Biology subjects

Adesnik, H.

Publications and source records attributed to Adesnik, H..

At least 19 recordsLinked to original sources

bioRxiv2025

Feature-specific inhibitory connectivity augments the accuracy of cortical representations

To interpret complex sensory scenes, animals exploit statistical regularities to infer missing features and suppress redundant or ambiguous information. Cortical microcircuits might contribute to this cognitive goal by either completing or cancelling predictable activity, but it remains unknown whether, and how, a single circuit can implement these antagonistic computations. To address this central question, we used all-optical physiology to simulate sensory-evoked activity patterns in pyramidal cells (PCs) and somatostatin interneurons (SSTs) in the mouses primary visual cortex (V1). In the absence of external visual input, photostimulation of orientation-tuned PC ensembles drove either completion or cancelation of input-matching representations, depending on the number of photostimulated cells. This dual computational capacity arose from the co-existence of like-to-like excitatory interactions between PCs, and a newly discovered like-to-like SST-PC connectivity motif, in which SSTs are preferentially recruited by, and in turn suppress, similarly tuned PCs. Finally, we show that photoactivation of tuned SST ensembles during visual processing improved the discriminability of their preferred visual input by suppressing ambiguous activity. Thus, these complementary feature-specific connectivity motifs allow different strategies of contextual modulation to optimize inference by either completion (through PC-PC interactions) or cancelation (via PC-SST-PC loops) of predictable activity, depending on the structure of the input and the network state.

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bioRxiv2025

Feature-tuned synaptic inputs to somatostatin interneurons drive context-dependent processing

Mapping neural computation onto the functional microarchitecture of sensory circuits is essential for understanding how brain circuits transform input signals into coherent percepts. Many higher-order perceptual processes emerge in the cortex, yet relatively little is known about how specific connectivity motifs give rise to these computations. To address this challenge, we combined single cell and population-level physiological recordings and perturbation methods to map a context-dependent cortical computation onto the synaptic microarchitecture of the mouse primary visual cortex (V1). We demonstrate a precise, recurrent circuit between cortical pyramidal cells (PCs) and somatostatin (SST) inhibitory interneurons that mediates context-driven figure/ground modulation in V1. Through a like-to-like connectivity rule from PCs and SSTs, this circuit explains SSTs visual encoding properties and their resulting impact on contextual modulation in V1. These findings reveal key synaptic and circuit mechanisms that may underlie the earliest stages of scene segmentation in the visual cortex. Moreover, they raise the notion that feature-specific excitatory-inhibitory microcircuitry from PC to SSTs could be a general strategy that the cortex exploits to give rise to higher level computations.

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bioRxiv2025

Circuit-Based Understanding of Fine Spatial Scale Clustering of Orientation Tuning in Mouse Visual Cortex

In sensory cortex of brain it is often the case that neurons are spatially organized by their functional properties. A hallmark of primary visual cortex (V1) in higher mammals is a columnar functional map, where neurons tuned to different stimuli features are regularly organized in space. However, rodent visual cortex is at odds with this rule and lacks any spatially ordered functional architecture, and rather neuron feature preference is haphazardly organized in patterns termed salt-and-pepper. This sharp contrast in feature organization between the visual cortices of rodents and higher mammals has been a persistent mystery, fueled in part by abundant evidence of conserved cortical physiology between species. In this work, we applied a novel GCaMP indicator that are localized in the nucleus of neurons during two-photon imaging in mouse V1, which enabled us to overcome most spurious spatially correlated activity due to fluorescence contamination, and to ensure a faithful observation of functional organization over space. We found that the orientation tuning properties of distant neuron pairs (> 20 {micro}m) are irregularly and randomly organized, while neuron pairs that are extremely close (< 20 {micro}m) have strongly correlated orientation tuning, indicating a narrow yet strong spatially clustered organization of orientation preference, which we term micro-clustered organization. Exploring a circuit-based model of recurrently coupled mouse V1 we derived two key predictions for the micro-cluster: spatially localized recurrent connections over a comparable narrow spatial scale, and common relative spatial spreads of balanced excitation and inhibition in the network over broad spatial scales. These predictions are validated by both anatomical and optogenetic-based physiological circuit mapping experiments. Altogether, our work takes an important step in building a circuit-based theory of visual processing in mouse V1 over spatial scales that are often ignored, yet contain powerful synaptic interactions.

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bioRxiv2024

Network influence determines the impact of cortical ensembles on stimulus detection

Observation of neural firing patterns can constrain theories for the types of activity patterns that the brain uses to guide behavior. However, directly perturbing these patterns, ideally with great specificity, is required to causally test any particular theory. We combined two-photon imaging and cellular resolution optogenetic photo-stimulation to causally test how neural activity in the mouse visual cortex is read out to detect visual stimuli. Contrary to expectations, targeted activation of highly sensitive neural ensembles did not preferentially modify behavior compared to random ensembles, contradicting a longstanding hypothesis for how neural activity drives stimulus detection. Instead, the main predictor of a targeted neural ensembles impact on perception was its effect on network activity. This argues that downstream regions summate visual cortex activity without preferentially weighting more informative neurons to make sensory detection decisions. Comparing mouse behavioral performance to decoding models of neural activity implies that mice employ this simple, albeit suboptimal strategy to solve the task. This work challenges conventional notions for how sensory representations mediate perception and demonstrates that specific neural perturbations are critical for determining which features of neural activity drive behavior.

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bioRxiv2024

Balanced bidirectional optogenetics reveals the causal impact of cortical temporal dynamics in sensory perception

Whether the fast temporal dynamics of neural activity in brain circuits causally drive perception and cognition remains one of most longstanding unresolved questions in neuroscience1-6. While some theories posit a timing code in which dynamics on the millisecond timescale is central to brain function, others instead argue that mean firing rates over more extended periods (a rate code) carry most of the relevant information. Existing tools, such as optogenetics, can be used to alter temporal structure of neural dynamics7, but they invariably change mean firing rates, leaving the interpretation of such experiments ambiguous. Here we developed and validated a new approach based on balanced, bidirectional optogenetics that can alter temporal structure of neural dynamics while mitigating effects on mean activity. Using this new approach, we found that selectively altering cortical temporal dynamics substantially reduced performance in a sensory perceptual task. These results demonstrate that endogenous temporal dynamics in the cortex are causally required for perception and behavior. More generally, this new bidirectional optogenetic approach should be broadly useful for disentangling the causal impact of different timescales of neural dynamics on behavior.

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bioRxiv2023

maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data

A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.

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bioRxiv2023

Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization

Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recordings, has the potential to map monosynaptic connectivity at an unprecedented scale. However, optogenetic mapping of nearby connections poses a challenge, due to stimulation artifacts. When the postsynaptic cell expresses opsin, optical excitation can directly induce current in the patched cell, confounding connectivity measurements. This problem is most severe in nearby cell pairs, where synaptic connectivity is often strongest. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are consistent across repeated stimulations at similar laser power. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available at https://github.com/bantin/PhoRC.

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bioRxiv2023

Recurrent pattern completion drives the neocortical representation of sensory inference

When sensory information is incomplete or ambiguous, the brain relies on prior expectations to infer perceptual objects. Despite the centrality of this process to perception, the neural mechanism of sensory inference is not known. Illusory contours (ICs) are key tools to study sensory inference because they contain edges or objects that are implied only by their spatial context. Using cellular resolution, mesoscale two-photon calcium imaging and multi-Neuropixels recordings in the mouse visual cortex, we identified a sparse subset of neurons in the primary visual cortex (V1) and higher visual areas that respond emergently to ICs. We found that these highly selective IC-encoders mediate the neural representation of IC inference. Strikingly, selective activation of these neurons using two-photon holographic optogenetics was sufficient to recreate IC representation in the rest of the V1 network, in the absence of any visual stimulus. This outlines a model in which primary sensory cortex facilitates sensory inference by selectively strengthening input patterns that match prior expectations through local, recurrent circuitry. Our data thus suggest a clear computational purpose for recurrence in the generation of holistic percepts under sensory ambiguity. More generally, selective reinforcement of top-down predictions by pattern-completing recurrent circuits in lower sensory cortices may constitute a key step in sensory inference.

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bioRxiv2023

Bayesian target optimisation for high-precision holographic optogenetics

Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision.

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bioRxiv2023

Dissociating instructive from permissive roles of brain circuits with reversible neural activity manipulations

Recent work has demonstrated that both permanent lesions and acute inactivation experiments can lead to erroneous conclusions about the causal role of brain areas in specific behaviors, casting serious doubt on major avenues by which neuroscientists study the brain. To overcome this challenge, we developed a three-stage optogenetic approach which leverages the ability to precisely control the temporal period of regional inactivation with either brief or sustained illumination, enabling investigators to dissociate between putative permissive and instructive roles of brain areas in behavior. We applied this approach to the mouse primary visual cortex (V1) to probe whether V1 is permissive or instructive for the detection low contrast stimuli. Acute inactivation of V1 drastically suppressed performance, but during persistent inactivation, the animals contrast detection recovered to pre-silencing levels. This recovery was itself reversible, as returning the animals to intermittent V1 inactivation reinstated the behavioral deficit. These results argue that V1 is the default circuit mice use to detect visual stimuli, but in its absence, other regions can compensate for it. This novel, temporally controllable optogenetic perturbation paradigm should be useful in other brain circuits to assess whether they are instructive or permissive in a brain function or behavior.

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bioRxiv2023

Probing inter-areal computations with a cellular resolution two-photon holographic mesoscope

Brain computation depends on intricately connected yet highly distributed neural networks. Due to the absence of the requisite technologies, causally testing fundamental hypotheses on inter-areal processing has remained largely out-of-each. Here we developed the first two photon holographic mesoscope, a system capable of simultaneously reading and writing neural activity patterns with near single cell resolution across large regions of the brain. We demonstrate the precise photo-activation of spatial and temporal sequences of neurons in one or multiple brain areas while reading out the downstream effect in several other regions. We thus establish mesoscale two photon holographic optogenetics as a new platform for mapping functional connectivity and causal interactions across distributed cortical areas with high resolution.

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bioRxiv2022

The logic of recurrent circuits in the primary visual cortex

Recurrent cortical activity sculpts visual perception by refining, amplifying, or suppressing incoming visual signals. Despite the importance of recurrent circuits for cortical processing, the basic rules that govern how nearby cortical neurons influence each other remains enigmatic. We used two-photon holographic optogenetics to activate ensembles of neurons in Layer 2/3 of the primary visual cortex (V1) in the absence of external stimuli to isolate the impact of local recurrence from external inputs. We find that the spatial arrangement and the stimulus feature preference of both the stimulated and the target ensemble jointly determine the net effect of recurrent activity, defining the cortical activity patterns that drive competition versus facilitation in L2/3 circuits. Computational modeling suggests that a combination of highly local recurrent excitatory connectivity and selective convergence onto inhibitory neurons give rise to these principles of recurrent activity. Our data and modeling reveal that recurrent activity can have varied impact, but a logic emerges through an understanding of the precise spatial distribution and feature preference of the multicellular pattern of activity.

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bioRxiv2022

Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing

Discovering how neural computations are implemented in the cortex at the level of monosynaptic connectivity requires probing for the existence of synapses from possibly thousands of presynaptic candidate neurons. Two-photon optogenetics has been shown to be a promising technology for mapping such monosynaptic connections via serial stimulation of neurons with single-cell resolution. However, this approach is limited in its ability to uncover connectivity at large scales because stimulating neurons one-by-one requires prohibitively long experiments. Here we developed novel computational tools that, when combined, enable learning of monosynaptic connectivity from high-speed holographic neural ensemble stimulation. First, we developed a model-based compressed sensing algorithm that identifies connections from postsynaptic responses evoked by stimulation of many neurons at once, considerably increasing the rate at which the existence and strength of synapses are screened. We show that this model-based approach, explicitly incorporating known biophysics of optogenetic mapping experiments, is critical for accurately determining synaptic connectivity using compressed sensing. Second, we developed a deep learning method that isolates the postsynaptic response evoked by each stimulus, allowing stimulation to rapidly switch between ensembles without waiting for the postsynaptic response to return to baseline. We then validated our approach by performing large-scale connectivity mapping experiments in slices from layer 2/3 of mouse primary visual cortex. Together, our system increases the throughput of monosynaptic connectivity mapping by an order of magnitude over existing approaches, enabling the acquisition of connectivity maps at speeds needed to discover the synaptic circuitry implementing neural computations.

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bioRxiv2022

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation. Author SummaryCortical responses are often variable across identical experimental conditions, and this variability is shared between neurons (noise correlations). These noise correlations have been studied extensively to understand how they impact neural coding and what mechanisms determine their properties. Here we show how correlations relate to divisive normalization, a mathematical operation widely adopted to describe how the activity of a neuron is modulated by other neurons via divisive gain control. We introduce the first statistical model of this relation. We extensively validate the model and investigate parameter inference in synthetic data. We find that our model, when applied to data from mouse visual cortex, outperforms a popular model of noise correlations that does not include normalization, and it reveals diverse influences of normalization on correlations. Our work demonstrates a framework to measure the relation between noise correlations and the parameters of the normalization model, which could become an indispensable tool for quantitative investigations of noise correlations in the wide range of neural systems that exhibit normalization.

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bioRxiv2021

Cryo-EM structures of the channelrhodopsin ChRmine in lipid nanodiscs

Microbial channelrhodopsins are light-gated ion channels widely used for optogenetic manipulation of neuronal activity. ChRmine is a bacteriorhodopsin-like cation channelrhodopsin (BCCR) more closely related to ion pump rhodopsins than other channelrhodopsins. ChRmine displays unique properties favorable for optogenetics including high light sensitivity, a red-shifted activation spectrum, cation selectivity, and large photocurrents while its slow closing kinetics impede some applications. The structural basis for ChRmine function, or that of any other BCCR, is unknown. Here, we present cryo-EM structures of ChRmine in lipid nanodiscs in apo (opsin) and retinal-bound (rhodopsin) forms. The structures reveal an unprecedented trimeric architecture with a lipid filled central pore. Large electronegative cavities on either side of the membrane facilitate high conductance and selectivity for cations over protons. The retinal binding pocket structure suggests spectral and kinetic properties could be tuned with mutations and we identify ChRmine variants with two-fold increased and ten-fold decreased closing rates. These results provide insight into structural features that generate an ultra-potent microbial opsin and provide a platform for rational engineering of channelrhodopsins with improved properties that could expand the scale, depth, and precision of optogenetic manipulations.

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bioRxiv2021

Multifunctional Cre-dependent transgenic mice for high-precision all-optical interrogation of neural circuits

Determining which features of the neural code drive perception and behavior requires the ability to simultaneous read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two photon (2p) calcium imaging and targeted 2p photostimulation enable the activation of specific, functionally defined groups of neurons in behaving animals. However, these techniques do not yet have the ability to reveal how the specific distribution of firing rates across a relevant neural population mediates neural computation and behavior. The key technical obstacle is the inability to transform single-cell calcium signals into accurate estimates of firing rate changes and then write in these cell-specific firing rate changes to each individual neuron in a targeted population. To overcome this challenge, we made two advances: first we introduce a new genetic line of mice for robust Cre-dependent co-expression of a high-performance calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we developed a pipeline that enables the read-out and write-in of precise population vectors of neural activity across a targeted group of neurons. The combination of the new multifunctional transgenic line and the photostimulation paradigm offer a powerful and convenient platform for investigating the neural codes of computation and behavior. It may prove particularly useful for probing causal features of the geometry of neural representations where the ability to directly control the topology of population activity is essential.

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bioRxiv2021

VIP neurons desynchronize cortical assemblies

Gamma band synchronization can facilitate local and long-range communication in neural circuits. In the primary visual cortex (V1) the strength of synchronization on the local level is strongly tuned to the contrast, size and center/surround orientation of grating stimuli. On the global level, the synchronization of gamma oscillations across the retinotopic map crucially depends on matched stimulus properties in the corresponding locations in the visual field. Although these features of V1 gamma rhythms are likely to be crucial for how they might support cortico-cortical communication and visual perception, their neural basis remains largely unknown. We hypothesized VIP disinhibitory interneurons, which shape other tuning properties in V1 by inhibiting SST neurons, may be responsible for tuning local gamma band power and global gamma synchronization. To test these ideas, we combined multi-electrode electrophysiology, cell-type specific optogenetic suppression of VIP neurons and computational modeling. Contrary to expectations, our data show that on the local level, VIP activity has no role in tuning gamma power to stimulus properties; rather, it scales the gain of gamma oscillations linearly across stimulus space and across behavioral state. Conversely, on the global level, VIP neurons specifically suppress gamma synchronization (as measured by spectral coherence) between spatially separated cortical ensembles when they are processing non-matched stimulus features. A straightforward computational model of V1 shows that like-to-like connectivity across retinotopic space, and specific, but powerful VIPSST inhibition are sufficient to capture these seemingly opposed effects. These data demonstrate how VIP neurons differentially impact local and global properties of gamma rhythms depending on the global statistics of the retinal image. VIP neurons may thus construct temporal filters in the gamma band for spatially continuous image features, such as contours, to facilitate the downstream generation of coherent visual percepts.

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bioRxiv2021

High performance microbial opsins for spatially and temporally precise perturbations of large neuronal networks

Patterned optogenetic activation of defined neuronal populations in the intact brain can reveal fundamental aspects of the neural codes of perception and behavior. The biophysical properties of existing optogenetic tools, however, constrain the scale, speed, and fidelity of precise optical control. Here we use structure-guided mutagenesis to engineer opsins that exhibit very high potency while retaining fast kinetics. These new opsins enable large-scale, temporally and spatially precise control of population neural activity in vivo and in vitro. We benchmark these new opsins against existing optogenetics tools with whole-cell electrophysiology and all-optical physiology and provide a detailed biophysical characterization of a diverse family of microbial opsins under two-photon illumination. This establishes a toolkit and a resource for matching the optimal opsin to the goals and constraints of patterned optogenetics experiments. Finally, by combining these new opsins with optimized procedures for cell-specific holographic photo-stimulation, we demonstrate the simultaneous co-activation of several hundred spatially defined neurons with a single hologram, and nearly double that number by temporally interleaving holograms at fast rates. These newly engineered opsins substantially extend the capabilities of patterned illumination optogenetic paradigms for addressing neural circuits and behavior.

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