Competing Neural Ensembles in Motor Cortex Gate Goal-Directed Motor Output

The claustrum and synchronized brain states

Cortical activity is constantly fluctuating between distinct spatiotemporal activity patterns denoted by changes in brain state. States of cortical desynchronization arise during motor generation, increased attention, and high cognitive load. Synchronized brain states comprise spatially widespread, coordinated low-frequency neural activity during rest and sleep when disengaged from the external environment or 'offline'. The claustrum is a small subcortical structure with dense reciprocal connections with the cortex suggesting modulation by, or participation in, brain state regulation. Here, we highlight recent work suggesting that neural activity in the claustrum supports cognitive processes associated with synchronized brain states characterized by increased low-frequency network activity. As an example, we outline how claustrum activity could support episodic memory consolidation during sleep.

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

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

Stimulation of caudal inferior and middle frontal gyri disrupts planning during spoken interaction

Turn-taking is a central feature of conversation across languages and cultures.1,2,3,4 This key social behavior requires numerous sensorimotor and cognitive operations1,5,6 that can be organized into three general phases: comprehension of a partner's turn, preparation of a speaker's own turn, and execution of that turn. Using intracranial electrocorticography, we recently demonstrated that neural activity related to these phases is functionally distinct during turn-taking.7 In particular, networks active during the perceptual and articulatory stages of turn-taking consisted of structures known to be important for speech-related sensory and motor processing,8,9,10,11,12,13,14,15,16,17 while putative planning dynamics were most regularly observed in the caudal inferior frontal gyrus (cIFG) and the middle frontal gyrus (cMFG). To test if these structures are necessary for planning during spoken interaction, we used direct electrical stimulation (DES) to transiently perturb cortical function in neurosurgical patient-volunteers performing a question-answer task.7,18,19 We found that stimulating the cIFG and cMFG led to various response errors9,13,20,21 but not gross articulatory deficits, which instead resulted from DES of structures involved in motor control8,13,20,22 (e.g., the precentral gyrus). Furthermore, perturbation of the cIFG and cMFG delayed inter-speaker timing-consistent with slowed planning-while faster responses could result from stimulation of sites located in other areas. Taken together, our findings suggest that the cIFG and cMFG contain critical preparatory circuits that are relevant for interactive language use.

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

Synaptic wiring motifs in posterior parietal cortex support decision-making

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

A frontal cortical network is critical for language planning during spoken interaction

Many brain areas exhibit activity correlated with language planning, but the impact of these dynamics on spoken interaction remains unclear. Here we use direct electrical stimulation to transiently perturb cortical function in neurosurgical patient-volunteers performing a question-answer task. Stimulating structures involved in speech motor function evoked diverse articulatory deficits, while perturbations of caudal inferior and middle frontal gyri - which exhibit preparatory activity during conversational turn-taking - led to response errors. Perturbation of the same planning-related frontal regions slowed inter-speaker timing, while faster responses could result from stimulation of sites located in other areas. Taken together, these findings further indicate that caudal inferior and middle frontal gyri constitute a critical planning network essential for interactive language use.

Behavioral measurements of motor readiness in mice

Motor planning facilitates rapid and precise execution of volitional movements. Although motor planning has been classically studied in humans and monkeys, the mouse has become an increasingly popular model system to study neural mechanisms of motor planning. It remains yet untested whether mice and primates share common behavioral features of motor planning. We combined videography and a delayed response task paradigm in an autonomous behavioral system to measure motor planning in non-body-restrained mice. Motor planning resulted in both reaction time (RT) savings and increased movement accuracy, replicating classic effects in primates. We found that motor planning was reflected in task-relevant body features. Both the specific actions prepared and the degree of motor readiness could be read out online during motor planning. The online readout further revealed behavioral evidence of simultaneous preparation for multiple actions under uncertain conditions. These results validate the mouse as a model to study motor planning, demonstrate body feature movements as a powerful real-time readout of motor readiness, and offer behavioral evidence that motor planning can be a parallel process that permits rapid selection of multiple prepared actions.

Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.

Dorsolateral Striatum is a Bottleneck for Responding to Task-Relevant Stimuli in a Learned Whisker Detection Task in Mice

A learned sensory-motor behavior engages multiple brain regions, including the neocortex and the basal ganglia. How a target stimulus is detected by these regions and converted to a motor response remains poorly understood. Here, we performed electrophysiological recordings and pharmacological inactivations of whisker motor cortex and dorsolateral striatum to determine the representations within, and functions of, each region during performance in a selective whisker detection task in male and female mice. From the recording experiments, we observed robust, lateralized sensory responses in both structures. We also observed bilateral choice probability and preresponse activity in both structures, with these features emerging earlier in whisker motor cortex than dorsolateral striatum. These findings establish both whisker motor cortex and dorsolateral striatum as potential contributors to the sensory-to-motor (sensorimotor) transformation. We performed pharmacological inactivation studies to determine the necessity of these brain regions for this task. We found that suppressing the dorsolateral striatum severely disrupts responding to task-relevant stimuli, without disrupting the ability to respond, whereas suppressing whisker motor cortex resulted in more subtle changes in sensory detection and response criterion. Together these data support the dorsolateral striatum as an essential node in the sensorimotor transformation of this whisker detection task.SIGNIFICANCE STATEMENT Selecting an item in a grocery store, hailing a cab - these daily practices require us to transform sensory stimuli into motor responses. Many decades of previous research have studied goal-directed sensory-to-motor transformations within various brain structures, including the neocortex and the basal ganglia. Yet, our understanding of how these regions coordinate to perform sensory-to-motor transformations is limited because these brain structures are often studied by different researchers and through different behavioral tasks. Here, we record and perturb specific regions of the neocortex and the basal ganglia and compare their contributions during performance of a goal-directed somatosensory detection task. We find notable differences in the activities and functions of these regions, which suggests specific contributions to the sensory-to-motor transformation process.

Scale-free behavioral dynamics directly linked with scale-free cortical dynamics

Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure. Scale-free dynamics of both brain and behavior are important because each is associated with functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here, we show that scale-free dynamics of mouse behavior and neurons in the visual cortex are strongly related. Surprisingly, the scale-free neural activity is limited to specific subsets of neurons, and these scale-free subsets exhibit stochastic winner-take-all competition with other neural subsets. This observation is inconsistent with prevailing theories of scale-free dynamics in neural systems, which stem from the criticality hypothesis. We develop a computational model which incorporates known cell-type-specific circuit structure, explaining our findings with a new type of critical dynamics. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity.

Grounding Mental Representations in a Virtual Multi-Level Functional Framework

According to the associative theory of learning, reactive behaviors described by stimulus-response pairs result in the progressive wiring of a plastic brain. In contrast, flexible behaviors are supposedly driven by neurologically grounded mental states that involve computations on informational contents. These theories appear complementary, but are generally opposed to each other. The former is favored by neuro-scientists who explore the low-level biological processes supporting cognition, and the later by cognitive psychologists who look for higher-level structures. This situation can be clarified through an analysis that independently defines abstract neurological and informational functionalities, and then relate them through a virtual interface. This framework is validated through a modeling of the first stage of Piaget's cognitive development theory, whose reported end experiments demonstrate the emergence of mental representations of object displacements. The neural correlates grounding this emergence are given in the isomorphic format of an associative memory. As a child's exploration of the world progresses, his mental models will eventually include representations of space, time and causality. Only then epistemological concepts, such as beliefs, will give rise to higher level mental representations in a possibly richer propositional format. This raises the question of which additional neurological functionalities, if any, would be required in order to include these extensions into a comprehensive grounded model. We relay previously expressed views, which in summary hypothesize that the ability to learn has evolved from associative reflexes and memories, to suggest that the functionality of associative memories could well provide the sufficient means for grounding cognitive capacities.

Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition

The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.

A cerebellar origin of feedforward inhibition to the motor cortex in non-human primates

Voluntary movements are driven by coordinated activity across a large population of motor cortical neurons. Formation of this activity is controlled by local interactions and long-range inputs. How remote areas of the brain communicate with motor cortical neurons to effectively drive movement remains unclear. We address this question by studying the cerebellar-thalamocortical system. We find that thalamic input to the motor cortex triggers feedforward inhibition by contacting inhibitory cells via highly effective GluR2-lacking AMPA receptors and that, during task performance, the activity of parvalbumin (PV) and pyramidal cells exhibits relations comparable with movement parameters. We also find that the movement-related activity of PV interneurons precedes firing of pyramidal cells. This counterintuitive sequence of events, where inhibitory cells are recruited more strongly and before excitatory cells, may amplify the cortical effect of cerebellar signals in a way that exceeds their sheer synaptic efficacy by suppressing other inputs.

Neural activity in the mouse claustrum in a cross-modal sensory selection task

The claustrum, a subcortical nucleus forming extensive connections with the neocortex, has been implicated in sensory selection. Sensory-evoked claustrum activity is thought to modulate the neocortex's context-dependent response to sensory input. Recording from claustrum neurons while mice performed a tactile-visual sensory-selection task, we found that neurons in the anterior claustrum, including putative optotagged claustrocortical neurons projecting to the primary somatosensory cortex (S1), were rarely modulated by sensory input. Rather, they exhibited different types of direction-tuned motor responses. Furthermore, we found that claustrum neurons encoded upcoming movement during intertrial intervals and that pairs of claustrum neurons exhibiting synchronous firing were enriched for pairs preferring contralateral lick directions, suggesting that the activity of specific ensembles of similarly tuned claustrum neurons may modulate cortical activity. Chemogenetic inhibition of claustrocortical neurons decreased lick responses to inappropriate sensory stimuli. Altogether, our data indicate that the claustrum is integrated into higher-order premotor circuits recently implicated in decision-making.

Gated feedforward inhibition in the frontal cortex releases goal-directed action

Cortical circuits process both sensory and motor information in animals performing perceptual tasks. However, it is still unclear how sensory inputs are transformed into motor signals in the cortex to initiate goal-directed actions. In this study, we found that a visual-to-motor inhibitory circuit in the anterior cingulate cortex (ACC) triggers precise action in mice performing visual Go/No-go tasks. Three distinct features of ACC neurons-visual amplitudes of sensory neurons, suppression times of motor neurons and network activity from other neurons-predicted response times of the mice. Moreover, optogenetic activation of visual inputs in the ACC, which drives fast-spiking sensory neurons, prompted task-relevant actions in mice by suppressing ACC motor neurons and disinhibiting downstream striatal neurons. Notably, when mice terminated actions in response to stop signals, both motor neuron and network activity increased. Collectively, our data demonstrate that visual inputs to the frontal cortex trigger gated feedforward inhibition to initiate goal-directed actions.

State-Dependent Regulation of Cortical Processing Speed via Gain Modulation

To thrive in dynamic environments, animals must be capable of rapidly and flexibly adapting behavioral responses to a changing context and internal state. Examples of behavioral flexibility include faster stimulus responses when attentive and slower responses when distracted. Contextual or state-dependent modulations may occur early in the cortical hierarchy and may be implemented via top-down projections from corticocortical or neuromodulatory pathways. However, the computational mechanisms mediating the effects of such projections are not known. Here, we introduce a theoretical framework to classify the effects of cell type-specific top-down perturbations on the information processing speed of cortical circuits. Our theory demonstrates that perturbation effects on stimulus processing can be predicted by intrinsic gain modulation, which controls the timescale of the circuit dynamics. Our theory leads to counterintuitive effects, such as improved performance with increased input variance. We tested the model predictions using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where we found that a decrease in single-cell intrinsic gain during locomotion led to an acceleration of visual processing. Our results establish a novel theory of cell type-specific perturbations, applicable to top-down modulation as well as optogenetic and pharmacological manipulations. Our theory links connectivity, dynamics, and information processing via gain modulation.SIGNIFICANCE STATEMENT To thrive in dynamic environments, animals adapt their behavior to changing circumstances and different internal states. Examples of behavioral flexibility include faster responses to sensory stimuli when attentive and slower responses when distracted. Previous work suggested that contextual modulations may be implemented via top-down inputs to sensory cortex coming from higher brain areas or neuromodulatory pathways. Here, we introduce a theory explaining how the speed at which sensory cortex processes incoming information is adjusted by changes in these top-down projections, which control the timescale of neural activity. We tested our model predictions in freely running mice, revealing that locomotion accelerates visual processing. Our theory is applicable to internal modulation as well as optogenetic and pharmacological manipulations and links circuit connectivity, dynamics, and information processing.

Movement and Performance Explain Widespread Cortical Activity in a Visual Detection Task

Recent studies in mice reveal widespread cortical signals during task performance; however, the various task-related and task-independent processes underlying this activity are incompletely understood. Here, we recorded wide-field neural activity, as revealed by GCaMP6s, from dorsal cortex while simultaneously monitoring orofacial movements, walking, and arousal (pupil diameter) of head-fixed mice performing a Go/NoGo visual detection task and examined the ability of task performance and spontaneous or task-related movements to predict cortical activity. A linear model was able to explain a significant fraction (33-55% of variance) of widefield dorsal cortical activity, with the largest factors being movements (facial, walk, eye), response choice (hit, miss, false alarm), and arousal and indicate that a significant fraction of trial-to-trial variability arises from both spontaneous and task-related changes in state (e.g., movements, arousal). Importantly, secondary motor cortex was highly correlated with lick rate, critical for optimal task performance (high d'), and was the first region to significantly predict the lick response on target trials. These findings suggest that secondary motor cortex is critically involved in the decision and performance of learned movements and indicate that a significant fraction of trial-to-trial variation in cortical activity results from spontaneous and task-related movements and variations in behavioral/arousal state.

Sequence Learning Induces Selectivity to Multiple Task Parameters in Mouse Somatosensory Cortex

Sequential temporal ordering and patterning are key features of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. To explore how cortical neuronal activity underpins sequence discrimination, we developed a task in which mice distinguished between tactile "word" sequences constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Mice could respond to the earliest possible cues allowing discrimination, effectively solving the task as a "detection of change" problem, but enhanced their performance when responding later. Optogenetic inactivation showed that the somatosensory cortex was necessary for sequence discrimination. Two-photon imaging in layer 2/3 of the primary somatosensory "barrel" cortex (S1bf) revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal's action decision and the trial outcome (presence or absence of the predicted reward). Many neurons were activated preceding goal-directed licking, thus reflecting the animal's learned action in response to the target sequence; these neurons were found as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory response tuning: neurons responding to stimulus features were found in naive mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf, sequence learning results in neurons whose activity reflects the learned association between target sequence and licking rather than a refined representation of sensory features.

Cortical Localization of the Sensory-Motor Transformation in a Whisker Detection Task in Mice

Responding to a stimulus requires transforming an internal sensory representation into an internal motor representation. Where and how this sensory-motor transformation occurs is a matter of vigorous debate. Here, we trained male and female mice in a whisker detection go/no-go task in which they learned to respond (lick) following a transient whisker deflection. Using single unit recordings, we quantified sensory-related, motor-related, and choice-related activities in whisker primary somatosensory cortex (S1), whisker region of primary motor cortex (wMC), and anterior lateral motor cortex (ALM), three regions that have been proposed to be critical for the sensory-motor transformation in whisker detection. We observed strong sensory encoding in S1 and wMC, with enhanced encoding in wMC, and a lack of sensory encoding in ALM. We observed strong motor encoding in all three regions, yet largest in wMC and ALM. We observed the earliest choice probability in wMC, despite earliest sensory responses in S1. Based on the criteria of having both strong sensory and motor representations and early choice probability, we identify whisker motor cortex as the cortical region most directly related to the sensory-motor transformation. Our data support a model of sensory encoding originating in S1, sensory amplification and sensory-motor transformation occurring within wMC, and motor signals emerging in ALM after the sensory-motor transformation.

Modeling the Synchronization of Multimodal Perceptions as a Basis for the Emergence of Deterministic Behaviors

Living organisms have either innate or acquired mechanisms for reacting to percepts with an appropriate behavior e.g., by escaping from the source of a perception detected as threat, or conversely by approaching a target perceived as potential food. In the case of artifacts, such capabilities must be built in through either wired connections or software. The problem addressed here is to define a neural basis for such behaviors to be possibly learned by bio-inspired artifacts. Toward this end, a thought experiment involving an autonomous vehicle is first simulated as a random search. The stochastic decision tree that drives this behavior is then transformed into a plastic neuronal circuit. This leads the vehicle to adopt a deterministic behavior by learning and applying a causality rule just as a conscious human driver would do. From there, a principle of using synchronized multimodal perceptions in association with the Hebb principle of wiring together neuronal cells is induced. This overall framework is implemented as a virtual machine i.e., a concept widely used in software engineering. It is argued that such an interface situated at a meso-scale level between abstracted micro-circuits representing synaptic plasticity, on one hand, and that of the emergence of behaviors, on the other, allows for a strict delineation of successive levels of complexity. More specifically, isolating levels allows for simulating yet unknown processes of cognition independently of their underlying neurological grounding.

Inhibition of impulsive action by projection-defined prefrontal pyramidal neurons

The prefrontal cortex (PFC) plays a critical role in curbing impulsive behavior, but the underlying circuit mechanism remains incompletely understood. Here we show that a subset of dorsomedial PFC (dmPFC) layer 5 pyramidal neurons, which project to the subthalamic nucleus (STN) of the basal ganglia, play a key role in inhibiting impulsive responses in a go/no-go task. Projection-specific labeling and calcium imaging showed that the great majority of STN-projecting neurons were preferentially active in no-go trials when the mouse successfully withheld licking responses, but lateral hypothalamus (LH)-projecting neurons were more active in go trials with licking; visual cortex (V1)-projecting neurons showed only weak task-related activity. Optogenetic activation and inactivation of STN-projecting neurons reduced and increased inappropriate licking, respectively, partly through their direct innervation of the STN, but manipulating LH-projecting neurons had the opposite effects. These results identify a projection-defined subtype of PFC pyramidal neurons as key mediators of impulse control.

Functional Localization of an Attenuating Filter within Cortex for a Selective Detection Task in Mice

An essential feature of goal-directed behavior is the ability to selectively respond to the diverse stimuli in one's environment. However, the neural mechanisms that enable us to respond to target stimuli while ignoring distractor stimuli are poorly understood. To study this sensory selection process, we trained male and female mice in a selective detection task in which mice learn to respond to rapid stimuli in the target whisker field and ignore identical stimuli in the opposite, distractor whisker field. In expert mice, we used widefield Ca²⁺ imaging to analyze target-related and distractor-related neural responses throughout dorsal cortex. For target stimuli, we observed strong signal activation in primary somatosensory cortex (S1) and frontal cortices, including both the whisker region of primary motor cortex (wMC) and anterior lateral motor cortex (ALM). For distractor stimuli, we observed strong signal activation in S1, with minimal propagation to frontal cortex. Our data support only modest subcortical filtering, with robust, step-like attenuation in distractor processing between mono-synaptically coupled regions of S1 and wMC. This study establishes a highly robust model system for studying the neural mechanisms of sensory selection and places important constraints on its implementation.SIGNIFICANCE STATEMENT Responding to task-relevant stimuli while ignoring task-irrelevant stimuli is critical for goal-directed behavior. However, the neural mechanisms involved in this selection process are poorly understood. We trained mice in a detection task with both target and distractor stimuli. During expert performance, we measured neural activity throughout cortex using widefield imaging. We observed responses to target stimuli in multiple sensory and motor cortical regions. In contrast, responses to distractor stimuli were abruptly suppressed beyond sensory cortex. Our findings localize the sites of attenuation when successfully ignoring a distractor stimulus and provide essential foundations for further revealing the neural mechanism of sensory selection and distractor suppression.

Neuromodulation of Brain State and Behavior

Neural activity and behavior are both notoriously variable, with responses differing widely between repeated presentation of identical stimuli or trials. Recent results in humans and animals reveal that these variations are not random in their nature, but may in fact be due in large part to rapid shifts in neural, cognitive, and behavioral states. Here we review recent advances in the understanding of rapid variations in the waking state, how variations are generated, and how they modulate neural and behavioral responses in both mice and humans. We propose that the brain has an identifiable set of states through which it wanders continuously in a nonrandom fashion, owing to the activity of both ascending modulatory and fast-acting corticocortical and subcortical-cortical neural pathways. These state variations provide the backdrop upon which the brain operates, and understanding them is critical to making progress in revealing the neural mechanisms underlying cognition and behavior.

Cortex-wide Changes in Extracellular Potassium Ions Parallel Brain State Transitions in Awake Behaving Mice

Brain state fluctuations modulate sensory processing, but the factors governing state-dependent neural activity remain unclear. Here, we tracked the dynamics of cortical extracellular K+ concentrations ([K+]o) during awake state transitions and manipulated [K+]o in slices, during visual processing, and during skilled motor execution. When mice transitioned from quiescence to locomotion, [K+]o increased by 0.6-1.0 mM in all cortical areas analyzed, and this preceded locomotion by 1 s. Emulating the state-dependent [K+]o increase in cortical slices caused neuronal depolarization and enhanced input-output transformation. In vivo, locomotion increased the gain of visually evoked responses in layer 2/3 of visual cortex; this effect was recreated by imposing a [K+]o increase. Elevating [K+]o in the motor cortex increased movement-induced neuronal spiking in layer 5 and improved motor performance. Thus, [K+]o increases in a cortex-wide state-dependent manner, and this [K+]o increase affects both sensory and motor processing through the dynamic modulation of neural activity.

Strong neuron-to-body coupling implies weak neuron-to-neuron coupling in motor cortex

Cortical neurons can be strongly or weakly coupled to the network in which they are embedded, firing in sync with the majority or firing independently. Both these scenarios have potential computational advantages in motor cortex. Commands to the body might be more robustly conveyed by a strongly coupled population, whereas a motor code with greater information capacity could be implemented by neurons that fire more independently. Which of these scenarios prevails? Here we measure neuron-to-body coupling and neuron-to-population coupling for neurons in motor cortex of freely moving rats. We find that neurons with high and low population coupling coexist, and that population coupling was tunable by manipulating inhibitory signaling. Importantly, neurons with different population coupling tend to serve different functional roles. Those with strong population coupling are not involved with body movement. In contrast, neurons with high neuron-to-body coupling are weakly coupled to other neurons in the cortical population.

Some cortical neurons fire together like a synchronized chorus, while others fire independently like soloists. Here, the authors show that soloist neurons in motor cortex tend to control body movements, while the choristers do not, and that soloists can become choristers by increasing inhibition.

Symbolic Modeling of Asynchronous Neural Dynamics Reveals Potential Synchronous Roots for the Emergence of Awareness

A new computational framework implementing asynchronous neural dynamics is used to address the duality between synchronous vs. asynchronous processes, and their possible relation to conscious vs. unconscious behaviors. Extending previous results on modeling the first three levels of animal awareness, this formalism is used here to produce the execution traces of parallel threads that implement these models. Running simulations demonstrate how sensory stimuli associated with a population of excitatory neurons inhibit in turn other neural assemblies i.e., a kind of neuronal asynchronous wiring/unwiring process that is reflected in the progressive trimming of execution traces. Whereas, reactive behaviors relying on configural learning produce vanishing traces, the learning of a rule and its later application produce persistent traces revealing potential synchronous roots of animal awareness. In contrast, to previous formalisms that use analytical and/or statistical methods to search for patterns existing in a brain, this new framework proposes a tool for studying the emergence of brain structures that might be associated with higher level cognitive capabilities.

More than Just a "Motor": Recent Surprises from the Frontal Cortex

Motor and premotor cortices are crucial for the control of movements. However, we still know little about how these areas contribute to higher-order motor control, such as deciding which movements to make and when to make them. Here we focus on rodent studies and review recent findings, which suggest that-in addition to motor control-neurons in motor cortices play a role in sensory integration, behavioral strategizing, working memory, and decision-making. We suggest that these seemingly disparate functions may subserve an evolutionarily conserved role in sensorimotor cognition and that further study of rodent motor cortices could make a major contribution to our understanding of the evolution and function of the mammalian frontal cortex.

Neural Representation of Motor Output, Context and Behavioral Adaptation in Rat Medial Prefrontal Cortex During Learned Behavior

Selecting behavioral outputs in a dynamic environment is the outcome of integrating multiple information streams and weighing possible action outcomes with their value. Integration depends on the medial prefrontal cortex (mPFC), but how mPFC neurons encode information necessary for appropriate behavioral adaptation is poorly understood. To identify spiking patterns of mPFC during learned behavior, we extracellularly recorded neuronal action potential firing in the mPFC of rats performing a whisker-based "Go"/"No-go" object localization task. First, we identify three functional groups of neurons, which show different degrees of spiking modulation during task performance. One group increased spiking activity during correct "Go" behavior (positively modulated), the second group decreased spiking (negatively modulated) and one group did not change spiking. Second, the relative change in spiking was context-dependent and largest when motor output had contextual value. Third, the negatively modulated population spiked more when rats updated behavior following an error compared to trials without integration of error information. Finally, insufficient spiking in the positively modulated population predicted erroneous behavior under dynamic "No-go" conditions. Thus, mPFC neuronal populations with opposite spike modulation characteristics differentially encode context and behavioral updating and enable flexible integration of error corrections in future actions.

A procedure to increase the power of Granger-causal analysis through temporal smoothing

BACKGROUND

How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics.

NEW METHOD

We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model.

RESULTS

We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy.

COMPARISON WITH EXISTING METHOD

The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies.

CONCLUSIONS

The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.

Overproduction of Neurons Is Correlated with Enhanced Cortical Ensembles and Increased Perceptual Discrimination

Brains vary greatly in neuronal number and density, even across individuals within the same species, yet it remains unclear whether such variation leads to differences in brain function or behavior. By imaging cortical activity of a mouse model in which neuronal production is moderately enhanced in utero, we find that animals with more cortical neurons also develop enhanced functional correlations and more distinct neuronal ensembles in primary visual cortex. These mice also have sharper orientation discrimination in their visual behavior. These results unveil a correlation between neuronal ensembles and behavior and suggest that neuronal number is linked to functional modularity and perceptual discrimination of visual cortex. By experimentally linking differences in neuronal number and behavior, our findings could help explain how evolutionary and developmental variability of individual and species brain size may lead to perceptual and cognitive differences.

Parvalbumin-Expressing GABAergic Neurons in Primary Motor Cortex Signal Reaching

The control of targeted reaching is thought to be shaped by distinct subtypes of local GABAergic inhibitory neurons in primary forelimb motor cortex (M1). However, little is known about their action potential firing dynamics during reaching. To address this, we recorded the activity of parvalbumin-expressing (PV+) GABAergic neurons identified from a larger population of fast-spiking units and putative excitatory regular-spiking units in layer 5 of the mouse forelimb M1 during an M1-dependent, sensory-triggered reaching task. PV+ neurons showed short latency responses to the acoustic cue and vibrotactile trigger stimulus and an increase in firing at reaching onset that scaled with the amplitude of reaching. Unexpectedly, PV+ neurons fired before regular-spiking units at reach onset and showed high overall firing rates during both sensory-triggered and spontaneous reaches. Our data suggest that increasing M1 PV+ neuron firing rates may play a role in the initiation of voluntary reaching.

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Extracellular recordings from layer 5 M1 PV+ neurons during sensory-triggered reaching

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Task-related sensory- and motor-evoked responses in forelimb M1 neurons

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PV+ neuron firing rates are positively correlated with reaching amplitude

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PV+ neurons are activated before regular-spiking units at reaching onset

Neuronal Assemblies Evidence Distributed Interactions within a Tactile Discrimination Task in Rats

Accumulating evidence suggests that neural interactions are distributed and relate to animal behavior, but many open questions remain. The neural assembly hypothesis, formulated by Hebb, states that synchronously active single neurons may transiently organize into functional neural circuits-neuronal assemblies (NAs)-and that would constitute the fundamental unit of information processing in the brain. However, the formation, vanishing, and temporal evolution of NAs are not fully understood. In particular, characterizing NAs in multiple brain regions over the course of behavioral tasks is relevant to assess the highly distributed nature of brain processing. In the context of NA characterization, active tactile discrimination tasks with rats are elucidative because they engage several cortical areas in the processing of information that are otherwise masked in passive or anesthetized scenarios. In this work, we investigate the dynamic formation of NAs within and among four different cortical regions in long-range fronto-parieto-occipital networks (primary somatosensory, primary visual, prefrontal, and posterior parietal cortices), simultaneously recorded from seven rats engaged in an active tactile discrimination task. Our results first confirm that task-related neuronal firing rate dynamics in all four regions is significantly modulated. Notably, a support vector machine decoder reveals that neural populations contain more information about the tactile stimulus than the majority of single neurons alone. Then, over the course of the task, we identify the emergence and vanishing of NAs whose participating neurons are shown to contain more information about animal behavior than randomly chosen neurons. Taken together, our results further support the role of multiple and distributed neurons as the functional unit of information processing in the brain (NA hypothesis) and their link to active animal behavior.

Specific excitatory connectivity for feature integration in mouse primary visual cortex

Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a 'like-to-like' scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a 'feature binding' scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity. Unlike under the like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1.

Motor cortex - to act or not to act?

The motor cortex is a large frontal structure in the cerebral cortex of eutherian mammals. A vast array of evidence implicates the motor cortex in the volitional control of motor output, but how does the motor cortex exert this 'control'? Historically, ideas regarding motor cortex function have been shaped by the discovery of cortical 'motor maps' - that is, ordered representations of stimulation-evoked movements in anaesthetized animals. Volitional control, however, entails the initiation of movements and the ability to suppress undesired movements. In this article, we highlight classic and recent findings that emphasize that motor cortex neurons have a role in both processes.

Whole-Cell Recording of Neuronal Membrane Potential during Behavior

Neuronal membrane potential is of fundamental importance for the mechanistic understanding of brain function. This review discusses progress in whole-cell patch-clamp recordings for low-noise measurement of neuronal membrane potential in awake behaving animals. Whole-cell recordings can be combined with two-photon microscopy to target fluorescently labeled neurons, revealing cell-type-specific membrane potential dynamics of retrogradely or genetically labeled neurons. Dual whole-cell recordings reveal behavioral modulation of membrane potential synchrony and properties of synaptic transmission in vivo. Optogenetic manipulations are also readily integrated with whole-cell recordings, providing detailed information about the effect of specific perturbations on the membrane potential of diverse types of neurons. Exciting developments for future behavioral experiments include dendritic whole-cell recordings and imaging, and use of the whole-cell recording pipette for single-cell delivery of drugs and DNA, as well as RNA expression profiling. Whole-cell recordings therefore offer unique opportunities for investigating the neuronal circuits and synaptic mechanisms driving membrane potential dynamics during behavior.

Voltage-sensitive dye imaging of mouse neocortex during a whisker detection task

Sensorimotor processing occurs in a highly distributed manner in the mammalian neocortex. The spatiotemporal dynamics of electrical activity in the dorsal mouse neocortex can be imaged using voltage-sensitive dyes (VSDs) with near-millisecond temporal resolution and [Formula: see text] spatial resolution. Here, we trained mice to lick a water reward spout after a 1-ms deflection of the C2 whisker, and we imaged cortical dynamics during task execution with VSD RH1691. Responses to whisker deflection were highly dynamic and spatially highly distributed, exhibiting high variability from trial to trial in amplitude and spatiotemporal dynamics. We differentiated trials based on licking and whisking behavior. Hit trials, in which the mouse licked after the whisker stimulus, were accompanied by overall greater depolarization compared to miss trials, with the strongest hit versus miss differences being found in frontal cortex. Prestimulus whisking decreased behavioral performance by increasing the fraction of miss trials, and these miss trials had attenuated cortical sensorimotor responses. Our data suggest that the spatiotemporal dynamics of depolarization in mouse sensorimotor cortex evoked by a single brief whisker deflection are subject to important behavioral modulation during the execution of a simple, learned, goal-directed sensorimotor transformation.

Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions

Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions.

Thalamic amplification of cortical connectivity sustains attentional control

While interactions between the thalamus and cortex are critical for cognitive function^1–3, the exact contribution of the thalamus to these interactions is often unclear. Recent studies have shown diverse connectivity patterns across the thalamus ^4,5, but whether this diversity translates to thalamic functions beyond relaying information to or between cortical regions⁶ is unknown. Here, by investigating prefrontal cortical (PFC) representation of two rules used to guide attention, we find that the mediodorsal thalamus (MD) sustains these representations without relaying categorical information. Specifically, MD input amplifies local PFC connectivity, enabling rule-specific neural sequences to emerge and thereby maintain rule representations. Consistent with this notion, broadly enhancing PFC excitability diminishes rule specificity and behavioral performance, while enhancing MD excitability improves both. Overall, our results define a previously unknown principle in neuroscience; thalamic control of functional cortical connectivity. This function indicates that the thalamus plays much more central roles in cognition than previously thought.

Delay activity of specific prefrontal interneuron subtypes modulates memory-guided behavior

Memory-guided behavior requires maintenance of task-relevant information without sensory input, but the underlying circuit mechanism remains unclear. Calcium imaging in mice performing a delayed Go/No-Go task revealed robust delay activity in dorsomedial prefrontal cortex (dmPFC), with different pyramidal neurons signaling Go and No-Go action plans. Inhibiting pyramidal neurons by optogenetically activating somatostatin (SST)- or parvalbumin (PV)-positive interneurons, even transiently during an early delay period, impaired task performance primarily by increasing inappropriate Go responses. In contrast, activating vasoactive intestinal peptide (VIP)-positive interneurons enhanced the behavioral performance and neuronal representation of action plans. Furthermore, while the natural activity of SST and PV neurons was strongly biased toward Go trials, VIP neurons were similarly active in Go and No-Go trials. SST/VIP neuron activation also impaired/enhanced performance of a delayed two-alternative forced choice task. Thus, dmPFC is a crucial component of the short-term memory network, and activation of its VIP neurons improves memory retention.

Visually Evoked 3-5 Hz Membrane Potential Oscillations Reduce the Responsiveness of Visual Cortex Neurons in Awake Behaving Mice

Low-frequency membrane potential (V_m) oscillations were once thought to only occur in sleeping and anesthetized states. Recently, low-frequency V_m oscillations have been described in inactive awake animals, but it is unclear whether they shape sensory processing in neurons and whether they occur during active awake behavioral states. To answer these questions, we performed two-photon guided whole-cell V_m recordings from primary visual cortex layer 2/3 excitatory and inhibitory neurons in awake mice during passive visual stimulation and performance of visual and auditory discrimination tasks. We recorded stereotyped 3-5 Hz V_m oscillations where the V_m baseline hyperpolarized as the V_m underwent high amplitude rhythmic fluctuations lasting 1-2 s in duration. When 3-5 Hz V_m oscillations coincided with visual cues, excitatory neuron responses to preferred cues were significantly reduced. Despite this disruption to sensory processing, visual cues were critical for evoking 3-5 Hz V_m oscillations when animals performed discrimination tasks and passively viewed drifting grating stimuli. Using pupillometry and animal locomotive speed as indicators of arousal, we found that 3-5 Hz oscillations were not restricted to unaroused states and that they occurred equally in aroused and unaroused states. Therefore, low-frequency V_m oscillations play a role in shaping sensory processing in visual cortical neurons, even during active wakefulness and decision making.SIGNIFICANCE STATEMENT A neuron's membrane potential (V_m) strongly shapes how information is processed in sensory cortices of awake animals. Yet, very little is known about how low-frequency V_m oscillations influence sensory processing and whether they occur in aroused awake animals. By performing two-photon guided whole-cell recordings from layer 2/3 excitatory and inhibitory neurons in the visual cortex of awake behaving animals, we found visually evoked stereotyped 3-5 Hz V_m oscillations that disrupt excitatory responsiveness to visual stimuli. Moreover, these oscillations occurred when animals were in high and low arousal states as measured by animal speed and pupillometry. These findings show, for the first time, that low-frequency V_m oscillations can significantly modulate sensory signal processing, even in awake active animals.

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”.

Movement Initiation Signals in Mouse Whisker Motor Cortex

Frontal cortex plays a central role in the control of voluntary movements, which are typically guided by sensory input. Here, we investigate the function of mouse whisker primary motor cortex (wM1), a frontal region defined by dense innervation from whisker primary somatosensory cortex (wS1). Optogenetic stimulation of wM1 evokes rhythmic whisker protraction (whisking), whereas optogenetic inactivation of wM1 suppresses initiation of whisking. Whole-cell membrane potential recordings and silicon probe recordings of action potentials reveal layer-specific neuronal activity in wM1 at movement initiation, and encoding of fast and slow parameters of movements during whisking. Interestingly, optogenetic inactivation of wS1 caused hyperpolarization and reduced firing in wM1, together with reduced whisking. Optogenetic stimulation of wS1 drove activity in wM1 with complex dynamics, as well as evoking long-latency, wM1-dependent whisking. Our results advance understanding of a well-defined frontal region and point to an important role for sensory input in controlling motor cortex.

Inhibitory Control in the Cortico-Basal Ganglia-Thalamocortical Loop: Complex Regulation and Interplay with Memory and Decision Processes

We developed a circuit model of spiking neurons that includes multiple pathways in the basal ganglia (BG) and is endowed with feedback mechanisms at three levels: cortical microcircuit, corticothalamic loop, and cortico-BG-thalamocortical system. We focused on executive control in a stop signal task, which is known to depend on BG across species. The model reproduces a range of experimental observations and shows that the newly discovered feedback projection from external globus pallidus to striatum is crucial for inhibitory control. Moreover, stopping process is enhanced by the cortico-subcortical reverberatory dynamics underlying persistent activity, establishing interdependence between working memory and inhibitory control. Surprisingly, the stop signal reaction time (SSRT) can be adjusted by weights of certain connections but is insensitive to other connections in this complex circuit, suggesting novel circuit-based intervention for inhibitory control deficits associated with mental illness. Our model provides a unified framework for inhibitory control, decision making, and working memory.

Vibrissa motor cortex activity suppresses contralateral whisking behavior

Anatomical, stimulation and lesion data implicate vibrissa motor cortex in whisker motor control. Work on motor cortex focused on movement generation, but correlations between vibrissa motor cortex activity and whisking are weak. The exact role of vibrissa motor cortex remains unknown. We recorded vibrissa motor cortex neurons during various forms of vibrissal touch, which were invariably associated with whisker protraction and movement. Free whisking, object palpation and social touch all resulted in decreased cortical activity. To understand this activity decrease, we performed juxtacellular recordings, nanostimulation and in vivo whole-cell-recordings. Social facial touch resulted in decreased spiking activity, decreased cell excitability and membrane hyperpolarization. Activation of vibrissa motor cortex by intra-cortical microstimulation elicited whisker retraction, as if to abort vibrissal touch. Various vibrissa motor cortex inactivation protocols resulted in contralateral protraction and increased whisker movements. These data collectively point to movement suppression as a prime function of vibrissa motor cortex activity.

Inferring connectivity in networked dynamical systems: Challenges using Granger causality

Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.

Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model

Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input-output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation.