Biology subjects

Dadarlat, M. C.

Publications and source records attributed to Dadarlat, M. C..

5 recordsLinked to original sources

bioRxiv2026

A redundant encoding algorithm for artificial sensory information speeds learning and improves multisensory-guided navigation

Intracortical microstimulation (ICMS) directly modulates cortical activity, providing an artificial sensory stream to guide accurate control of prosthetic limbs. Yet, only a fraction of reported sensations evoked by ICMS are proprioceptive (i.e., describing the position and movement of the body). Taking a learning-based approach to encoding an artificial proprioceptive signal bypasses this limitation. For example, animals trained with multi-channel ICMS in association with natural vision learn to both decode the ICMS signal to localize an invisible target and learn to integrate artificial sensation with natural sensation. The limitation of a learning-based approach, however, is that it requires training. We hypothesized that changing the algorithm used to encode artificial sensory information could both reduce learning time and improve plateau performance on ICMS-guided navigation. To test this idea, we trained eight mice on a sensory-guided navigation task: mice were required to locate a target in a training cage, the position of which was encoded by a red circle (vision), by multi-channel ICMS (ICMS trials), or by both vision and ICMS (multimodal). ICMS was encoded using one of two algorithms: sparse (fewer simultaneously stimulating electrodes) or redundant (more simultaneously stimulating electrodes). We found that redundant encoding sped learning of the ICMS signal relative to sparse encoding. Further, mice guided by redundant ICMS ran faster and completed trials in less time than when guided by sparse ICMS. Having redundant encoding also facilitated multisensory integration of ICMS with natural vision, improving success rates, path efficiency, movement speed, and movement time. We conclude that optimized ICMS encoding algorithms could overcome the current limitations of learning-based sensory encoding, facilitating learning and integration of artificial sensory information into existing sensorimotor neural circuits, paving the way to restoring both the sensory and motor streams of information flow in damaged sensorimotor systems.

neuroscience↗

bioRxiv2026

A shared multi-feature population code for sensory reliability across mouse visual cortex

Optimal use of sensory information requires the brain to represent uncertainty alongside sensory variables. Yet, the neural response properties that mediate reliability encoding remain undefined. Using large-scale two-photon calcium imaging of 140,000 neurons in primary visual cortex (V1) and higher visual areas (HVAs) during presentation of random dot kine-matograms, we identified a common coding strategy in which sensory reliability is represented via coordinated changes in response gain, selectivity, and variability. These response properties are expressed at both single neuron and population levels and are consistent across areas, with quantitative differences in magnitude that track retinotopic drive and functional specialization. Together, our results establish reliability encoding as a general cortical computation implemented through a shared population code, providing empirical constraints on probabilistic theories of perception and hierarchical models of visual processing.

neuroscience↗

bioRxiv2022

Spatiotemporal recruitment of inhibition and excitation in the mammalian cortex during electrical stimulation

Electrical stimulation has emerged as a powerful and precise treatment in which to modulate aberrant neural activity patterns common in neural dysfunction and disease; however, the physiological process involved in microstimulation is poorly understood, particularly regarding the contributions of inhibitory neurons to shaping stimulation-evoked activity. To address this issue, we used 2-photon imaging of transgenic mice to measure the widespread responses of inhibitory and excitatory neurons to electrical stimulation through a chronically-implanted cortical microelectrode. We found that increasing stimulation amplitude both raised the fraction of neurons that responded to a stimulus and increased the distance at which inhibitory and excitatory neurons were significantly modulated by stimulation; however, the lateral spread of inhibitory activity preceded that of excitatory activity. By 50 {micro}A, a significantly larger fraction of inhibitory neurons vs. excitatory neurons were modulated by stimulation. Increasing amplitude also shifted the temporal response properties of the population -- towards longer-latency excitation close to the electrode tip and strong inhibition of more distant neurons. Animal behavior, specifically their locomotion patterns, strongly correlated with trial-to-trial variability in stimulation-evoked responses. We conclude that changing electrical stimulation amplitude can shift the balance of excitation to inhibition in the brain in a manner that interact with ongoing animal behavior.

neuroscience↗

bioRxiv2018

Flow stimuli reveal ecologically-appropriate responses in mouse visual cortex

Assessments of the mouse visual system based on spatial frequency analysis imply that its visual capacity is low, with few neurons responding to spatial frequencies greater than 0.5 cycles/degree. However, visually-mediated behaviors, such as prey capture, suggest that the mouse visual system is more precise. We introduce a new stimulus class--visual flow patterns--that is more like what the mouse would encounter in the natural world than are sine-wave gratings but is more tractable for analysis than are natural images. We used 128-site silicon microelectrodes to measure the simultaneous responses of single neurons in the primary visual cortex (V1) of alert mice. While holding temporal-frequency content fixed, we explored a class of drifting patterns of black or white dots that have energy only at higher spatial frequencies. These flow stimuli evoke strong visually-mediated responses well beyond those predicted by spatial frequency analysis. Flow responses predominate in higher spatial-frequency ranges (0.15-1.6 cycles/degree); many are orientation- or direction-selective; and flow responses of many neurons depend strongly on sign of contrast. Many cells exhibit distributed responses across our stimulus ensemble. Together, these results challenge conventional linear approaches to visual processing and expand our understanding of the mouses visual capacity to behaviorally-relevant ranges.\n\nSignificance StatementThe visual system of the mouse is now widely studied as a model for development and disease in humans. Studies of its primary visual cortex (V1) using conventional grating stimuli to construct linear-nonlinear receptive fields suggest that the mouse must have very poor vision. Using novel stimuli resembling the flow of images across the retina as the mouse moves through the grass, we find that most V1 neurons respond reliably to very much finer details of the visual scene than previously believed. Our findings suggest that the conventional notion of a unique receptive field does not capture the operation of the neural network in mouse V1.

neuroscience↗

bioRxiv2017

Locomotion enhances neural encoding of visual stimuli in mouse V1

Neurons in mouse primary visual cortex (V1) are selective for particular properties of visual stimuli. Locomotion causes a change in cortical state that leaves their selectivity unchanged but strengthens their responses. Both locomotion and the change in cortical state are initiated by projections from the mesencephalic locomotor region (MLR), the latter through a disinhibitory circuit in V1. The function served by this change in cortical state is unknown. By recording simultaneously from a large number of single neurons in alert mice viewing moving gratings, we investigated the relationship between locomotion and the information contained within the neural population. We found that locomotion improved encoding of visual stimuli in V1 by two mechanisms. First, locomotion-induced increases in firing rates enhanced the mutual information between visual stimuli and single neuron responses over a fixed window of time. Second, stimulus discriminability was improved, even for fixed population firing rates, because of a decrease in noise correlations across the population during locomotion. These two mechanisms contributed differently to improvements in discriminability across cortical layers, with changes in firing rates most important in the upper layers and changes in noise correlations most important in layer V. Together, these changes resulted in a three- to five-fold reduction in the time needed to precisely encode grating direction and orientation. These results support the hypothesis that cortical state shifts during locomotion to accommodate an increased load on the visual system when mice are moving.\n\nSignificance StatementThis paper contains three novel findings about the representation of information in neurons within the primary visual cortex of the mouse. First, we show that locomotion reduces by at least a factor of three the time needed for information to accumulate in the visual cortex that allows the distinction of different visual stimuli. Second, we show that the effect of locomotion is to increase information in cells of all layers of the visual cortex. Third we show that the means by which information is enhanced by locomotion differs between the upper layers, where the major effect is the increasing of firing rates, and in layer V, where the major effect is the reduction in noise correlations.

neuroscience↗