Multilaminar networks of cortical neurons integrate common inputs from sensory thalamus

A dendritic mechanism for balancing synaptic flexibility and stability

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Primary auditory thalamus relays directly to cortical layer 1 interneurons

Inhibitory interneurons within cortical layer 1 (L1-INs) integrate inputs from diverse brain regions to modulate sensory processing and plasticity, but the sensory inputs that recruit these interneurons have not been identified. Here we used monosynaptic retrograde tracing and whole-cell electrophysiology to characterize the thalamic inputs onto two major subpopulations of L1-INs in the mouse auditory cortex. We find that the vast majority of auditory thalamic inputs to these L1-INs unexpectedly arise from the ventral subdivision of the medial geniculate body (MGBv), the tonotopically-organized primary auditory thalamus. Moreover, these interneurons receive robust functional monosynaptic MGBv inputs that are comparable to those recorded in the L4 excitatory pyramidal neurons. Our findings identify a direct pathway from the primary auditory thalamus to the L1-INs, suggesting that these interneurons are uniquely positioned to integrate thalamic inputs conveying precise sensory information with top-down inputs carrying information about brain states and learned associations.

High-throughput volumetric mapping of synaptic transmission

Volumetric imaging of synaptic transmission in vivo requires high spatial and high temporal resolution. Shaping the wavefront of two-photon fluorescence excitation light, we developed Bessel-droplet foci for high-contrast and high-resolution volumetric imaging of synapses. Applying our method to imaging glutamate release, we demonstrated high-throughput mapping of excitatory inputs at >1,000 synapses per volume and >500 dendritic spines per neuron in vivo and unveiled previously unseen features of functional synaptic organization in the mouse primary visual cortex.

Functional specificity of recurrent inhibition in visual cortex

In the neocortex, neural activity is shaped by the interaction of excitatory and inhibitory neurons, defined by the organization of their synaptic connections. Although connections among excitatory pyramidal neurons are sparse and functionally tuned, inhibitory connectivity is thought to be dense and largely unstructured. By measuring in vivo visual responses and synaptic connectivity of parvalbumin-expressing (PV+) inhibitory cells in mouse primary visual cortex, we show that the synaptic weights of their connections to nearby pyramidal neurons are specifically tuned according to the similarity of the cells' responses. Individual PV+ cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This structured organization of inhibitory synaptic weights provides a circuit mechanism for tuned inhibition onto pyramidal cells despite dense connectivity, stabilizing activity within feature-specific excitatory ensembles while supporting competition between them.

Microstimulation of sensory cortex engages natural sensory representations

Cortical activity patterns occupy a small subset of possible network states. If this is due to intrinsic network properties, microstimulation of sensory cortex should evoke activity patterns resembling those observed during natural sensory input. Here, we use optical microstimulation of virally transfected layer 2/3 pyramidal neurons in the mouse primary vibrissal somatosensory cortex to compare artificially evoked activity with natural activity evoked by whisker touch and movement ("whisking"). We find that photostimulation engages touch- but not whisking-responsive neurons more than expected by chance. Neurons that respond to photostimulation and touch or to touch alone exhibit higher spontaneous pairwise correlations than purely photoresponsive neurons. Exposure to several days of simultaneous touch and optogenetic stimulation raises both overlap and spontaneous activity correlations among touch and photoresponsive neurons. We thus find that cortical microstimulation engages existing cortical representations and that repeated co-presentation of natural and artificial stimulation enhances this effect.

Correlated Somatosensory Input in Parvalbumin/Pyramidal Cells in Mouse Motor Cortex

In mammalian cortex, feedforward excitatory connections recruit feedforward inhibition. This is often carried by parvalbumin (PV+) interneurons, which may densely connect to local pyramidal (Pyr) neurons. Whether this inhibition affects all local excitatory cells indiscriminately or is targeted to specific subnetworks is unknown. Here, we test how feedforward inhibition is recruited by using two-channel circuit mapping to excite cortical and thalamic inputs to PV+ interneurons and Pyr neurons to mouse primary vibrissal motor cortex (M1). Single Pyr and PV+ neurons receive input from both cortex and thalamus. Connected pairs of PV+ interneurons and excitatory Pyr neurons receive correlated cortical and thalamic inputs. While PV+ interneurons are more likely to form local connections to Pyr neurons, Pyr neurons are much more likely to form reciprocal connections with PV+ interneurons that inhibit them. This suggests that Pyr and PV ensembles may be organized based on their local and long-range connections, an organization that supports the idea of local subnetworks for signal transduction and processing. Excitatory inputs to M1 can thus target inhibitory networks in a specific pattern which permits recruitment of feedforward inhibition to specific subnetworks within the cortical column.

The fractal brain: scale-invariance in structure and dynamics

The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.

Haploinsufficiency of Shank3 increases the orientation selectivity of V1 neurons

Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose hallmarks are social deficits, language impairment, repetitive behaviors, and sensory alterations. It has been reported that patients with ASD show differential activity in cortical regions, for instance, increased neuronal activity in visual processing brain areas and atypical visual perception compared with healthy subjects. The causes of these alterations remain unclear, although many studies demonstrate that ASD has a strong genetic correlation. An example is Phelan-McDermid syndrome, caused by a deletion of the Shank3 gene in one allele of chromosome 22. However, the neuronal consequences relating to the haploinsufficiency of Shank3 in the brain remain unknown. Given that sensory abnormalities are often present along with the core symptoms of ASD, our goal was to study the tuning properties of the primary visual cortex to orientation and direction in awake, head-fixed Shank3^+/- mice. We recorded neural activity in vivo in response to visual gratings in the primary visual cortex from a mouse model of ASD (Shank3^+/- mice) using the genetically encoded calcium indicator GCaMP6f, imaged with a two-photon microscope through a cranial window. We found that Shank3^+/- mice showed a higher proportion of neurons responsive to drifting gratings stimuli than wild-type mice. Shank3^+/- mice also show increased responses to some specific stimuli. Furthermore, analyzing the distributions of neurons for the tuning width, we found that Shank3^+/- mice have narrower tuning widths, which was corroborated by analyzing the orientation selectivity. Regarding this, Shank3^+/- mice have a higher proportion of selective neurons, specifically neurons showing increased selectivity to orientation but not direction. Thus, the haploinsufficiency of Shank3 modified the neuronal response of the primary visual cortex.

In vivo extracellular recordings of thalamic and cortical visual responses reveal V1 connectivity rules

The brain's connectome provides the scaffold for canonical neural computations. However, a comparison of connectivity studies in the mouse primary visual cortex (V1) reveals that the average number and strength of connections between specific neuron types can vary. Can variability in V1 connectivity measurements coexist with canonical neural computations? We developed a theory-driven approach to deduce V1 network connectivity from visual responses in mouse V1 and visual thalamus (dLGN). Our method revealed that the same recorded visual responses were captured by multiple connectivity configurations. Remarkably, the magnitude and selectivity of connectivity weights followed a specific order across most of the inferred connectivity configurations. We argue that this order stems from the specific shapes of the recorded contrast response functions and contrast invariance of orientation tuning. Remarkably, despite variability across connectivity studies, connectivity weights computed from individual published connectivity reports followed the order we identified with our method, suggesting that the relations between the weights, rather than their magnitudes, represent a connectivity motif supporting canonical V1 computations.

Functional and structural features of L2/3 pyramidal cells continuously covary with pial depth in mouse visual cortex

Pyramidal cells of neocortical layer 2/3 (L2/3 PyrCs) integrate signals from numerous brain areas and project throughout the neocortex. These PyrCs show pial depth-dependent functional and structural specializations, indicating participation in different functional microcircuits. However, whether these depth-dependent differences result from separable PyrC subtypes or whether their features display a continuum correlated with pial depth is unknown. Here, we assessed the stimulus selectivity, electrophysiological properties, dendritic morphology, and excitatory and inhibitory connectivity across the depth of L2/3 in the binocular visual cortex of mice. We find that the apical, but not the basal dendritic tree structure, varies with pial depth, which is accompanied by variation in subthreshold electrophysiological properties. Lower L2/3 PyrCs receive increased input from L4, while upper L2/3 PyrCs receive a larger proportion of intralaminar input. In vivo calcium imaging revealed a systematic change in visual responsiveness, with deeper PyrCs showing more robust responses than superficial PyrCs. Furthermore, deeper PyrCs are more driven by contralateral than ipsilateral eye stimulation. Importantly, the property value transitions are gradual, and L2/3 PyrCs do not display discrete subtypes based on these parameters. Therefore, L2/3 PyrCs' multiple functional and structural properties systematically correlate with their depth, forming a continuum rather than discrete subtypes.

The role of higher order thalamus during learning and correct performance in goal-directed behavior

The thalamus is a gateway to the cortex. Cortical encoding of complex behavior can therefore only be understood by considering the thalamic processing of sensory and internally generated information. Here, we use two-photon Ca²⁺ imaging and optogenetics to investigate the role of axonal projections from the posteromedial nucleus of the thalamus (POm) to the forepaw area of the mouse primary somatosensory cortex (forepaw S1). By recording the activity of POm axonal projections within forepaw S1 during expert and chance performance in two tactile goal-directed tasks, we demonstrate that POm axons increase activity in the response and, to a lesser extent, reward epochs specifically during correct HIT performance. When performing at chance level during learning of a new behavior, POm axonal activity was decreased to naive rates and did not correlate with task performance. However, once evoked, the Ca²⁺ transients were larger than during expert performance, suggesting POm input to S1 differentially encodes chance and expert performance. Furthermore, the POm influences goal-directed behavior, as photoinactivation of archaerhodopsin-expressing neurons in the POm decreased the learning rate and overall success in the behavioral task. Taken together, these findings expand the known roles of the higher-thalamic nuclei, illustrating the POm encodes and influences correct action during learning and performance in a sensory-based goal-directed behavior.

Orientation and direction tuning align with dendritic morphology and spatial connectivity in mouse visual cortex

The functional properties of neocortical pyramidal cells (PCs), such as direction and orientation selectivity in visual cortex, predominantly derive from their excitatory and inhibitory inputs. For layer 2/3 (L2/3) PCs, the detailed relationship between their functional properties and how they sample and integrate information across cortical space is not fully understood. Here, we study this relationship by combining functional in vivo two-photon calcium imaging, in vitro functional circuit mapping, and dendritic reconstruction of the same L2/3 PCs in mouse visual cortex. Our work reveals direct correlations between dendritic morphology and functional input connectivity and the orientation as well as direction tuning of L2/3 PCs. First, the apical dendritic tree is elongated along the postsynaptic preferred orientation, considering the representation of visual space in the cortex as determined by its retinotopic organization. Additionally, sharply orientation-tuned cells show a less complex apical tree compared with broadly tuned cells. Second, in direction-selective L2/3 PCs, the spatial distribution of presynaptic partners is offset from the soma opposite to the preferred direction. Importantly, although the presynaptic excitatory and inhibitory input distributions spatially overlap on average, the excitatory input distribution is spatially skewed along the preferred direction, in contrast to the inhibitory distribution. Finally, the degree of asymmetry is positively correlated with the direction selectivity of the postsynaptic L2/3 PC. These results show that the dendritic architecture and the spatial arrangement of excitatory and inhibitory presynaptic cells of L2/3 PCs play important roles in shaping their orientation and direction tuning.

Pyramidal tract neurons drive amplification of excitatory inputs to striatum through cholinergic interneurons

Corticostriatal connectivity is central for many cognitive and motor processes, such as reinforcement or action initiation and invigoration. The cortical input to the striatum arises from two main cortical populations: intratelencephalic (IT) and pyramidal tract (PT) neurons. We report a previously unknown excitatory circuit, supported by a polysynaptic motif from PT neurons to cholinergic interneurons (ChIs) to glutamate-releasing axons, which runs in parallel to the canonical monosynaptic corticostriatal connection. This motif conveys a delayed second phase of excitation to striatal spiny projection neurons, through an acetylcholine-dependent glutamate release mechanism mediated by α4-containing nicotinic receptors, resulting in biphasic corticostriatal signals. These biphasic signals are a hallmark of PT, but not IT, corticostriatal inputs, due to a stronger relative input from PT neurons to ChIs. These results describe a previously unidentified circuit mechanism by which PT activity amplifies excitatory inputs to the striatum, with potential implications for behavior, plasticity, and learning.

Synaptic connectivity to L2/3 of primary visual cortex measured by two-photon optogenetic stimulation

Understanding cortical microcircuits requires thorough measurement of physiological properties of synaptic connections formed within and between diverse subclasses of neurons. Towards this goal, we combined spatially precise optogenetic stimulation with multicellular recording to deeply characterize intralaminar and translaminar monosynaptic connections to supragranular (L2/3) neurons in the mouse visual cortex. The reliability and specificity of multiphoton optogenetic stimulation were measured across multiple Cre lines, and measurements of connectivity were verified by comparison to paired recordings and targeted patching of optically identified presynaptic cells. With a focus on translaminar pathways, excitatory and inhibitory synaptic connections from genetically defined presynaptic populations were characterized by their relative abundance, spatial profiles, strength, and short-term dynamics. Consistent with the canonical cortical microcircuit, layer 4 excitatory neurons and interneurons within L2/3 represented the most common sources of input to L2/3 pyramidal cells. More surprisingly, we also observed strong excitatory connections from layer 5 intratelencephalic neurons and potent translaminar inhibition from multiple interneuron subclasses. The hybrid approach revealed convergence to and divergence from excitatory and inhibitory neurons within and across cortical layers. Divergent excitatory connections often spanned hundreds of microns of horizontal space. In contrast, divergent inhibitory connections were more frequently measured from postsynaptic targets near each other.

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

Systems memory consolidation involves the transfer of memories across brain regions and the transformation of memory content. For example, declarative memories that transiently depend on the hippocampal formation are transformed into long-term memory traces in neocortical networks, and procedural memories are transformed within cortico-striatal networks. These consolidation processes are thought to rely on replay and repetition of recently acquired memories, but the cellular and network mechanisms that mediate the changes of memories are poorly understood. Here, we suggest that systems memory consolidation could arise from Hebbian plasticity in networks with parallel synaptic pathways-two ubiquitous features of neural circuits in the brain. We explore this hypothesis in the context of hippocampus-dependent memories. Using computational models and mathematical analyses, we illustrate how memories are transferred across circuits and discuss why their representations could change. The analyses suggest that Hebbian plasticity mediates consolidation by transferring a linear approximation of a previously acquired memory into a parallel pathway. Our modelling results are further in quantitative agreement with lesion studies in rodents. Moreover, a hierarchical iteration of the mechanism yields power-law forgetting-as observed in psychophysical studies in humans. The predicted circuit mechanism thus bridges spatial scales from single cells to cortical areas and time scales from milliseconds to years.

A long-range, recurrent neuronal network linking the emotion regions with the somatic motor cortex

Recurrent neural networks (RNNs) are designed to learn sequential patterns in silico, but it is unclear whether and how an RNN forms in the native networks of the mammalian brain. Here, we report an innate RNN, which is formed by the unidirectional connections from three basic units: input units arriving from emotion regions, a hidden unit in the medial prefrontal cortex (mPFC), and output units located at the somatic motor cortex (sMO). Specifically, the neurons from basal lateral amygdala (BLA) and the insular cortex (IC) project to the mPFC motor-cortex-projecting (MP) neurons. These MP neurons form a local self-feedback loop and target major projecting neurons of the sMO. Within the sMO, the neurons in the infragranular layers receive stronger input than the neurons in supragranular layers. Finally, we show in vivo evidence that the communications from the emotion regions to the sMO are abolished when MP neurons are chemogenetically silenced.

Auditory input enhances somatosensory encoding and tactile goal-directed behavior

The capacity of the brain to encode multiple types of sensory input is key to survival. Yet, how neurons integrate information from multiple sensory pathways and to what extent this influences behavior is largely unknown. Using two-photon Ca2+ imaging, optogenetics and electrophysiology in vivo and in vitro, we report the influence of auditory input on sensory encoding in the somatosensory cortex and show its impact on goal-directed behavior. Monosynaptic input from the auditory cortex enhanced dendritic and somatic encoding of tactile stimulation in layer 2/3 (L2/3), but not layer 5 (L5), pyramidal neurons in forepaw somatosensory cortex (S1). During a tactile-based goal-directed task, auditory input increased dendritic activity and reduced reaction time, which was abolished by photoinhibition of auditory cortex projections to forepaw S1. Taken together, these results indicate that dendrites of L2/3 pyramidal neurons encode multisensory information, leading to enhanced neuronal output and reduced response latency during goal-directed behavior.

How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model

The mouse, as a model organism to study the brain, gives us unprecedented experimental access to the mammalian cerebral cortex. By determining the cortex's cellular composition, revealing the interaction between its different components, and systematically perturbing these components, we are obtaining mechanistic insight into some of the most basic properties of cortical function. In this review, we describe recent advances in our understanding of how circuits of cortical neurons implement computations, as revealed by the study of mouse primary visual cortex. Further, we discuss how studying the mouse has broadened our understanding of the range of computations performed by visual cortex. Finally, we address how future approaches will fulfill the promise of the mouse in elucidating fundamental operations of cortex.

Untangling the cortico-thalamo-cortical loop: cellular pieces of a knotty circuit puzzle

Functions of the neocortex depend on its bidirectional communication with the thalamus, via cortico-thalamo-cortical (CTC) loops. Recent work dissecting the synaptic connectivity in these loops is generating a clearer picture of their cellular organization. Here, we review findings across sensory, motor and cognitive areas, focusing on patterns of cell type-specific synaptic connections between the major types of cortical and thalamic neurons. We outline simple and complex CTC loops, and note features of these loops that appear to be general versus specialized. CTC loops are tightly interlinked with local cortical and corticocortical (CC) circuits, forming extended chains of loops that are probably critical for communication across hierarchically organized cerebral networks. Such CTC-CC loop chains appear to constitute a modular unit of organization, serving as scaffolding for area-specific structural and functional modifications. Inhibitory neurons and circuits are embedded throughout CTC loops, shaping the flow of excitation. We consider recent findings in the context of established CTC and CC circuit models, and highlight current efforts to pinpoint cell type-specific mechanisms in CTC loops involved in consciousness and perception. As pieces of the connectivity puzzle fall increasingly into place, this knowledge can guide further efforts to understand structure-function relationships in CTC loops.

A model integrating multiple processes of synchronization and coherence for information instantiation within a cortical area

What is the form of dynamic, e.g., sensory, information in the mammalian cortex? Information in the cortex is modeled as a coherence map of a mixed chimera state of synchronous, phasic, and disordered minicolumns. The theoretical model is built on neurophysiological evidence. Complex spatiotemporal information is instantiated through a system of interacting biological processes that generate a synchronized cortical area, a coherent aperture. Minicolumn elements are grouped in macrocolumns in an array analogous to a phased-array radar, modeled as an aperture, a "hole through which radiant energy flows." Coherence maps in a cortical area transform inputs from multiple sources into outputs to multiple targets, while reducing complexity and entropy. Coherent apertures can assume extremely large numbers of different information states as coherence maps, which can be communicated among apertures with corresponding very large bandwidths. The coherent aperture model incorporates considerable reported research, integrating five conceptually and mathematically independent processes: 1) a damped Kuramoto network model, 2) a pumped area field potential, 3) the gating of nearly coincident spikes, 4) the coherence of activity across cortical lamina, and 5) complex information formed through functions in macrocolumns. Biological processes and their interactions are described in equations and a functional circuit such that the mathematical pieces can be assembled the same way the neurophysiological ones are. The model can be conceptually convolved over the specifics of local cortical areas within and across species. A coherent aperture becomes a node in a graph of cortical areas with a corresponding distribution of information.

Cell-type-specific imaging of neurotransmission reveals a disrupted excitatory-inhibitory cortical network in isoflurane anaesthesia

BACKGROUND

Despite the fundamental clinical significance of general anaesthesia, the cortical mechanism underlying anaesthetic-induced loss of consciousness (aLOC) remains elusive.

METHODS

Here, we measured the dynamics of two major cortical neurotransmitters, gamma-aminobutyric acid (GABA) and glutamate, through in vivo two-photon imaging and genetically encoded neurotransmitter sensors in a cell type-specific manner in the primary visual (V1) cortex.

FINDINGS

We found a general decrease in cortical GABA transmission during aLOC. However, the glutamate transmission varies among different cortical cell types, where in it is almost preserved on pyramidal cells and is significantly reduced on inhibitory interneurons. Cortical interneurons expressing vasoactive intestinal peptide (VIP) and parvalbumin (PV) specialize in disinhibitory and inhibitory effects, respectively. During aLOC, VIP neuronal activity was delayed, and PV neuronal activity was dramatically inhibited and highly synchronized.

INTERPRETATION

These data reveal that aLOC resembles a cortical state with a disrupted excitatory-inhibitory network and suggest that a functional inhibitory network is indispensable in the maintenance of consciousness.

FUNDING

This work was supported by the grants of the National Natural Science Foundation of China (grant nos. 81620108012 and 82030038 to H.D. and grant nos. 31922029, 61890951, and 61890950 to J.H.).

Laminar-specific cortico-cortical loops in mouse visual cortex

Many theories propose recurrent interactions across the cortical hierarchy, but it is unclear if cortical circuits are selectively wired to implement looped computations. Using subcellular channelrhodopsin-2-assisted circuit mapping in mouse visual cortex, we compared feedforward (FF) or feedback (FB) cortico-cortical (CC) synaptic input to cells projecting back to the input source (looped neurons) with cells projecting to a different cortical or subcortical area. FF and FB afferents showed similar cell-type selectivity, making stronger connections with looped neurons than with other projection types in layer (L)5 and L6, but not in L2/3, resulting in selective modulation of activity in looped neurons. In most cases, stronger connections in looped L5 neurons were located on their apical tufts, but not on their perisomatic dendrites. Our results reveal that CC connections are selectively wired to form monosynaptic excitatory loops and support a differential role of supragranular and infragranular neurons in hierarchical recurrent computations.

Survey of spiking in the mouse visual system reveals functional hierarchy

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically¹. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory²-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas³. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits

Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.

A Gradient of Hippocampal Inputs to the Medial Mesocortex

Memory-guided decisions depend on complex interactions between the hippocampus (HIPP) and medial mesocortical (MMC) regions, including the anterior cingulate (CG) and retrosplenial (RSC). The functional circuitry underlying these interactions is unclear. Using anatomy, electrophysiology, and optogenetics, we show that such circuitry is characterized by a functional-anatomical gradient. While the CG receives hippocampal excitatory projections originated in CA1 stratum pyramidale, the RSC additionally receives long-range inhibitory inputs from radiatum and lacunosum-moleculare. Such hippocampal projections establish bona fide synapses, with the RSC densely targeted on its superficial layers L1-L3 by a combination of inhibitory and excitatory synapses. We show that the MMC is targeted by dorsal-intermediate CA1 (diCA1) axons following a caudorostral gradient in which a dense, dual (excitatory/inhibitory), layer-specific projection is progressively converted in a sparse, excitatory, and diffuse projection. This gradient is reflected in higher oscillatory synchronicity between the HIPP and RSC in the awake-behaving animal, compatible with their known functional proximity and contrasting with that found in the CG.

Circuitry Underlying Experience-Dependent Plasticity in the Mouse Visual System

Since the discovery of ocular dominance plasticity, neuroscientists have understood that changes in visual experience during a discrete developmental time, the critical period, trigger robust changes in the visual cortex. State-of-the-art tools used to probe connectivity with cell-type-specific resolution have expanded the understanding of circuit changes underlying experience-dependent plasticity. Here, we review the visual circuitry of the mouse, describing projections from retina to thalamus, between thalamus and cortex, and within cortex. We discuss how visual circuit development leads to precise connectivity and identify synaptic loci, which can be altered by activity or experience. Plasticity extends to visual features beyond ocular dominance, involving subcortical and cortical regions, and connections between cortical inhibitory interneurons. Experience-dependent plasticity contributes to the alignment of networks spanning retina to thalamus to cortex. Disruption of this plasticity may underlie aberrant sensory processing in some neurodevelopmental disorders.

Unveiling the Synaptic Function and Structure Using Paired Recordings From Synaptically Coupled Neurons

Synaptic transmission between neurons is the basic mechanism for information processing in cortical microcircuits. To date, paired recording from synaptically coupled neurons is the most widely used method which allows a detailed functional characterization of unitary synaptic transmission at the cellular and synaptic level in combination with a structural characterization of both pre- and postsynaptic neurons at the light and electron microscopic level. In this review, we will summarize the many applications of paired recordings to investigate synaptic function and structure. Paired recordings have been used to study the detailed electrophysiological and anatomical properties of synaptically coupled cell pairs within a synaptic microcircuit; this is critical in order to understand the connectivity rules and dynamic properties of synaptic transmission. Paired recordings can also be adopted for quantal analysis of an identified synaptic connection and to study the regulation of synaptic transmission by neuromodulators such as acetylcholine, the monoamines, neuropeptides, and adenosine etc. Taken together, paired recordings from synaptically coupled neurons will remain a very useful approach for a detailed characterization of synaptic transmission not only in the rodent brain but also that of other species including humans.

NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity

Neuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology comes with certain challenges in term of complex subgraph matching and the difficulty in computing intermediate shapes in between two imaged temporal samples. Here we report a novel, efficacious graph-theoretic method that rises to the challenges. The morphology of a neuron, which consists of its overall size, global shape, local branch patterns, and cell-specific biophysical properties, can vary significantly with the cell's identity, location, as well as developmental and physiological state. Various algorithms have been developed to customize shape based statistical and graph related features for quantitative analysis of neuromorphology, followed by the classification of neuron cell types using the features. Unlike the classical feature extraction based methods from imaged or 3D reconstructed neurons, we propose a model based on the rooted path decomposition from the soma to the dendrites of a neuron and extract morphological features from each constituent path. We hypothesize that measuring the distance between two neurons can be realized by minimizing the cost of continuously morphing the set of all rooted paths of one neuron to another. To validate this claim, we first establish the correspondence of paths between two neurons using a modified Munkres algorithm. Next, an elastic deformation framework that employs the square root velocity function is established to perform the continuous morphing, which, as an added benefit, provides an effective visualization tool. We experimentally show the efficacy of NeuroPath2Path, NeuroP2P, over the state of the art.

Long-term Monocular Deprivation during Juvenile Critical Period Disrupts Binocular Integration in Mouse Visual Thalamus

Study of the neural deficits caused by mismatched binocular vision in early childhood has predominantly focused on circuits in the primary visual cortex (V1). Recent evidence has revealed that neurons in mouse dorsolateral geniculate nucleus (dLGN) can undergo rapid ocular dominance plasticity following monocular deprivation (MD). It remains unclear, however, whether the long-lasting deficits attributed to MD during the critical period originate in the thalamus. Using in vivo two-photon Ca2+ imaging of dLGN afferents in superficial layers of V1 in female and male mice, we demonstrate that 14 d MD during the critical period leads to a chronic loss of binocular dLGN inputs while sparing response strength and spatial acuity. Importantly, MD leads to profoundly mismatched visual tuning properties in remaining binocular dLGN afferents. Furthermore, MD impairs binocular modulation, reducing facilitation of responses of both binocular and monocular dLGN inputs during binocular viewing. As predicted by our findings in thalamic inputs, Ca2+ imaging from V1 neurons revealed spared spatial acuity but impaired binocularity in L4 neurons. V1 L2/3 neurons in contrast displayed deficits in both binocularity and spatial acuity. Our data demonstrate that critical-period MD produces long-lasting disruptions in binocular integration beginning in early binocular circuits in dLGN, whereas spatial acuity deficits first arise from circuits further downstream in V1. Our findings indicate that the development of normal binocular vision and spatial acuity depend upon experience-dependent refinement of distinct stages in the mammalian visual system.SIGNIFICANCE STATEMENT Abnormal binocular vision and reduced acuity are hallmarks of amblyopia, a disorder that affects 2%-5% of the population. It is widely thought that the neural deficits underlying amblyopia begin in the circuits of primary visual cortex. Using in vivo two-photon calcium imaging of thalamocortical axons in mice, we show that depriving one eye of input during a critical period in development chronically impairs binocular integration in thalamic inputs to primary visual cortex. In contrast, visual acuity is spared in thalamic inputs. These findings shed new light on the role for developmental mechanisms in the thalamus in establishing binocular vision and may have critical implications for amblyopia.

Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex

Mouse primary somatosensory barrel cortex (wS1) processes whisker sensory information, receiving input from two distinct thalamic nuclei. The first-order ventral posterior medial (VPM) somatosensory thalamic nucleus most densely innervates layer 4 (L4) barrels, whereas the higher-order posterior thalamic nucleus (medial part, POm) most densely innervates L1 and L5A. We optogenetically stimulated VPM or POm axons, and recorded evoked excitatory postsynaptic potentials (EPSPs) in different cell-types across cortical layers in wS1. We found that excitatory neurons and parvalbumin-expressing inhibitory neurons received the largest EPSPs, dominated by VPM input to L4 and POm input to L5A. In contrast, somatostatin-expressing inhibitory neurons received very little input from either pathway in any layer. Vasoactive intestinal peptide-expressing inhibitory neurons received an intermediate level of excitatory input with less apparent layer-specificity. Our data help understand how wS1 neocortical microcircuits might process and integrate sensory and higher-order inputs.

Layer-specific modulation of pyramidal cell excitability by electroconvulsive shock

Dysregulation of cortical excitability crucially involves in behavioral and cognitive deficits of neurodegenerative and neuropsychiatric diseases. Electroconvulsive shock (ECS) changes neuronal excitability and has been used in the therapy of major depressive disorder and mood disorders. However, the action and the targets of the ECS in the cortical circuits are still poorly understood. Here we show that the ECS differently changes intrinsic properties of pyramidal cells (PCs) among superficial and deep layers. In layer 2/3 PCs, the ECS induced membrane hyperpolarization and the reduction of input resistances. In layer 5 PCs, the ECS also induced membrane hyperpolarization but had little effects on input resistances. In layer 6 PCs, the ECS had no effects on both of resting membrane potentials and input resistances. In addition, the ECS reduced the firing frequency of PCs in layer 2/3 but not in both layers 5 and 6. We further examined the ECS-induced changes in the influx of Ca²⁺ currents, and observed an enhanced Ca²⁺ currents in PCs of both layers 2/3 and 5 but not of layer 6. Thus, this study suggests the layer-specific suppression of PC excitability which will underlie the mechanism of the ECS action on the cortical activity.

Control of synaptic plasticity in deep cortical networks

Humans and many other animals have an enormous capacity to learn about sensory stimuli and to master new skills. However, many of the mechanisms that enable us to learn remain to be understood. One of the greatest challenges of systems neuroscience is to explain how synaptic connections change to support maximally adaptive behaviour. Here, we provide an overview of factors that determine the change in the strength of synapses, with a focus on synaptic plasticity in sensory cortices. We review the influence of neuromodulators and feedback connections in synaptic plasticity and suggest a specific framework in which these factors can interact to improve the functioning of the entire network.

Locomotion-dependent remapping of distributed cortical networks

The interactions between neocortical areas are fluid and state-dependent, but how individual neurons couple to cortex-wide network dynamics remains poorly understood. We correlated the spiking of neurons in primary visual (V1) and retrosplenial (RSP) cortex to activity across dorsal cortex, recorded simultaneously by widefield calcium imaging. Neurons were correlated with distinct and reproducible patterns of activity across the cortical surface; while some fired predominantly with their local area, others coupled to activity in distal areas. The extent of distal coupling was predicted by how strongly neurons correlated with the local network. Changes in brain state triggered by locomotion strengthened affiliations of V1 neurons with higher visual and motor areas, while strengthening distal affiliations of RSP neurons with sensory cortices. Thus, the diverse coupling of individual neurons to cortex-wide activity patterns is restructured by running in an area-specific manner, resulting in a shift in the mode of cortical processing during locomotion.

Complementary networks of cortical somatostatin interneurons enforce layer specific control

The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.

Visual physiology of the layer 4 cortical circuit in silico

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

How can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry by employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in response to a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 –in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.

Dual-Channel Photostimulation for Independent Excitation of Two Populations

Manipulation of defined neurons using excitatory opsins, including channelrhodopsin, enables studies of connectivity and the functional role of these circuit components in the brain. These techniques are vital in the neocortex, where diverse neurons are intermingled, and stimulation of specific cell types is difficult without the spatial, temporal, and genetic control available with optogenetic approaches. Channelrhodopsins are effective for mapping excitatory connectivity from one input type to its target. Attempts to use multiple opsins to simultaneously map multiple inputs face the challenge of partially overlapping light spectra for different opsins. This protocol describes one strategy to independently stimulate two comingled inputs in the same brain area to assess convergence and interaction of pathways in neural circuits. This is highly relevant in the neocortex, where pyramidal neurons integrate excitatory inputs from multiple local cell types and long-range corticocortical and thalamocortical projections. © 2018 by John Wiley & Sons, Inc.

Thalamocortical Circuits and Functional Architecture

The thalamocortical pathway is the main route of communication between the eye and the cerebral cortex. During embryonic development, thalamocortical afferents travel to L4 and are sorted by receptive field position, eye of origin, and contrast polarity (i.e., preference for light or dark stimuli). In primates and carnivores, this sorting involves numerous afferents, most of which sample a limited region of the binocular field. Devoting abundant thalamocortical resources to process a limited visual field has a clear advantage: It allows many stimulus combinations to be sampled at each spatial location. Moreover, the sampling efficiency can be further enhanced by organizing the afferents in a cortical grid for eye input and contrast polarity. We argue that thalamocortical interactions within this eye-polarity grid can be used to represent multiple stimulus combinations found in nature and to build an accurate cortical map for multidimensional stimulus space.

Response to Targeted Cognitive Training Correlates with Change in Thalamic Volume in a Randomized Trial for Early Schizophrenia

Reduced thalamic volume is consistently observed in schizophrenia, and correlates with cognitive impairment. Targeted cognitive training (TCT) of auditory processing in schizophrenia drives improvements in cognition that are believed to result from functional neuroplasticity in prefrontal and auditory cortices. In this study, we sought to determine whether response to TCT is also associated with structural neuroplastic changes in thalamic volume in patients with early schizophrenia (ESZ). Additionally, we examined baseline clinical, cognitive, and neural characteristics predictive of a positive response to TCT. ESZ patients were randomly assigned to undergo either 40 h of TCT (N=22) or a computer games control condition (CG; N=22 s). Participants underwent MRI, clinical, and neurocognitive assessments before and after training (4-month interval). Freesurfer automated segmentation of the subcortical surface was carried out to measure thalamic volume at both time points. Left thalamic volume at baseline correlated with baseline global cognition, while a similar trend was observed in the right thalamus. The relationship between change in cognition and change in left thalamus volume differed between groups, with a significant positive correlation in the TCT group and a negative trend in the CG group. Lower baseline symptoms were related to improvements in cognition and left thalamic volume preservation following TCT. These findings suggest that the cognitive gains induced by TCT in ESZ are associated with structural neuroplasticity in the thalamus. Greater symptom severity at baseline reduced the likelihood of response to TCT both with respect to improved cognition and change in thalamic volume.

Studies of cortical connectivity using optical circuit mapping methods

An important consideration when probing the function of any neuron is to uncover the source of synaptic input onto the cell, its intrinsic physiology and efferent targets. Over the years, electrophysiological approaches have generated considerable insight into these properties in a variety of cortical neuronal subtypes and circuits. However, as researchers explore neuronal function in greater detail, they are increasingly turning to optical techniques to bridge the gap between local network interactions and behaviour. The application of optical methods has increased dramatically over the past decade, spurred on by the optogenetic revolution. In this review, we provide an account of recent innovations, providing researchers with a primer detailing circuit mapping strategies in the cerebral cortex. We will focus on technical aspects of performing neurotransmitter uncaging and channelrhodopsin-assisted circuit mapping, with the aim of identifying common pitfalls that can negatively influence the collection of reliable data.

Synaptic organization of visual space in primary visual cortex

How a sensory stimulus is processed and perceived depends on the surrounding sensory scene. In the visual cortex, contextual signals can be conveyed by an extensive network of intra- and inter-areal excitatory connections that link neurons representing stimulus features separated in visual space1–4. However, the connectional logic of visual contextual inputs remains unknown; it is not clear what information individual neurons receive from different parts of the visual field, nor how this input relates to the visual features a neuron encodes, defined by its spatial receptive field. We determined the organisation of excitatory synaptic inputs responding to different locations in the visual scene by mapping spatial receptive fields in dendritic spines of mouse visual cortex neurons using two-photon calcium imaging. We found that neurons received functionally diverse inputs from extended regions of visual space. Inputs representing similar visual features from the same location in visual space were more likely to cluster on neighbouring spines. Inputs from visual field regions beyond the postsynaptic neuron’s receptive field often synapsed on higher-order dendritic branches. These putative long-range inputs were more frequent and more likely to share the preference for oriented edges with the postsynaptic neuron when the input’s receptive field was spatially displaced along the axis of the postsynaptic neuron’s receptive field orientation. Therefore, the connectivity between neurons with displaced receptive fields obeys a specific rule, whereby they connect preferentially when their receptive fields are co-oriented and co-axially aligned. This organization of synaptic connectivity is ideally suited for amplification of elongated edges, which are enriched in the visual environment, and thus provides a potential substrate for contour integration and object grouping.

Linking structure and activity in nonlinear spiking networks

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks’ spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities—including those of different cell types—combine with connectivity to shape population activity and function.

Neuronal networks, like many biological systems, exhibit variable activity. This activity is shaped by both the underlying biology of the component neurons and the structure of their interactions. How can we combine knowledge of these two things—that is, models of individual neurons and of their interactions—to predict the statistics of single- and multi-neuron activity? Current approaches rely on linearizing neural activity around a stationary state. In the face of neural nonlinearities, however, these linear methods can fail to predict spiking statistics and even fail to correctly predict whether activity is stable or pathological. Here, we show how to calculate any spike train cumulant in a broad class of models, while systematically accounting for nonlinear effects. We then study a fundamental effect of nonlinear input-rate transfer–coupling between different orders of spiking statistic–and how this depends on single-neuron and network properties.

Neural Elements for Predictive Coding

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).