortical areas. However, the exact roles of these interneuron subtypes and of excitatory pyramidal (Pyr) neurons in regulating neuronal network activity and establishing perception (via interactions between feedforward sensory and feedback attentional signals) remain largely unknown. To explore the regulatory roles of distinct neuronal types in cortical computation, we developed a computational microcircuit model with biologically plausible visual cortex layers 2/3 that combined Pyr neurons and the 3 inhibitory interneuron subtypes to generate network activity. In simulations with our model, inhibitory signals from PV and SOM neurons preferentially induced neuronal firing at gamma (30-80 Hz) and beta (20-30 Hz) frequencies, respectively, in agreement with observed physiological results. Furthermore, our model indicated that rapid inhibition from VIP to SOM subtypes underlies marked attentional modulation for low-gamma frequency (30-50 Hz) in Pyr neuron responses. Our results suggest the distinct but cooperative roles of inhibitory interneuron subtypes in the establishment of visual perception.

Dynamics and Mechanisms of Contrast-Dependent Modulation of Spatial-Frequency Tuning in the Early Visual Cortex

The spatial-frequency (SF) tuning of neurons in the early visual cortex is adjusted for stimulus contrast. As the contrast increases, SF tuning is modulated so that the transmission of fine features is facilitated. A variety of mechanisms are involved in shaping SF tunings, but those responsible for the contrast-dependent modulations are unclear. To address this, we measured the time course of SF tunings of area 17 neurons in male cats under different contrasts with a reverse correlation. After response onset, the optimal SF continuously shifted to a higher SF over time, with a larger shift for higher contrast. At high contrast, whereas neurons with a large shift of optimal SF exhibited a large bandwidth decrease, those with a negligible shift increased the bandwidth over time. Between these two extremes, the degree of SF shift and bandwidth change continuously varied. At low contrast, bandwidth generally decreased over time. These dynamic effects enhanced the processing of high-frequency range under a high-contrast condition and allowed time-average SF tuning curves to show contrast-dependent modulation, like that of steady-state SF tuning curves reported previously. Combinations of two mechanisms, one that decreases bandwidth and shifts optimal SF, and another that increases bandwidth without shifting optimal SF, would explain the full range of SF tuning dynamics. Our results indicate that one of the essential roles of tuning dynamics of area 17 neurons, which have been observed for various visual features, is to adjust tunings depending on contrast.SIGNIFICANCE STATEMENT The spatial scales of features transmitted by cortical neurons are adjusted depending on stimulus contrast. However, the underlying mechanism is not fully understood. We measured the time course of spatial frequency tunings of cat area 17 neurons under different contrast conditions and observed a variety of dynamic effects that contributed to spatial-scale adjustment, allowing neurons to adjust their spatial frequency tuning range depending on contrast. Our results suggest that one of the essential roles of tuning dynamics of area 17 neurons, which have been observed for various visual features, is to adjust tunings depending on contrast.

Characteristics and Impact of the rNST GABA Network on Neural and Behavioral Taste Responses

The rostral nucleus of the solitary tract (rNST), the initial CNS site for processing gustatory information, is comprised of two major cell types, glutamatergic excitatory and GABAergic inhibitory neurons. Although many investigators have described taste responses of rNST neurons, the phenotypes of these cells were unknown. To directly compare the response characteristics of both inhibitory and noninhibitory neurons, we recorded from mice expressing Channelrhodopsin-2 (ChR2) under the control of GAD65, a synthetic enzyme for GABA. We observed that chemosensitive profiles of GABAergic taste neurons (G+TASTE) were similar to non-GABA taste neurons (G-TASTE) but had much lower response rates. We further observed a novel subpopulation of GABA cells located more ventrally in the nucleus that were unresponsive to taste stimulation (G+UNR), suggesting pathways for inhibition initiated by centrifugal sources. This preparation also allowed us to determine how optogenetic activation of the rNST GABA network impacted the taste responses of G-TASTE neurons. Activating rNST inhibitory circuitry suppressed gustatory responses of G-TASTE neurons across all qualities and chemosensitive types of neurons. Although the tuning curves of identified G-TASTE were modestly sharpened, the overall shape of response profiles and the ensemble pattern remained highly stable. These neurophysiological effects were consistent with the behavioral consequences of activating GAD65-expressing inhibitory neurons using DREADDs. In a brief-access licking task, concentration-response curves to both palatable (sucrose, maltrin) and unpalatable (quinine) stimuli were shifted to the right when GABA neurons were activated. Thus, the rNST GABAergic network is poised to modulate taste intensity across the qualitative and hedonic spectrum.

Visual-area-specific tonic modulation of GABA release by endocannabinoids sets the activity and coordination of neocortical principal neurons

Perisomatic inhibition of pyramidal neurons (PNs) coordinates cortical network activity during sensory processing, and this role is mainly attributed to parvalbumin-expressing basket cells (BCs). However, cannabinoid receptor type 1 (CB1)-expressing interneurons are also BCs, but the connectivity and function of these elusive but prominent neocortical inhibitory neurons are unclear. We find that their connectivity pattern is visual area specific. Persistently active CB1 signaling suppresses GABA release from CB1 BCs in the medial secondary visual cortex (V2M), but not in the primary visual cortex (V1). Accordingly, in vivo, tonic CB1 signaling is responsible for higher but less coordinated PN activity in the V2M than in the V1. These differential firing dynamics in the V1 and V2M can be captured by a computational network model that incorporates visual-area-specific properties. Our results indicate a differential CB1-mediated mechanism controlling PN activity, suggesting an alternative connectivity scheme of a specific GABAergic circuit in different cortical areas.

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CB1⁺ basket cells exhibit visual-area-specific morphology and connectivity patterns

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Tonic CB1 signaling underlies high pyramidal neurons (PN) activity in V2M but not V1

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Tonic CB1 signaling differentially modulates PN-correlated activity in V1 and V2M

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Numerical simulations capture specific CB1-dependent firing dynamics of V1 and V2M

Cascaded normalizations for spatial integration in the primary visual cortex of primates

Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.