Physiology and Impact of Horizontal Connections in Rat Neocortex

Network-neuron interactions underlying sensory responses of layer 5 pyramidal tract neurons in barrel cortex

Neurons in the cerebral cortex receive thousands of synaptic inputs per second from thousands of presynaptic neurons. How the dendritic location of inputs, their timing, strength, and presynaptic origin, in conjunction with complex dendritic physiology, impact the transformation of synaptic input into action potential (AP) output remains generally unknown for in vivo conditions. Here, we introduce a computational approach to reveal which properties of the input causally underlie AP output, and how this neuronal input-output computation is influenced by the morphology and biophysical properties of the dendrites. We demonstrate that this approach allows dissecting of how different input populations drive in vivo observed APs. For this purpose, we focus on fast and broadly tuned responses that pyramidal tract neurons in layer 5 (L5PTs) of the rat barrel cortex elicit upon passive single whisker deflections. By reducing a multi-scale model that we reported previously, we show that three features are sufficient to predict with high accuracy the sensory responses and receptive fields of L5PTs under these specific in vivo conditions: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on these three features, we derive an analytically tractable description of the input-output computation of L5PTs, which enabled us to dissect how synaptic input from thalamus and different cell types in barrel cortex contribute to these responses. We show that the input-output computation is preserved across L5PTs despite morphological and biophysical diversity of their dendrites. We found that trial-to-trial variability in L5PT responses, and cell-to-cell variability in their receptive fields, are sufficiently explained by variability in synaptic input from the network, whereas variability in biophysical and morphological properties have minor contributions. Our approach to derive analytically tractable models of input-output computations in L5PTs provides a roadmap to dissect network-neuron interactions underlying L5PT responses across different in vivo conditions and for other cell types.

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.

Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse

The development of novel techniques to record wide-field brain activity enables estimation of data-driven models from thousands of recording channels and hence across large regions of cortex. These in turn improve our understanding of the modulation of brain states and the richness of traveling waves dynamics. Here, we infer data-driven models from high-resolution in-vivo recordings of mouse brain obtained from wide-field calcium imaging. We then assimilate experimental and simulated data through the characterization of the spatio-temporal features of cortical waves in experimental recordings. Inference is built in two steps: an inner loop that optimizes a mean-field model by likelihood maximization, and an outer loop that optimizes a periodic neuro-modulation via direct comparison of observables that characterize cortical slow waves. The model reproduces most of the features of the non-stationary and non-linear dynamics present in the high-resolution in-vivo recordings of the mouse brain. The proposed approach offers new methods of characterizing and understanding cortical waves for experimental and computational neuroscientists.

Classical-Contextual Interactions in V1 May Rely on Dendritic Computations

A signature feature of the neocortex is the dense network of horizontal connections (HCs) through which pyramidal neurons (PNs) exchange "contextual" information. In primary visual cortex (V1), HCs are thought to facilitate boundary detection, a crucial operation for object recognition, but how HCs modulate PN responses to boundary cues within their classical receptive fields (CRF) remains unknown. We began by "asking" natural images, through a structured data collection and ground truth labeling process, what function a V1 cell should use to compute boundary probability from aligned edge cues within and outside its CRF. The "answer" was an asymmetric 2-D sigmoidal function, whose nonlinear form provides the first normative account for the "multiplicative" center-flanker interactions previously reported in V1 neurons (Kapadia et al., 1995, 2000; Polat et al., 1998). Using a detailed compartmental model, we then show that this boundary-detecting classical-contextual interaction function can be computed by NMDAR-dependent spatial synaptic interactions within PN dendrites - the site where classical and contextual inputs first converge in the cortex. In additional simulations, we show that local interneuron circuitry activated by HCs can powerfully leverage the nonlinear spatial computing capabilities of PN dendrites, providing the cortex with a highly flexible substrate for integration of classical and contextual information.

Global organization of neuronal activity only requires unstructured local connectivity

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet, this parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

Roles of Motor Cortex Neuron Classes in Reach-Related Modulation for Hemiparkinsonian Rats

Disruption of the function of the primary motor cortex (M1) is thought to play a critical role in motor dysfunction in Parkinson's disease (PD). Detailed information regarding the specific aspects of M1 circuits that become abnormal is lacking. We recorded single units and local field potentials (LFPs) of M1 neurons in unilateral 6-hydroxydopamine (6-OHDA) lesion rats and control rats to assess the impact of dopamine (DA) cell loss during rest and a forelimb reaching task. Our results indicated that M1 neurons can be classified into two groups (putative pyramidal neurons and putative interneurons) and that 6-OHDA could modify the activity of different M1 subpopulations to a large extent. Reduced activation of putative pyramidal neurons during inattentive rest and reaching was observed. In addition, 6-OHDA intoxication was associated with an increase in certain LFP frequencies, especially those in the beta range (broadly defined here as any frequency between 12 and 35 Hz), which become pathologically exaggerated throughout cortico-basal ganglia circuits after dopamine depletion. Furthermore, assessment of different spike-LFP coupling parameters revealed that the putative pyramidal neurons were particularly prone to being phase-locked to ongoing cortical oscillations at 12-35 Hz during reaching. Conversely, putative interneurons were neither hypoactive nor synchronized to ongoing cortical oscillations. These data collectively demonstrate a neuron type-selective alteration in the M1 in hemiparkinsonian rats. These alterations hamper the ability of the M1 to contribute to motor conduction and are likely some of the main contributors to motor impairments in PD.

Up and Down States During Slow Oscillations in Slow-Wave Sleep and Different Levels of Anesthesia

Slow oscillations are a pattern of synchronized network activity generated by the cerebral cortex. They consist of Up and Down states, which are periods of activity interspersed with periods of silence, respectively. However, even when this is a unique dynamic regime of transitions between Up and Down states, this pattern is not constant: there is a range of oscillatory frequencies (0.1-4 Hz), and the duration of Up vs. Down states during the cycles is variable. This opens many questions. Is there a constant relationship between the duration of Up and Down states? How much do they vary across conditions and oscillatory frequencies? Are there different sub regimes within the slow oscillations? To answer these questions, we aimed to explore a concrete aspect of slow oscillations, Up and Down state durations, across three conditions: deep anesthesia, light anesthesia, and slow-wave sleep (SWS), in the same chronically implanted rats. We found that light anesthesia and SWS have rather similar properties, occupying a small area of the Up and Down state duration space. Deeper levels of anesthesia occupy a larger region of this space, revealing that a large variety of Up and Down state durations can emerge within the slow oscillatory regime. In a network model, we investigated the network parameters that can explain the different points within our bifurcation diagram in which slow oscillations are expressed.

A network model of the barrel cortex combined with a differentiator detector reproduces features of the behavioral response to single-neuron stimulation

The stimulation of a single neuron in the rat somatosensory cortex can elicit a behavioral response. The probability of a behavioral response does not depend appreciably on the duration or intensity of a constant stimulation, whereas the response probability increases significantly upon injection of an irregular current. Biological mechanisms that can potentially suppress a constant input signal are present in the dynamics of both neurons and synapses and seem ideal candidates to explain these experimental findings. Here, we study a large network of integrate-and-fire neurons with several salient features of neuronal populations in the rat barrel cortex. The model includes cellular spike-frequency adaptation, experimentally constrained numbers and types of chemical synapses endowed with short-term plasticity, and gap junctions. Numerical simulations of this model indicate that cellular and synaptic adaptation mechanisms alone may not suffice to account for the experimental results if the local network activity is read out by an integrator. However, a circuit that approximates a differentiator can detect the single-cell stimulation with a reliability that barely depends on the length or intensity of the stimulus, but that increases when an irregular signal is used. This finding is in accordance with the experimental results obtained for the stimulation of a regularly-spiking excitatory cell.

It is widely assumed that only a large group of neurons can encode a stimulus or control behavior. This tenet of neuroscience has been challenged by experiments in which stimulating a single cortical neuron has had a measurable effect on an animal’s behavior. Recently, theoretical studies have explored how a single-neuron stimulation could be detected in a large recurrent network. However, these studies missed essential biological mechanisms of cortical networks and are unable to explain more recent experiments in the barrel cortex. Here, to describe the stimulated brain area, we propose and study a network model endowed with many important biological features of the barrel cortex. Importantly, we also investigate different readout mechanisms, i.e. ways in which the stimulation effects can propagate to other brain areas. We show that a readout network which tracks rapid variations in the local network activity is in agreement with the experiments. Our model demonstrates a possible mechanism for how the stimulation of a single neuron translates into a signal at the population level, which is taken as a proxy of the animal’s response. Our results illustrate the power of spiking neural networks to properly describe the effects of a single neuron’s activity.

Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception

The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a 'barrel' (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.

Facilitating the propagation of spiking activity in feedforward networks by including feedback

Transient oscillations in network activity upon sensory stimulation have been reported in different sensory areas of the brain. These evoked oscillations are the generic response of networks of excitatory and inhibitory neurons (EI-networks) to a transient external input. Recently, it has been shown that this resonance property of EI-networks can be exploited for communication in modular neuronal networks by enabling the transmission of sequences of synchronous spike volleys (’pulse packets’), despite the sparse and weak connectivity between the modules. The condition for successful transmission is that the pulse packet (PP) intervals match the period of the modules’ resonance frequency. Hence, the mechanism was termed communication through resonance (CTR). This mechanism has three severe constraints, though. First, it needs periodic trains of PPs, whereas single PPs fail to propagate. Second, the inter-PP interval needs to match the network resonance. Third, transmission is very slow, because in each module, the network resonance needs to build up over multiple oscillation cycles. Here, we show that, by adding appropriate feedback connections to the network, the CTR mechanism can be improved and the aforementioned constraints relaxed. Specifically, we show that adding feedback connections between two upstream modules, called the resonance pair, in an otherwise feedforward modular network can support successful propagation of a single PP throughout the entire network. The key condition for successful transmission is that the sum of the forward and backward delays in the resonance pair matches the resonance frequency of the network modules. The transmission is much faster, by more than a factor of two, than in the original CTR mechanism. Moreover, it distinctly lowers the threshold for successful communication by synchronous spiking in modular networks of weakly coupled networks. Thus, our results suggest a new functional role of bidirectional connectivity for the communication in cortical area networks.

The cortex is organized as a modular system, with the modules (cortical areas) communicating via weak long-range connections. It has been suggested that the intrinsic resonance properties of population activities in these areas might contribute to enabling successful communication. A module’s intrinsic resonance appears in the damped oscillatory response to an incoming spike volley, enabling successful communication during the peaks of the oscillation. Such communication can be exploited in feedforward networks, provided the participating networks have similar resonance frequencies. This, however, is not necessarily true for cortical networks. Moreover, the communication is slow, as it takes several oscillation cycles to build up the response in the downstream network. Also, only periodic trains of spikes volleys (and not single volleys) with matching intervals can propagate. Here, we present a novel mechanism that alleviates these shortcomings and enables propagation of synchronous spiking across weakly connected networks with not necessarily identical resonance frequencies. In this framework, an individual spike volley can propagate by local amplification through reverberation in a loop between two successive networks, connected by feedforward and feedback connections: the resonance pair. This overcomes the need for activity build-up in downstream networks, causing the volley to propagate distinctly faster and more reliably.

Slow Waves in Cortical Slices: How Spontaneous Activity is Shaped by Laminar Structure

Cortical slow oscillations (SO) of neural activity spontaneously emerge and propagate during deep sleep and anesthesia and are also expressed in isolated brain slices and cortical slabs. We lack full understanding of how SO integrate the different structural levels underlying local excitability of cell assemblies and their mutual interaction. Here, we focus on ongoing slow waves (SWs) in cortical slices reconstructed from a 16-electrode array designed to probe the neuronal activity at multiple spatial scales. In spite of the variable propagation patterns observed, we reproducibly found a smooth strip of loci leading the SW fronts, overlapping cortical layers 4 and 5, along which Up states were the longest and displayed the highest firing rate. Propagation modes were uncorrelated in time, signaling a memoryless generation of SWs. All these features could be modeled by a multimodular large-scale network of spiking neurons with a specific balance between local and intermodular connectivity. Modules work as relaxation oscillators with a weakly stable Down state and a peak of local excitability to model layers 4 and 5. These conditions allow for both optimal sensitivity to the network structure and richness of propagation modes, both of which are potential substrates for dynamic flexibility in more general contexts.

Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers

Pyramidal tract neurons (PTs) represent the major output cell type of the mammalian neocortex. Here, we report the origins of the PTs’ ability to respond to a broad range of stimuli with onset latencies that rival or even precede those of their intracortical input neurons. We find that neurons with extensive horizontally projecting axons cluster around the deep-layer terminal fields of primary thalamocortical axons. The strategic location of these corticocortical neurons results in high convergence of thalamocortical inputs, which drive reliable sensory-evoked responses that precede those in other excitatory cell types. The resultant fast and horizontal stream of excitation provides PTs throughout the cortical area with input that acts to amplify additional inputs from thalamocortical and other intracortical populations. The fast onsets and broadly tuned characteristics of PT responses hence reflect a gating mechanism in the deep layers, which assures that sensory-evoked input can be reliably transformed into cortical output.

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Simulations predict in vivo responses for major output cell type of the neocortex

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Simulations reveal strategy how to test the origins of cortical output empirically

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Manipulations confirm that deep-layer corticocortical neurons gate cortical output

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Gating of cortical output originates from deep-layer thalamocortical input stratum

From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks

Spatio-temporal sequences of neuronal activity are observed in many brain regions in a variety of tasks and are thought to form the basis of meaningful behavior. However, mechanisms by which a neuronal network can generate spatio-temporal activity sequences have remained obscure. Existing models are biologically untenable because they either require manual embedding of a feedforward network within a random network or supervised learning to train the connectivity of a network to generate sequences. Here, we propose a biologically plausible, generative rule to create spatio-temporal activity sequences in a network of spiking neurons with distance-dependent connectivity. We show that the emergence of spatio-temporal activity sequences requires: (1) individual neurons preferentially project a small fraction of their axons in a specific direction, and (2) the preferential projection direction of neighboring neurons is similar. Thus, an anisotropic but correlated connectivity of neuron groups suffices to generate spatio-temporal activity sequences in an otherwise random neuronal network model.

Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth

The spatial distribution of neurons and activity-dependent neurite outgrowth shape long-range interaction, recurrent local connectivity and the modularity in neuronal networks. We investigated how this mesoscale architecture develops by interaction of neurite outgrowth, cell migration and activity in cultured networks of rat cortical neurons and show that simple rules can explain variations of network modularity. In contrast to theoretical studies on activity-dependent outgrowth but consistent with predictions for modular networks, spontaneous activity and the rate of synchronized bursts increased with clustering, whereas peak firing rates in bursts increased in highly interconnected homogeneous networks. As Ca²⁺ influx increased exponentially with increasing network recruitment during bursts, its modulation was highly correlated to peak firing rates. During network maturation, long-term estimates of Ca²⁺ influx showed convergence, even for highly different mesoscale architectures, neurite extent, connectivity, modularity and average activity levels, indicating homeostatic regulation towards a common set-point of Ca²⁺ influx.

Assessing the Impact of Single-Cell Stimulation on Local Networks in Rat Barrel Cortex-A Feasibility Study

In contrast to the long-standing notion that the role of individual neurons in population activity is vanishingly small, recent studies have shown that electrical activation of only a single cortical neuron can have measurable effects on global brain state, movement, and perception. Although highly important for understanding how neuronal activity in cortex is orchestrated, the cellular and network mechanisms underlying this phenomenon are unresolved. Here, we first briefly review the current state of knowledge regarding the phenomenon of single-cell induced network modulation and discuss possible underpinnings. Secondly, we show proof of principle for an experimental approach to elucidate the mechanisms of single-cell induced changes in cortical activity. The setup allows simultaneous recordings of the spiking activity of multiple neurons across all layers of the cortex using a multi-electrode array, while manipulating the activity of one individual neuron in close proximity to the array. We demonstrate that single cells can be recorded and stimulated reliably for hundreds of trials, conferring high statistical power even for expectedly small effects of single-neuron spiking on network activity. Preliminary results suggest that single-cell stimulation on average decreases the firing rate of local network units. We expect that characterization of the spatiotemporal spread of single-cell evoked activity across layers and columns will yield novel insights into intracortical processing.

The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) Version 2.0: Modelling in vitro electrical stimulation of brain tissue

Neuronal circuits can be modelled in detail allowing us to predict the effects of stimulation on individual neurons. Electrical stimulation of neuronal circuits in vitro and in vivo excites a range of neurons within the tissue and measurements of neural activity, e.g the local field potential (LFP), are again an aggregate of a large pool of cells. The previous version of our Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) allowed for the simulation of the LFP generated by a patch of brain tissue. Here, we extend VERTEX to simulate the effect of electrical stimulation through a focal electric field. We observe both direct changes in neural activity and changes in synaptic plasticity. Testing our software in a model of a rat neocortical slice, we determine the currents contributing to the LFP, the effects of paired pulse stimulation to induce short term plasticity (STP), and the effect of theta burst stimulation (TBS) to induce long term potentiation (LTP).

Cortical Reorganization of Sensorimotor Systems and the Role of Intracortical Circuits After Spinal Cord Injury

The plasticity of sensorimotor systems in mammals underlies the capacity for motor learning as well as the ability to relearn following injury. Spinal cord injury, which both deprives afferent input and interrupts efferent output, results in a disruption of cortical somatotopy. While changes in corticospinal axons proximal to the lesion are proposed to support the reorganization of cortical motor maps after spinal cord injury, intracortical horizontal connections are also likely to be critical substrates for rehabilitation-mediated recovery. Intrinsic connections have been shown to dictate the reorganization of cortical maps that occurs in response to skilled motor learning as well as after peripheral injury. Cortical networks incorporate changes in motor and sensory circuits at subcortical or spinal levels to induce map remodeling in the neocortex. This review focuses on the reorganization of cortical networks observed after injury and posits a role of intracortical circuits in recovery.

Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick

Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.

Dual Cortical Plasticity After Spinal Cord Injury

During cortical development, plasticity reflects the dynamic equilibrium between increasing and decreasing functional connectivity subserved by synaptic sprouting and pruning. After adult cortical deafferentation, plasticity seems to be dominated by increased functional connectivity, leading to the classical expansive reorganization from the intact to the deafferented cortex. In contrast, here we show a striking "decrease" in the fast cortical responses to high-intensity forepaw stimulation 1-3 months after complete thoracic spinal cord transection, as evident in both local field potentials and intracellular in vivo recordings. Importantly, this decrease in fast cortical responses co-exists with an "increase" in cortical activation over slower post-stimulus timescales, as measured by an increased forepaw-to-hindpaw propagation of stimulus-triggered cortical up-states, as well as by the enhanced slow sustained depolarization evoked by high-frequency forepaw stimuli in the deafferented hindpaw cortex. This coincidence of diminished fast cortical responses and enhanced slow cortical activation offers a dual perspective of adult cortical plasticity after spinal cord injury.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

Brain encoding of saltatory velocity through a pulsed pneumotactile array in the lower face

Processing dynamic tactile inputs is a primary function of the somatosensory system. Spatial velocity encoding mechanisms by the nervous system are important for skilled movement production and may play a role in recovery of sensorimotor function following neurological insult. Little is known about tactile velocity encoding in mechanosensory trigeminal networks required for speech, suck, mastication, and facial gesture. High resolution functional magnetic resonance imaging (fMRI) was used to investigate the neural substrates of velocity encoding in the human orofacial somatosensory system during unilateral saltatory pneumotactile stimulation of perioral and buccal hairy skin in 20 neurotypical adults. A custom multichannel, scalable pneumotactile array consisting of 7 TAC-Cells was used to present 5 stimulus conditions: 5cm/s, 25cm/s, 65cm/s, ALL-ON synchronous activation, and ALL-OFF. The spatiotemporal organization of whole-brain blood oxygen level-dependent (BOLD) response was analyzed with general linear modeling (GLM) and fitted response estimates of percent signal change to compare activations associated with each velocity, and the main effect of velocity alone. Sequential saltatory inputs to the right lower face produced localized BOLD responses in 6 key regions of interest (ROI) including; contralateral precentral and postcentral gyri, and ipsilateral precentral, superior temporal (STG), supramarginal gyri (SMG), and cerebellum. The spatiotemporal organization of the evoked BOLD response was highly dependent on velocity, with the greatest amplitude of BOLD signal change recorded during the 5cm/s presentation in the contralateral hemisphere. Temporal analysis of BOLD response by velocity indicated rapid adaptation via a scalability of networks processing changing pneumotactile velocity cues.

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

Inhibitory Circuits in Cortical Layer 5

Inhibitory neurons play a fundamental role in cortical computation and behavior. Recent technological advances, such as two photon imaging, targeted in vivo recording, and molecular profiling, have improved our understanding of the function and diversity of cortical interneurons, but for technical reasons most work has been directed towards inhibitory neurons in the superficial cortical layers. Here we review current knowledge specifically on layer 5 (L5) inhibitory microcircuits, which play a critical role in controlling cortical output. We focus on recent work from the well-studied rodent barrel cortex, but also draw on evidence from studies in primary visual cortex and other cortical areas. The diversity of both deep inhibitory neurons and their pyramidal cell targets make this a challenging but essential area of study in cortical computation and sensory processing.

Role of input correlations in shaping the variability and noise correlations of evoked activity in the neocortex

Recent analysis of evoked activity recorded across different brain regions and tasks revealed a marked decrease in noise correlations and trial-by-trial variability. Given the importance of correlations and variability for information processing within the rate coding paradigm, several mechanisms have been proposed to explain the reduction in these quantities despite an increase in firing rates. These models suggest that anatomical clusters and/or tightly balanced excitation-inhibition can generate intrinsic network dynamics that may exhibit a reduction in noise correlations and trial-by-trial variability when perturbed by an external input. Such mechanisms based on the recurrent feedback crucially ignore the contribution of feedforward input to the statistics of the evoked activity. Therefore, we investigated how statistical properties of the feedforward input shape the statistics of the evoked activity. Specifically, we focused on the effect of input correlation structure on the noise correlations and trial-by-trial variability. We show that the ability of neurons to transfer the input firing rate, correlation, and variability to the output depends on the correlations within the presynaptic pool of a neuron, and that an input with even weak within-correlations can be sufficient to reduce noise correlations and trial-by-trial variability, without requiring any specific recurrent connectivity structure. In general, depending on the ongoing activity state, feedforward input could either increase or decrease noise correlation and trial-by-trial variability. Thus, we propose that evoked activity statistics are jointly determined by the feedforward and feedback inputs.

Spatial organization of excitatory synaptic inputs to layer 4 neurons in mouse primary auditory cortex

Layer 4 (L4) of primary auditory cortex (A1) receives a tonotopically organized projection from the medial geniculate nucleus of the thalamus. However, individual neurons in A1 respond to a wider range of sound frequencies than would be predicted by their thalamic input, which suggests the existence of cross-frequency intracortical networks. We used laser scanning photostimulation and uncaging of glutamate in brain slices of mouse A1 to characterize the spatial organization of intracortical inputs to L4 neurons. Slices were prepared to include the entire tonotopic extent of A1. We find that L4 neurons receive local vertically organized (columnar) excitation from layers 2 through 6 (L6) and horizontally organized excitation primarily from L4 and L6 neurons in regions centered ~300–500 μm caudal and/or rostral to the cell. Excitatory horizontal synaptic connections from layers 2 and 3 were sparse. The origins of horizontal projections from L4 and L6 correspond to regions in the tonotopic map that are approximately an octave away from the target cell location. Such spatially organized lateral connections may contribute to the detection and processing of auditory objects with specific spectral structures.