Biology subjects

Mrsic-Flogel, T. D.

Publications and source records attributed to Mrsic-Flogel, T. D..

11 recordsLinked to original sources

bioRxiv2024

Zapit: Open Source Random-Access Photostimulation For Neuroscience

Optogenetic tools are indispensable for understanding the causal neural mechanisms underlying animal behavior,providing millisecond-precision control over genetically defined neural populations. Random-access laser-scanning optogenetics is a powerful and underutilized method for transiently manipulating cortical activity in mice. Despite the utility of this technique, no general-purpose open-source implementation currently exists, and potential users need expertise across optics, real-time hardware control, and programming. This represents a major barrier to adoption, particularly for newly established research groups. Here we present 'Zapit', the first open-source general-purpose platform for random-access laser-scanning optogenetic experiments in head-fixed mice. Zapit is fully documented, has a user-friendly GUI, works in stereotaxic coordinates, and comes with easy to build hardware options that extend functionality beyond any published system. We validate Zapit's performance through electrophysiological recordings and cortical photoinhibition in behaving mice. Zapit is a novel and innovative tool with the potential to democratize laser-scanning optogenetics and significantly increase uptake throughout the scientific community.

neuroscience↗

bioRxiv2023

Cooperative thalamocortical circuit mechanism for sensory prediction errors

The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and the outcomes of our actions. Discrepancies between expected and actual events, referred to as prediction errors, are leveraged to update the internal model and guide our attention towards unexpected events1-10. Despite the importance of prediction error signals for various neural computations across multiple brain regions, surprisingly little is known about the neural circuit mechanisms responsible for their implementation. Here we describe a thalamocortical disinhibitory circuit required for generating sensory prediction errors in mouse primary visual cortex (V1). Using calcium imaging with optogenetic manipulations as mice traverse a familiar virtual environment, we show that violation of animals predictions by an unexpected visual stimulus preferentially boosts responses of layer 2/3 V1 neurons most selective for that stimulus. Prediction errors specifically amplify the unexpected visual input, rather than representing a non-specific surprise or difference signal about how the visual input deviates from animals predictions. Selective amplification of unexpected visual input is implemented by a cooperative mechanism requiring thalamic input from the pulvinar, and cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response to prediction errors, VIP neurons inhibit a specific subpopulation of somatostatin-expressing (SOM) inhibitory interneurons that gate excitatory pulvinar input to V1, resulting in specific pulvinar-driven response-amplification of the most stimulus-selective neurons in V1. Therefore, the brain prioritizes unpredicted sensory information by selectively increasing the salience of unpredicted sensory features through the synergistic interaction of thalamic input and neocortical disinhibitory circuits.

neuroscience↗

bioRxiv2023

A Brain-Wide Map of Neural Activity during Complex Behaviour

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements and thereby make decisions. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 621733 neurons across 139 mice in 12 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 699 Neuropixels probe insertions covering 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encoded key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus negative feedback were also widespread. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

neuroscience↗

bioRxiv2023

Brain-wide representations of prior information in mouse decision-making

The neural representations of prior information about the state of the world are poorly understood. To investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACAd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.

neuroscience↗

bioRxiv2022

Bio-inspired neural networks implement different recurrent visual processing strategies than task-trained ones do

Behavioral studies suggest that recurrence in the visual system is important for processing degraded stimuli. There are two broad anatomical forms this recurrence can take, lateral or feedback, each with different assumed functions. Here we add four different kinds of recurrence--two of each anatomical form--to a feedforward convolutional neural network and find all forms capable of increasing the ability of the network to classify noisy digit images. Specifically, we take inspiration from findings in biology by adding predictive feedback and lateral surround suppression. To compare these forms of recurrence to anatomically-matched counterparts we also train feedback and lateral connections directly to classify degraded images. Counter-intuitively, we find that the anatomy of the recurrence is not related to its function: both forms of task-trained recurrence change neural activity and behavior similarly to each other and differently from their bio-inspired anatomical counterparts. By using several analysis tools frequently applied to neural data, we identified the distinct strategies used by the predictive versus task-trained networks. Specifically, predictive feedback de-noises the representation of noisy images at the first layer of the network and decreases its dimensionality, leading to an expected increase in classification performance. Surprisingly, in the task-trained networks, representations are not de-noised over time at the first layer (in fact, they become noiser and dimensionality increases) yet these dynamics do lead to de-noising at later layers. The analyses used here can be applied to real neural recordings to identify the strategies at play in the brain. Our analysis of an fMRI dataset weakly supports the predictive feedback model but points to a need for higher-resolution cross-regional data to understand recurrent visual processing..

neuroscience↗

bioRxiv2021

Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance, and across seconds when animals switch attention. While both phenomena are expressed in the same cortical circuit, it is unknown whether they rely on similar mechanisms. We imaged activity of the same neuronal populations in primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the task relevant stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM and VIP inhibitory cell classes. Circuit modelling revealed that cell class-specific top-down inputs best explained attentional modulation, while the reorganization of local functional connectivity accounted for learning related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter time scales.

neuroscience↗

bioRxiv2019

Mesoscale cortical dynamics reflect the interaction of sensory evidence and temporal expectation during perceptual decision-making

How sensory evidence is transformed across multiple brain regions to influence behavior remains poorly understood. We trained mice in a visual change detection task designed to separate the covert antecedents of choices from activity associated with their execution. Widefield calcium imaging across dorsal cortex revealed fundamentally different dynamics of activity underlying these processes. While signals related to execution of choice were widespread, fluctuations in sensory evidence in the absence of overt motor responses triggered a confined activity cascade, beginning with transient modulation of visual cortex, followed by sustained recruitment of secondary and primary motor cortex. The activation of motor cortex by sensory evidence was selectively gated by animals expectation of when the stimulus was likely to change. These results identify distinct activation profiles of specific cortical areas during decision-making, and show that the recruitment of motor cortex depends on the interaction of sensory evidence and expectation.

neuroscience↗

bioRxiv2018

Cerebellar contribution to preparatory activity in motor neocortex

In motor-related areas of the neocortex, preparatory neuronal activity predictive of specific actions emerges seconds in advance of movement and is maintained by a positive feedback loop with the thalamus. A major source of excitatory drive to the motor thalamus is the cerebellum, which has been implicated in coordination and timing of learned actions. However, the cerebellar contribution to neocortical signals coupled to movement planning remains poorly understood. Here we show that cerebellar output neurons in the dentate nucleus exhibit preparatory ramping activity in anticipation of expected rewards in a virtual reality conditioning task, a profile similar to that recorded in anterolateral motor neocortex (ALM). The preparatory activity in the dentate nucleus is controlled by a disinhibitory circuit involving inhibitory Purkinje cells in the cerebellar cortex, many of which suppress their firing in advance of rewards. Silencing the activity in the dentate nucleus by photoactivation of Purkinje cells caused robust, short-latency suppression in the majority of ALM neurons exhibiting preparatory activity. Thus, preparatory signals in motor neocortex require the output of the cerebellum. We suggest that the reciprocal circuitry between neocortex and cerebellum generates the sequence of activity required for planning and temporal coordination of learned, goal- directed actions.

neuroscience↗

bioRxiv2018

Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex

In the cerebral cortex, the interaction of excitatory and inhibitory synaptic inputs shapes the responses of neurons to sensory stimuli, stabilizes network dynamics1 and improves the efficiency and robustness of the neural code2-4. Excitatory neurons receive inhibitory inputs that track excitation5-8. However, how this co-tuning of excitation and inhibition is achieved by cortical circuits is unclear, since inhibitory interneurons are thought to pool the inputs of nearby excitatory cells and provide them with non-specific inhibition proportional to the activity of the local network9-13. Here we show that although parvalbumin-expressing (PV) inhibitory cells in mouse primary visual cortex make connections with the majority of nearby pyramidal cells, the strength of their synaptic connections is structured according to the similarity of the cells responses. Individual PV cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This fine-tuning of synaptic weights supports co-tuning of inhibitory and excitatory inputs onto individual pyramidal cells despite dense connectivity between inhibitory and excitatory neurons. Our results indicate that individual PV cells are preferentially integrated into subnetworks of inter-connected, co-tuned pyramidal cells, stabilising their recurrent dynamics. Conversely, weak but dense inhibitory connectivity between subnetworks is sufficient to support competition between them, de-correlating their output. We suggest that the history and structure of correlated firing adjusts the weights of both inhibitory and excitatory connections, supporting stable amplification and selective recruitment of cortical subnetworks.

neuroscience↗

bioRxiv2017

Exclusive functional subnetworks of intracortical projection neurons in primary visual cortex

The rules by which neurons in neocortex choose their synaptic partners are not fully understood. In sensory cortex, intermingled neurons encode different attributes of sensory inputs and relay them to different long-range targets. While neurons with similar responses to sensory stimuli make connections preferentially, the relationship between synaptic connectivity within an area and long-range projection target remains unclear. We examined the local connectivity and visual responses of primary visual cortex neurons projecting to anterolateral (AL) and posteromedial (PM) higher visual areas in mice. Although the response properties of layer 2/3 neurons projecting to different targets were often similar, they avoided making connections with each other. Thus, projection target acts independently of response similarity to constrain local synaptic connectivity of cortical neurons. We propose that this segregated connectivity rule reduces the crosstalk between different populations of projection neurons, allowing top-down signal to modulate the activity of these output channels independently.

neuroscience↗

bioRxiv2017

A single-cell anatomical blueprint for intracortical information transfer from primary visual cortex

The wiring diagram of the neocortex determines how information is processed across dozens of cortical areas. Each area communicates with multiple others via extensive long-range axonal projections 1-6, but the logic of inter-area information transfer is unresolved. Specifically, the extent to which individual neurons send dedicated projections to single cortical targets or distribute their signals across multiple areas remains unclear5,7-20. Distinguishing between these possibilities has been challenging because axonal projections of only a few individual neurons have been reconstructed. Here we map the projection patterns of axonal arbors from 591 individual neurons in mouse primary visual cortex (V1) using two complementary methods: whole-brain fluorescence-based axonal tracing21,22 and high-throughput DNA sequencing of genetically barcoded neurons (MAPseq)23. Although our results confirm the existence of dedicated projections to certain cortical areas, we find these are the exception, and that the majority of V1 neurons broadcast information to multiple cortical targets. Furthermore, broadcasting cells do not project to all targets randomly, but rather comprise subpopulations that either avoid or preferentially innervate specific subsets of cortical areas. Our data argue against a model of dedicated lines of intracortical information transfer via \"one neuron - one target area\" mapping. Instead, long-range communication between a sensory cortical area and its targets may be based on a principle whereby individual neurons copy information to, and potentially coordinate activity across, specific subsets of cortical areas.

neuroscience↗