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  1. Article ; Online: Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons

    Steven Jack Jerjian / Maneesh Sahani / Alexander Kraskov

    eLife, Vol

    2020  Volume 9

    Abstract: Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and (M1) provide direct input to spinal circuitry and are critical for skilled movement control. Contrary to initial hypotheses, they can also be active during action ... ...

    Abstract Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and (M1) provide direct input to spinal circuitry and are critical for skilled movement control. Contrary to initial hypotheses, they can also be active during action observation, in the absence of any movement. A population-level understanding of this phenomenon is currently lacking. We recorded from single neurons, including identified PTNs, in (M1) (n = 187), and F5 (n = 115) as two adult male macaques executed, observed, or withheld (NoGo) reach-to-grasp actions. F5 maintained a similar representation of grasping actions during both execution and observation. In contrast, although many individual M1 neurons were active during observation, M1 population activity was distinct from execution, and more closely aligned to NoGo activity, suggesting this activity contributes to withholding of self-movement. M1 and its outputs may dissociate initiation of movement from representation of grasp in order to flexibly guide behaviour.
    Keywords pyramidal tract neurons ; mirror neurons ; primary motor cortex ; PCA ; ventral premotor cortex ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Subject code 590
    Language English
    Publishing date 2020-07-01T00:00:00Z
    Publisher eLife Sciences Publications Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction.

    Ross S Williamson / Maneesh Sahani / Jonathan W Pillow

    PLoS Computational Biology, Vol 11, Iss 4, p e

    2015  Volume 1004141

    Abstract: Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an ... ...

    Abstract Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex.
    Keywords Biology (General) ; QH301-705.5
    Subject code 310
    Language English
    Publishing date 2015-04-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Prior context in audition informs binding and shapes simple features

    Claire Chambers / Sahar Akram / Vincent Adam / Claire Pelofi / Maneesh Sahani / Shihab Shamma / Daniel Pressnitzer

    Nature Communications, Vol 8, Iss 1, Pp 1-

    2017  Volume 11

    Abstract: Perception can be swayed by prior context. Here the authors report an auditory illusion in which sounds with ambiguous pitch shifts are perceived as shifting upward or downward based on the preceding contextual sounds, explore the neural correlates, and ... ...

    Abstract Perception can be swayed by prior context. Here the authors report an auditory illusion in which sounds with ambiguous pitch shifts are perceived as shifting upward or downward based on the preceding contextual sounds, explore the neural correlates, and propose a probabilistic model based on temporal binding.
    Keywords Science ; Q
    Language English
    Publishing date 2017-04-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Outlier responses reflect sensitivity to statistical structure in the human brain.

    Marta I Garrido / Maneesh Sahani / Raymond J Dolan

    PLoS Computational Biology, Vol 9, Iss 3, p e

    2013  Volume 1002999

    Abstract: We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction ... ...

    Abstract We constantly look for patterns in the environment that allow us to learn its key regularities. These regularities are fundamental in enabling us to make predictions about what is likely to happen next. The physiological study of regularity extraction has focused primarily on repetitive sequence-based rules within the sensory environment, or on stimulus-outcome associations in the context of reward-based decision-making. Here we ask whether we implicitly encode non-sequential stochastic regularities, and detect violations therein. We addressed this question using a novel experimental design and both behavioural and magnetoencephalographic (MEG) metrics associated with responses to pure-tone sounds with frequencies sampled from a Gaussian distribution. We observed that sounds in the tail of the distribution evoked a larger response than those that fell at the centre. This response resembled the mismatch negativity (MMN) evoked by surprising or unlikely events in traditional oddball paradigms. Crucially, responses to physically identical outliers were greater when the distribution was narrower. These results show that humans implicitly keep track of the uncertainty induced by apparently random distributions of sensory events. Source reconstruction suggested that the statistical-context-sensitive responses arose in a temporo-parietal network, areas that have been associated with attention orientation to unexpected events. Our results demonstrate a very early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. We suggest that this sensitivity provides a computational basis for our ability to make perceptual inferences in noisy environments and to make decisions in an uncertain world.
    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2013-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: A structured model of video reproduces primary visual cortical organisation.

    Pietro Berkes / Richard E Turner / Maneesh Sahani

    PLoS Computational Biology, Vol 5, Iss 9, p e

    2009  Volume 1000495

    Abstract: The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response ... ...

    Abstract The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.
    Keywords Biology (General) ; QH301-705.5
    Subject code 612
    Language English
    Publishing date 2009-09-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Inhibitory control of correlated intrinsic variability in cortical networks

    Carsen Stringer / Marius Pachitariu / Nicholas A Steinmetz / Michael Okun / Peter Bartho / Kenneth D Harris / Maneesh Sahani / Nicholas A Lesica

    eLife, Vol

    2016  Volume 5

    Abstract: Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, ... ...

    Abstract Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.
    Keywords Gerbil ; neural networks ; inhibition ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
    Subject code 612
    Language English
    Publishing date 2016-12-01T00:00:00Z
    Publisher eLife Sciences Publications Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface

    Eric M. Trautmann / Daniel J. O’Shea / Xulu Sun / James H. Marshel / Ailey Crow / Brian Hsueh / Sam Vesuna / Lucas Cofer / Gergő Bohner / Will Allen / Isaac Kauvar / Sean Quirin / Matthew MacDougall / Yuzhi Chen / Matthew P. Whitmire / Charu Ramakrishnan / Maneesh Sahani / Eyal Seidemann / Stephen I. Ryu /
    Karl Deisseroth / Krishna V. Shenoy

    Nature Communications, Vol 12, Iss 1, Pp 1-

    2021  Volume 20

    Abstract: Surface two-photon imaging of the brain cannot access somatic calcium signals of neurons from deep layers of the macaque cortex. Here, the authors present an implant and imaging system for chronic motion-stabilized two-photon imaging of dendritic calcium ...

    Abstract Surface two-photon imaging of the brain cannot access somatic calcium signals of neurons from deep layers of the macaque cortex. Here, the authors present an implant and imaging system for chronic motion-stabilized two-photon imaging of dendritic calcium signals to drive an optical brain-computer interface in macaques.
    Keywords Science ; Q
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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