Biology subjects

Stanley, G. B.

Publications and source records attributed to Stanley, G. B..

At least 19 recordsLinked to original sources

bioRxiv2026

The engagement of layer 6 corticothalamic neurons in somatosensation

The corticothalamic neurons from layer 6 (L6CT) of primary sensory cortices provide extensive input to the thalamus in addition to projecting within the cortex, positioning them to play a key hypothesized role in shaping thalamocortical signaling. With the expansion of tools for precise functional identification of L6CT neurons for in-vivo electrophysiology, increasing evidence highlights L6CT neurons as dynamic gain modulators of thalamocortical sensory responses. However much of the work to date has been conducted under anesthesia and not in the context of awake and/or behaving animals. In this study, we show that L6CT neurons in the awake mouse convey information about ascending sensory inputs, the timing of which is fast enough to contribute to the sensory response of neurons throughout the thalamocortical circuit. Overall, L6CT neurons robustly encode the presence vs absence of a sensory stimulus but are relatively weak encoders of the fine details. Benchmarked against the activity of other excitatory cortical neuron, we also provide evidence for L6CT neurons as predictors of the behavioral outcome during a trained detection task. Taken together, the results in this study tie L6CT neurons to behavior in tactile detection, one of the most fundamental functional roles of the pathway.

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bioRxiv2024

Layer 6 corticothalamic neurons induce high gamma oscillations through cortico-cortical and cortico-thalamo-cortical pathways

Layer 6 corticothalamic (L6CT) neurons project to both cortex and thalamus, inducing multiple effects including the modulation of cortical and thalamic firing, and the emergence of high gamma oscillations in the cortical local field potential (LFP). We hypothesize that the high gamma oscillations driven by L6CT neuron activation are shaped by the dynamic engagement of intracortical and cortico-thalamo-cortical circuits. To test this, we optogenetically activated L6CT neurons in NTSR1-cre mice expressing channelrhodopsin-2 in L6CT neurons. Leveraging the vibrissal pathway in awake, head-fixed mice, we presented ramp-and-hold light at different intensities while recording neural activity in the primary somatosensory barrel cortex (S1) and the ventral posteromedial nucleus (VPm) of the thalamus using silicon probes. We found that the activation of S1 L6CT neurons induces high-frequency LFP oscillations in S1 that are modulated in frequency, but not in amplitude, across light intensities and over time. To identify which neuronal classes contribute to these oscillations, we examined the temporal evolution of firing rate in S1 and VPm. While most S1 neurons were steadily suppressed, VPm and S1 Layer 4 fast spiking (L4 FS) neurons evolved from being suppressed to facilitated within 500 ms, suggesting differential recruitment of the intracortical vs cortico-thalamo-cortical pathways. Finally, we found that LFP frequency selectively correlates with VPm firing rate. Taken together, our data suggest that L6CT neurons sculpt the frequency of S1 LFP high gamma oscillations through cortico-thalamo-cortical circuits, linking the recurrent interactions mediated by L6CT neurons to the high gamma oscillations observed across physiological and pathological conditions. Significance StatementLayer 6 corticothalamic (L6CT) neurons are strategically positioned to modulate the cortex and the thalamus allowing them to engage distinct, yet interlocked, circuits. Here we show that the activation of L6CT neurons in the mouse primary somatosensory cortex induces fast cortical oscillations through the coordinated engagement of cortico-thalamo-cortical and intracortical pathways. Our work reveals that these two L6CT-mediated pathways exert competing effects: while intracortical connections suppress cortical spiking, the activity of the cortico-thalamo-cortical loop rapidly evolves, facilitating cortical spiking. We demonstrate that the cortico-thalamo-cortical pathway operates on a faster timescale than the intracortical pathway and critically shapes cortical oscillation frequency. These findings reveal how the unique position of corticothalamic neurons allows them to flexibly and dynamically modulate the thalamocortical network.

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bioRxiv2024

Spike Reliability is Cell-Type Specific and Shapes Excitation and Inhibition in the Cortex

Neurons encode information in the highly variable spiking activity of neuronal populations, so that different repetitions of the same stimulus can generate action potentials that vary significantly in terms of the count and timing. How does spiking variability originate, and does it have a functional purpose? Leveraging the Allen Institute cell types dataset, we relate the spiking reliability of cortical neurons in-vitro during the intracellular injection of current resembling synaptic inputs to their morphologic, electrophysiologic, and transcriptomic classes. Our findings demonstrate that parvalbumin+ (PV) interneurons, a subclass of inhibitory neurons, show high reliability compared to other neuronal subclasses, particularly excitatory neurons. Through computational modeling, we predict that the high reliability of PV interneurons allows for strong and precise inhibition in downstream neurons, while the lower reliability of excitatory neurons allows for integrating multiple synaptic inputs leading to a spiking rate code. These findings illuminate how spiking variability in different neuronal classes affect information propagation in the brain, leading to precise inhibition and spiking rate codes.

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bioRxiv2024

Sensory-Behavioral Deficits in Parkinson's Disease: Insights from a 6-OHDA Mouse Model

Parkinsons disease (PD) is characterized by the degeneration of dopaminergic (DA) neurons in the substantia nigra pars compacta, leading to dopamine depletion in the striatum and the hallmark motor symptoms of the disease. However, non-motor deficits, particularly sensory symptoms, often precede motor manifestations, offering a potential early diagnostic window. The impact of non-motor deficits on sensation behavior and the underlying mechanisms remain poorly understood. In this study, we examined changes in tactile sensation within a parkinsonian state by employing a mouse model of PD induced by 6-hydroxydopamine (6-OHDA) to deplete striatal DA. Leveraging the conserved mouse whisker system as a model for tactile-sensory stimulation, we conducted psychophysical experiments to assess sensory-driven behavioral performance during a tactile detection task in both the healthy and PD-like state. Our findings reveal a range of deficits across subjects following 6-OHDA lesion, including DA loss, motor asymmetry, weight loss, and varying levels of altered tactile sensation behavior. Behavioral changes ranged from no impairments in minor cases to isolated sensory-behavioral deficits in moderate cases and severe motor dysfunction in advanced stages. These results underscore the complex relationship between DA imbalance and sensory-motor processing, emphasizing the need for precise and multifaceted behavioral measurements to accurately capture the diverse manifestations of PD. SIGNIFICANCE STATEMENTThis study explores sensory-motor aspects of Parkinsons disease using a 6-OHDA mouse model. Leveraging the mouse whisker system, we reveal diverse deficits in tactile sensation behavior due to dopamine depletion. Our findings emphasize the importance of sensory assessments in understanding the diverse spectrum of PD symptoms.

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bioRxiv2024

Multiple distinct timescales of rapid sensory adaptation in the thalamocortical circuit

Numerous studies have shown that neuronal representations in sensory pathways are far from static but are instead strongly shaped by the complex properties of the sensory inputs they receive. Adaptation dynamically shapes the neural signaling that underlies our perception of the world yet remains poorly understood. We investigated rapid adaptation across timescales from hundreds of milliseconds to seconds through simultaneous multi-electrode recordings from the ventro-posteromedial nucleus of the thalamus (VPm) and layer 4 of the primary somatosensory cortex (S1) in male and female anesthetized mice in response to controlled, persistent whisker stimulation. Observations in VPm and S1 reveal a degree of adaptation that progresses through the pathway. Signatures of two distinct timescales of rapid adaptation in the firing rates of both thalamic and cortical neuronal populations were revealed, also reflected in the synchrony of the thalamic population and in the thalamocortical synaptic efficacy that was measured in putatively monosynaptically connected thalamocortical pairs. Controlled optogenetic activation of VPm further demonstrated that the longer timescale adaptation observed in S1 is likely inherited from slow decreases in thalamic firing rate and synchrony. Despite the degraded sensory responses, adaptation resulted in a shift in coding strategy that favors theoretical discrimination over detection across the observed timescales of adaptation. Overall, although multiple mechanisms contribute to rapid adaptation at distinct timescales, they support a unifying framework on the role of adaptation in sensory processing. Significance StatementAlthough the perceptual effects of persistent sensory stimulation have been known for centuries, the rapid sensory adaptation of the underlying neural signaling to these persistent inputs are not well understood. Here, we present evidence for two distinct timescales of adaptation over several seconds across the thalamocortical circuit in mice. We identify both the overall level of neural activity and the corresponding population synchrony of the thalamic inputs to primary somatosensory cortex as key role players shaping the cortical adaptation.

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bioRxiv2023

Dynamic corticothalamic modulation of the somatosensory thalamocortical circuit during wakefulness

The feedback projections from cortical layer 6 (L6CT) to sensory thalamus have long been implicated in playing a primary role in gating sensory signaling but remain poorly understood. To causally elucidate the full range of effects of these projections, we targeted silicon probe recordings to the whisker thalamocortical circuit of awake mice selectively expressing Channelrhodopsin-2 in L6CT neurons. Through optogenetic manipulation of L6CT neurons, multi-site electrophysiological recordings, and modeling of L6CT circuitry, we establish L6CT neurons as dynamic modulators of ongoing spiking in the ventro-posterior-medial nucleus of thalamus (VPm), either suppressing or enhancing VPm spiking depending on L6CT neurons firing rate and synchrony. Differential effects across the cortical excitatory and inhibitory sub-populations point to an overall influence of L6CT feedback on cortical excitability that could have profound implications for regulating sensory signaling across a range of ethologically relevant conditions.

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bioRxiv2023

A Machine Learning Approach for Real-time Cortical State Estimation

ObjectiveCortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as "cortical state". Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. ApproachWe use unsupervised Gaussian Mixture Models (GMMs) to identify discrete, emergent clusters in spontaneous local field potential (LFP) signals in cortex. We then extend our approach to a temporally-informed Hidden semi-Markov Model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main resultsUnsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. SignificanceTo our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

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bioRxiv2022

Cortical networks relating to arousal are differentially coupled to neural activity and hemodynamics

Even in the absence of specific sensory input or a behavioral task, the brain produces structured patterns of activity. This organized activity is modulated by changes in arousal. Here, we use wide-field voltage imaging to establish how arousal relates to cortical network voltage and hemodynamic activity in spontaneously behaving head-fixed male and female mice expressing the voltage-sensitive fluorescent FRET sensor Butterfly 1.2. We find that global voltage and hemodynamic signals are both positively correlated with changes in arousal with a maximum correlation of 0.5 and 0.25 respectively at a time lag of 0 seconds. We next show that arousal influences distinct cortical regions for both voltage and hemodynamic signals. These include a broad positive correlation across most sensory-motor cortices extending posteriorly to the primary visual cortex observed in both signals. In contrast, activity in prefrontal cortex is positively correlated to changes in arousal for the voltage signal while it is a slight net negative correlation observed in the hemodynamic signal. Additionally, we show that coherence between voltage and hemodynamic signals relative to arousal is strongest for slow frequencies below 0.15 Hz and is near zero for frequencies greater than 1Hz. We finally show that coupling patterns are dependent on the behavioral state of the animal with correlations being driven by periods of increased orofacial movement. Our results indicate that while hemodynamic signals show strong relations to behavior and arousal, these relations are distinct from those observed by voltage activity. Significance StatementWe leverage wide-field voltage imaging to examine the relation between cortical changes in membrane potential dynamics and hemodynamics. These two signals are then examined with respect to changes in arousal, as measured by pupil diameter, in awake head fixed mice. Our results show similarities as well as important differences in the correlation of arousal with neuronal population activity dynamics and the hemodynamic signal. Further, the spatial activity correlation maps with arousal depended differentially on the behavioral state of the animal in a frequency dependent manner. Our results indicate that the modulation of brain networks by arousal is dynamically regulated, and only partly overlap between functional networks determined from hemodynamic or voltage activity.

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bioRxiv2022

Bridging scales from spiking activity to the local field potential through latent dynamics

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Here, we relate the latent dynamics of spiking activity of populations of neurons in the whisker area of primary somatosensory cortex of awake mice to classic measurements of cortical state in S1. We found that a hidden Markov model fit the population spiking data well with a relatively small number of states, and that putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas. Author SummaryBrain states have long been known to strongly shape sensory perception, decision making, cognition, and movement. Brain state during wakefulness changes constantly, classically assessed using changes in the spectral features of the local field potential (LFP) and behavioral measures. However, the connection between these classical measurements of brain state and the collective dynamics of populations of neurons is unclear. Here we fit a latent-variable model to population spiking activity, finding that latent variables inferred under the model are highly predictive of cortical state changes and that the latent dynamics are profoundly shaped by inhibitory cell activity. Our approach connects the activity patterns of ensembles of neurons to a classical measurement of brain state and opens new avenues for investigating brain state dynamics across diverse cortical areas.

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bioRxiv2021

Ipsilateral stimulus encoding in primary and secondary somatosensory cortex of awake mice

Lateralization is a hallmark of somatosensory processing in the mammalian brain. However, in addition to their contralateral representation, unilateral tactile stimuli also modulate neuronal activity in somatosensory cortices of the ipsilateral hemisphere. The cellular organization and functional role of these ipsilateral stimulus responses in awake somatosensory cortices, especially regarding stimulus coding, are unknown. Here, we targeted silicon probe recordings to the vibrissa region of primary (S1) and secondary (S2) somatosensory cortex of awake head-fixed mice of either sex while delivering ipsilateral and contralateral whisker stimuli. Ipsilateral stimuli drove larger and more reliable responses in S2 than in S1, and activated a larger fraction of stimulus-responsive neurons. Ipsilateral stimulus-responsive neurons were rare in layer 4 of S1, but were located in equal proportion across all layers in S2. Linear classifier analyses further revealed that decoding of the ipsilateral stimulus was more accurate in S2 than S1, while S1 decoded contralateral stimuli most accurately. These results reveal substantial encoding of ipsilateral stimuli in S1 and especially S2, consistent with the hypothesis that higher cortical areas may integrate tactile inputs across larger portions of space, spanning both sides of the body. S1ignificance StatementTactile information obtained by one side of the body is represented in the activity of neurons of the opposite brain hemisphere. However unilateral tactile stimulation also modulates neuronal activity in the other, or ipsilateral, brain hemisphere. This ipsilateral activity may play an important role in the representation and processing of tactile information, in particular when the sense of touch involves both sides of the body. Our work in the whisker system of awake mice reveals that neocortical ipsilateral activity, in particular that of deep layer excitatory neurons of secondary somatosensory cortex (S2), contains information about the presence and the velocity of unilateral tactile stimuli, which supports a key role for S2 in integrating tactile information across both body sides.

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bioRxiv2021

Thalamic state controls timing and synchronization of primary somatosensory cortical representations in the awake mouse

The thalamus controls transmission of sensory signals from periphery to cortex, ultimately shaping perception. Despite this significant role, dynamic thalamic gating and the consequences for downstream cortical sensory representations have not been well studied in the awake brain. We optogenetically modulated the ventro-posterior medial thalamus in the vibrissa pathway of the awake mouse, and measured spiking activity in the thalamus, and activity in primary somatosensory cortex (S1) using extracellular electrophysiology and genetically encoded voltage imaging. Thalamic hyperpolarization significantly enhanced thalamic sensory-evoked bursting, yet surprisingly the S1 cortical response was not amplified, but instead timing precision was significantly increased, spatial activation more focused, and there was an increased synchronization of cortical inhibitory neurons. A thalamocortical network model implicates the modulation of precise timing of feedforward thalamic population spiking, presenting a highly sensitive, timing-based gating of sensory signaling to cortex.

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bioRxiv2021

Adaptive Behavior and the Role of Primary Somatosensory Cortex

Behavioral experience and flexibility are crucial for survival in a constantly changing environment. Despite evolutionary pressures to develop adaptive behavioral strategies in a dynamically changing sensory landscape, the underlying neural correlates have not been well explored. Here, we use genetically encoded voltage imaging to measure signals in primary somatosensory cortex (S1) during sensory learning and behavioral adaptation in the mouse. In response to changing stimulus statistics, mice adopt a strategy that modifies their detection behavior in a context dependent manner as to maintain reward expectation. Surprisingly, neuronal activity in S1 shifts from simply representing stimulus properties to transducing signals necessary for adaptive behavior in an experience dependent manner. Our results suggest that neuronal signals in S1 are part of an adaptive framework that facilitates flexible behavior as individuals gain experience, which could be part of a general scheme that dynamically distributes the neural correlates of behavior during learning.

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bioRxiv2020

Inferring Monosynaptic Connectivity Across Brain Structures In-Vivo

Abstract/SummaryAs the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex-vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in-vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in-vivo extracellular single unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal-detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that result in masking of synaptic relationships. Taken together, we provide a framework for establishing connectivity in multi-site, multi-electrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures. New & NoteworthyDespite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior.

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bioRxiv2020

Rapid Cortical Adaptation and the Role of Thalamic Synchrony During Wakefulness

Rapid sensory adaptation is observed across all sensory systems, and strongly shapes sensory percepts in complex sensory environments. Yet despite its ubiquity and likely necessity for survival, the mechanistic basis is poorly understood. A wide range of primarily in-vitro and anesthetized studies have demonstrated the emergence of adaptation at the level of primary sensory cortex, with only modest signatures in earlier stages of processing. The nature of rapid adaptation and how it shapes sensory representations during wakefulness, and thus the potential role in perceptual adaptation, is underexplored, as are the mechanisms that underlie this phenomenon. To address these knowledge gaps, we recorded spiking activity in primary somatosensory cortex (S1) and the upstream ventral posteromedial (VPm) thalamic nucleus in the vibrissa pathway of awake male and female mice, and quantified responses to whisker stimuli delivered in isolation and embedded in an adapting sensory background. We found that cortical sensory responses were indeed adapted by persistent sensory stimulation; putative excitatory neurons were profoundly adapted, and inhibitory neurons only modestly so. Further optogenetic manipulation experiments and network modeling suggest this largely reflects adaptive changes in synchronous thalamic firing combined with robust engagement of feedforward inhibition, with little contribution from synaptic depression. Taken together, these results suggest that cortical adaptation in the regime explored here results from changes in the timing of thalamic input, and the way in which this differentially impacts cortical excitation and feedforward inhibition, pointing to a prominent role of thalamic gating in rapid adaptation of primary sensory cortex. Significance StatementRapid adaptation of sensory activity strongly shapes representations of sensory inputs across all sensory pathways over the timescale of seconds, and has profound effects on sensory perception. Despite its ubiquity and theoretical role in the efficient encoding of complex sensory environments, the mechanistic basis is poorly understood, particularly during wakefulness. In this study in the vibrissa pathway of awake mice, we show that cortical representations of sensory inputs are strongly shaped by rapid adaptation, and that this is mediated primarily by adaptive gating of the thalamic inputs to primary sensory cortex and the differential way in which these inputs engage cortical sub-populations of neurons.

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bioRxiv2020

State-space optimal feedback control of optogenetically driven neural activity

ObjectiveThe rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse. ApproachClosed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch Main resultsThis model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications SignificanceTo our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in injury or disease.

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bioRxiv2020

Compensation of physiological motion enables high-yield whole-cell recording in vivo

1.1.1Whole-cell patch-clamp recording in vivo is the gold-standard method for measuring subthreshold electrophysiology from single cells during behavioural tasks, sensory stimulations, and optogenetic manipulation. However, these recordings require a tight, gigaohm resistance, seal between a glass pipette electrodes aperture and a cells membrane. These seals are difficult to form, especially in vivo, in part because of a strong dependence on the distance between the pipette aperture and cell membrane. We elucidate and utilize this dependency to develop an autonomous method for placement and synchronization of pipettes tip aperture to the membrane of a nearby, moving neuron, which enables high-yield seal formation and subsequent recordings in the deep in the brain of the living mouse, in the thalamus. This synchronization procedure nearly doubles the reported gigaseal yield in the thalamus (>3 mm below the pial surface) from 26% (n=17/64) to 48% (n=32/66). Whole-cell recording yield improved from 10% (n = 9/88) to 24% (n=18/76) when motion compensation was used during the gigaseal formation. As an example of its application, we utilized this system to investigate the role of the sensory environment and ventral posterior medial region (VPM) projection synchrony on intracellular dynamics in the barrel cortex. This method results in substantially greater subcortical whole-cell recording yield than previously reported and thus makes pan-brain whole-cell electrophysiology practical in the living mouse brain.

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bioRxiv2018

State-aware detection of sensory stimuli in the cortex of the awake mouse

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states.\n\nAuthor summaryEstablishing the link between neural activity and behavior is a central goal of neuroscience. One context in which to examine this link is in a sensory detection task, in which an animal is trained to report the presence of a barely perceptible sensory stimulus. In such tasks, both sensory responses in the brain and behavioral responses are highly variable. A simple hypothesis, originating in signal detection theory, is that perceived inputs generate neural activity that cross some threshold for detection. According to this hypothesis, sensory response variability would predict behavioral variability, but previous studies have not born out this prediction. Further complicating the picture, sensory response variability is partially dependent on the ongoing state of cortical activity, and we wondered whether this could resolve the mismatch between response variability and behavioral variability. Here, we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs. This observer has higher overall accuracy than the standard ideal observer. Moreover, because of the adaptation, the observer breaks the direct link between neural and behavioral variability, which could resolve discrepancies arising in past studies. We suggest new experiments to test our theory.

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bioRxiv2018

Thalamic state influences timing and feature selectivity in the thalamocortical circuit

Sensory signals from the outside world are transduced at the periphery, passing through thalamus before reaching cortex, ultimately giving rise to the sensory representations that enable us to perceive the world. The thalamocortical circuit is particularly sensitive to the temporal precision of thalamic spiking due to highly convergent synaptic connectivity. Thalamic neurons can exhibit burst and tonic modes of firing that strongly influence timing within the thalamus. The impact of these changes in thalamic state on sensory encoding in the cortex, however, remains unclear. Here, we investigated the role of thalamic state on timing in the thalamocortical circuit of the vibrissa pathway in the anesthetized rat. We optogenetically hyperpolarized thalamus while recording single unit activity in both thalamus and cortex. Tonic spike triggered analysis revealed temporally precise thalamic spiking that was locked to weak white-noise sensory stimuli, while thalamic burst spiking was associated with a loss in stimulus-locked temporal precision. These thalamic state dependent changes propagated to cortex such that the cortical timing precision was diminished during the hyperpolarized (burst biased) thalamic state. While still sensory driven, the cortical neurons became significantly less precisely locked to the weak white-noise stimulus. The results here suggests a state dependent differential regulation of spike timing precision in the thalamus that could gate what signals are ultimately propagated to cortex.

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