Biology subjects

Histed, M. H.

Publications and source records attributed to Histed, M. H..

12 recordsLinked to original sources

bioRxiv2024

Active filtering of sequences of neural activity by recurrent circuits of sensory cortex

Recurrent neural networks can generate dynamics, but in sensory cortex it has been unclear if any dynamic processing is supported by the dense recurrent excitatory-excitatory network. Here we show a new role for recurrent connections in mouse visual cortex: they support powerful dynamical computations, but by filtering sequences of input instead of generating sequences. Using two-photon optogenetics, we measure neural responses to natural images and play them back, finding responses are boosted when inputs are played back during the correct movie dynamic context-- when the preceding sequence corresponds to natural vision. This sequence selectivity depends on a network mechanism: earlier input patterns produce responses in other local neurons, which interact with later input patterns. We confirm this mechanism by designing sequences of inputs that are boosted or attenuated by the network. These data suggest recurrent cortical connections perform predictive processing, encoding the statistics of the natural world in input-output transformations.

neuroscience↗

bioRxiv2023

Single cell optogenetics reveals attenuation-by-suppression in visual cortical neurons

The relationship between neurons input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest nonlinearities emerge in cells input-output (activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons activity varies with sensory stimuli. We find responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons IO functions can filter sensory inputs -- not only do sensory stimuli change neurons spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged. Significance statementHow neurons transform their inputs into outputs is a fundamental building block of brain computation. Past studies have measured neurons input-output (IO) functions in vitro or in anesthetized states. Here, we measure neurons IO functions in the awake and intact brain, where ongoing network activity can influence neurons responses to input. Using state-of-the-art optogenetic methods to deliver precise inputs to neurons near the cell body, or soma, we discover neurons have a supralinear-to-linear IO function, contrary to previous findings of threshold-linear, strongly saturating, or power law IO functions. This supralinear-to-linear somatic IO function shape allows neurons to decrease their responses to, or filter, inputs while they are suppressed below their resting firing rates, a computation we term attenuation-by-suppression.

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bioRxiv2022

Bicistronic expression of a high-performance calcium indicator and opsin yields stable, robust cortical expression for holographic two-photon stimulation

Patterns of activity across many neurons are fundamental units of neural computation. Two-photon holographic photostimulation allows both delivering input to, and imaging responses from, patterns or populations of neurons. However, to make this method an easily-deployable tool, simple methods are needed to robustly and stably express opsins and indicators in the same cells. Here we describe a bicistronic adeno-associated virus (AAV) that in transfected cells expresses both the fast and bright calcium indicator GCaMP8s, and a soma-targeted (st) and two-photon-activatable opsin, ChrimsonR. With this method, in the visual cortex of mice, stChrimsonR stimulation with two-photon holography drives robust spiking in targeted cells, and neural responses to visual sensory stimuli and spontaneous activity are strong and easy to measure. stChrimsonR is a good choice of opsin when a balance is needed between stimulation-laser activatability and avoidance of imaging laser activation. This approach is a simple and robust way to prepare neurons in vivo for two-photon holography and imaging. Significance statementThe recent advent of holographic photostimulation methods in conjunction with standard two-photon calcium imaging promises unprecedented levels of control in manipulating and dissecting brain circuitry in vivo while reading out neural activity. These all-optical methods rely on a working synergy between optogenetic strategies to both measure calcium activity through genetically-encoded calcium indicators and modulate cell activity through light-activated opsins. Genetic strategies to achieve reliable and stable co-expression of opsin and indicator remain sparse and often challenging to execute. Here, we present a genetic tool to achieve robust co-expression of jGCaMP8s indicator and stChrimsonR opsin via a single injected virus to help facilitate experiments aiming to use holography to investigate the circuit principles underlying brain activity.

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bioRxiv2022

Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

Dense local, recurrent connections are a major feature of cortical circuits, yet how they affect neurons responses is unclear, with some studies reporting weak recurrent effects, some amplification, and others showing instead local suppression. Here, we show that optogenetic input to mouse V1 excitatory neurons generates salt-and-pepper patterns of both excitation and suppression. Responses in individual neurons are not strongly predicted by that neurons direct input. A balanced-state network model reconciles a set of diverse observations: the observed dynamics, suppressed responses, decoupling of input and output, and long tail of excited responses. The model shows recurrent excitatory-excitatory connections are strong and also variable across neurons. Together, these results demonstrate that excitatory recurrent connections can have major effects on cortical computations, by shaping and changing neurons responses to input.

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bioRxiv2022

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons, yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low/high rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

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bioRxiv2022

Amplified cortical neural responses as animals learn to use novel activity patterns

Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task, and therefore these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.

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bioRxiv2020

Emergence of irregular activity in networks of strongly coupled conductance-based neurons

Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e. if the mean number of synapses per neuron K is large and synaptic efficacy is of order [Formula]. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of order 1/log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.

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bioRxiv2020

Performance in even a simple perceptual task depends on mouse secondary visual areas

Primary visual cortex (V1) in the mouse projects to numerous brain areas, including several secondary visual areas, frontal cortex, and basal ganglia. While it has been demonstrated that optogenetic silencing of V1 strongly impairs visually-guided behavior, it is not known which downstream areas are required for visual behaviors. Here we trained mice to perform a contrast-increment change detection task, for which substantial stimulus information is present in V1. Optogenetic silencing of visual responses in secondary visual areas revealed that their activity is required for even this simple visual task. In vivo electrophysiology showed that, although inhibiting secondary visual areas could produce some feedback effects in V1, the principal effect was profound suppression at the location of the optogenetic light. The results show that pathways through secondary visual areas are necessary for even simple visual behaviors.

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bioRxiv2019

Response nonlinearities in networks of spiking neurons

Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities contradict the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the observed diversity of non-linearities observed in cortical networks. AUTHOR SUMMARYModels of cortical networks are often studied in the strong coupling limit, where the so-called balanced state emerges. In this strong coupling limit, networks exhibit without fine tuning, a number of ubiquitous properties of cortex, such as the irregular nature of neuronal firing. However, it fails to account for nonlinear summation of inputs, since the strong coupling limit leads to a linear network transfer function. We show that, in networks of spiking neurons, nonlinearities at response-onset and saturation emerge at finite coupling. Critically, for realistic parameter values, both types of nonlinearities are observed at experimentally observed rates. Thus, we propose that these models could explain experimentally observed nonlinearities.

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bioRxiv2019

Inhibition stabilization is a widespread property of cortical networks

Many cortical network models use recurrent coupling strong enough to require inhibition for stabilization. Yet it has been experimentally unclear whether inhibition-stabilized network (ISN) models describe cortical function well across areas and states. Here we test several ISN predictions, including the counterintuitive (paradoxical) suppression of inhibitory firing in response to optogenetic inhibitory stimulation. We find clear evidence for ISN operation in mouse visual, somatosensory, and motor cortex. Simple two-population ISN models describe the data well and let us quantify coupling strength. Though some models predict a non-ISN to ISN transition with increasingly strong sensory stimuli, we find ISN effects without sensory stimulation and even during light anesthesia. Additionally, average paradoxical effects result only with transgenic, not viral, opsin expression in parvalbumin (PV)-positive neurons; theory and expression data show this is consistent with ISN operation. Taken together, these results show strong coupling and inhibition stabilization are common features of cortex.

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bioRxiv2018

Different inhibitory interneuron cell classes make distinct contributions to visual perception

While recent work has revealed how different inhibitory interneurons influence cortical responses to sensory stimuli, little is known about how their activity contributes to sensory perception. Here, we optogenetically stimulated different genetically defined interneurons (parvalbumin (PV), somatostatin (SST), vasoactive intestinal peptide (VIP)) in visual cortex (V1) of mice working at threshold in contrast increment or decrement detection tasks. The visual stimulus was paired with optogenetic stimulation to assess how enhancing V1 inhibitory neuron activity synchronously during cortical responses altered task performance. PV or SST activation impaired, while VIP stimulation improved, contrast increment detection. Notably, PV or SST stimulation also impaired contrast decrement detection, when opsin-evoked inhibition would exaggerate stimulus-evoked decrements in firing rate, and thus might improve performance. The impairment produced by PV or SST stimulation persisted throughout many weeks of testing. In contrast mice learned to reliably detect VIP activation in the absence of natural visual stimulation. Thus, different inhibitory signals make distinct contributions to visual contrast perception.

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bioRxiv2017

Feedforward inhibition allows input summation to vary in recurrent cortical networks

Brain computations depend on how neurons transform inputs to spike outputs. Here, to understand input-output transformations in cortical networks, we recorded spiking responses from visual cortex (V1) of awake mice of either sex while pairing sensory stimuli with optogenetic perturbation of excitatory and parvalbumin-positive inhibitory neurons. We found V1 neurons average responses were primarily additive (linear). We used a recurrent cortical network model to determine if these data, as well as past observations of nonlinearity, could be described by a common circuit architecture. The model showed cortical input-output transformations can be changed from linear to sublinear with moderate ([~]20%) strengthening of connections between inhibitory neurons, but this change depends on the presence of feedforward inhibition. Thus, feedforward inhibition, a common feature of cortical circuitry, enables networks to flexibly change their spiking responses via changes in recurrent connectivity.\n\nSignificance statementBrains are made up of neural networks that process information by receiving input activity and transforming those inputs into output activity. We use optogenetic manipulations in awake mice to expose how a transformation in a cortical network depends on internal network activity. Combining numerical simulations with our observations uncovers that transformation depend critically on feedforward inhibition - the fact that inputs to the cortex often make strong connections on both excitatory and inhibitory neurons.

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