Biology subjects

Narain, D.

Publications and source records attributed to Narain, D..

9 recordsLinked to original sources

bioRxiv2026

Impaired representation of temporal statistics in cerebellar degeneration

The nervous system represents the statistical structure of the environment to facilitate perception. In time perception, exploiting prior information through Bayesian inference reduces the influence of perceptual noise. However, the neural substrate underlying this computation remains unknown. The cerebellum is a strong candidate whose integrity is essential for motor and perceptual tasks requiring precise timing, and rodent physiological studies indicate that cerebellar plasticity mechanisms can support the encoding of temporal statistics. To examine how the cerebellum is involved in Bayesian timing, we tested patients with cerebellar degeneration and matched controls on duration reproduction tasks with varying prior distributions. Patients showed the signatures of Bayesian inference, but with greater timing variability, stronger regression toward the mean, and slower updating of the prior. We developed a model of the cerebellar microcircuit in which Purkinje cell plasticity over a granule cell temporal basis set provides a shared substrate for the prior and the inference. Simulations of cerebellar degeneration showed deficits consistent with the behavioral changes observed in the patients. These results suggest a critical role for the human cerebellum in Bayesian inference, with the system generating optimally timed behavior across varying contexts and even remaining robust under extensive neuropathology.

neuroscience↗

bioRxiv2026

Cortico-cerebellar beta-band dynamics predict flexible motor timing

Flexible motor control requires that movements adapt to changing temporal contexts. Here, we test whether flexible timing is driven by context-dependent encoding across cortico-cerebellar circuits and how neural dynamics within these circuits enable accurate performance. To overcome the signal-to-noise limitations of conventional electro- and magnetoencephalography, we recorded whole-head neural dynamics using optically pumped magnetometer arrays (OPM-MEG). Participants learned a context-dependent task, executing manual button presses at time intervals 800 ms (T1) and 1,600 ms (T2), respectively, to avoid a periocular air puff, which was associated with an implicit conditioned eyeblink. We found that the motor cortex contralateral to the hand and bilateral cerebellar lobule VI dynamically encoded these intervals through beta-band (13-30 Hz) event-related desynchronisation (ERD) that precisely scaled with T1 and T2. The cerebellar trial-by-trial latency of the beta-band ERD predicted the timing of explicit manual actions. Finally, partial directed coherence revealed that baseline bidirectional beta-band coupling across the network transiently weakened from the ipsilateral cerebellum to the contralateral motor cortex during finger movement execution. Our findings show that cortico-cerebellar coupling functions as a gating mechanism and suggest that cerebellar circuits modulate cortical motor activity for flexible, accurate motor timing.

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bioRxiv2024

RATS: Unsupervised manifold learning using low-distortion alignment of tangent spaces

With the ubiquity of high-dimensional datasets in various biological fields, identifying low-dimensional topological manifolds within such datasets may reveal principles connecting latent variables to measurable instances in the world. The reliable discovery of such manifold structure in high-dimensional datasets can prove challenging, however, largely due to the introduction of distortion by leading manifold learning methods. The problem is further exacerbated by the lack of consensus on how to evaluate the quality of the recovered manifolds. Here, we present a novel measure of distortion to evaluate low-dimensional representations obtained using different techniques. We additionally develop a novel bottom-up manifold learning technique called Riemannian Alignment of Tangent Spaces (RATS) that aims to recover low-distortion embeddings of data, including the ability to embed closed manifolds into their intrinsic dimension using a unique tearing process. Compared to previous methods, we show that RATS provides low-distortion embeddings that excel in the visualization and deciphering of latent variables across a range of idealized, biological, and surrogate datasets that mimic real-world data. One-sentence summaryWe introduce a novel dimensionality reduction technique that generates low-dimensional embeddings while preserving the global structure within the data for a variety of biological and non-biological datasets.

neuroscience↗

bioRxiv2024

Cerebellar encoding of prior knowledge of temporal statistics

We possess the ability to anticipate and preempt occurrences under familiar circumstances, which suggests a reliance on prior experience with regularities in our environment1-6, especially when observations become more uncertain. We know little about the neural mechanisms that can learn the probabilities of events in the environment and use this prior experience to guide actions. To examine this, we studied a rudimentary predictive behavior, eyeblink conditioning, and asked whether a simple effector like the eyelid could adapt its movements to varying probabilities of environmental events that reflect different degrees of uncertainty. We found that predictive eyeblink behavior systematically changed almost all its properties according to the temporal statistics of stimulus probability distributions. We also found that the activity of cerebellar Purkinje cells and putative molecular layer interneurons changed concomitantly with temporal statistics of the stimuli and with behavior. Targeted optogenetic perturbation of Purkinje cells during critical time windows severely attenuated the predictive behavior but left reflexive eyeblinks intact. Furthermore, we discovered a novel Purkinje cell complex spike signal coinciding with the onset of the earliest probable time interval in prior distributions with high uncertainty. This signal could not be explained as a motor or sensory correlate and appears to be anticipatory in nature. Theoretical modeling results pointed to a possible synaptic mechanism for how Purkinje cells could encode prior experience of environmental statistics in their activity through the juxtaposition of long-term depression and potentiation dynamics.

neuroscience↗

bioRxiv2024

Fast implicit and slow explicit learning of temporal context in cortico-cerebellar loops

One is seldom aware of the anticipatory and preemptive feats that the eyeblink systems achieves in daily life but it frequently protects the eye from projectiles gone awry and insects on apparent collision courses. This poor awareness is why predictive eyeblinks are considered a form of implicit learning. In motor neuroscience, implicit learning is considered to be slow and, eyeblink conditioning, in particular, is believed to be a rigid and inflexible cerebellar-dependent behavior. In cognitive neuroscience, however, implicit and automatic processes are thought to be rapidly acquired. Here we show that the eyeblink system is, in fact, capable of remarkable cognitive flexibility and can learn on more rapid timescales than previously expected. In a task where we yoked contextual learning of predictive eyeblinks and manual responses in humans, well-timed eyeblink responses flexibly adjusted to external context on each trial. The temporal precision of the predictive eyeblinks exceeded that of manual response times. Learning of the well-timed eyeblink responses was also more rapid than that for the manual response times. This pattern persevered with the use of a cognitive strategy, which seemed to accelerate both types of learning. These results suggest that behaviors associated with the cerebellar cortex that were previously believed to be inflexible and largely implicit, can demonstrate rapid and precise context-dependent temporal control.

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bioRxiv2018

Bayesian computation through cortical latent dynamics

Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged, and has had a major impact on models of perception 1, sensorimotor function 2,3, and cognition 4. However, it is not known how recurrent interactions among neurons mediate Bayesian integration. Using a time interval reproduction task in monkeys, we found that prior statistics warp the underlying structure of population activity in the frontal cortex allowing the mapping of sensory inputs to motor outputs to be biased in accordance with Bayesian inference. Analysis of neural network models performing the task revealed that this warping was mediated by a low-dimensional curved manifold, and allowed us to further probe the potential causal underpinnings of this computational strategy. These results uncover a simple and general principle whereby prior beliefs exert their influence on behavior by sculpting cortical latent dynamics.

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bioRxiv2018

Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics

Sensorimotor computations can be flexibly adjusted according to internal states and contextual inputs. The mechanisms supporting this flexibility are not understood. Here, we tested the utility of a dynamical system perspective to approach this problem. In a dynamical system whose state is determined by interactions among neurons, computations can be rapidly and flexibly reconfigured by controlling the system s inputs and initial conditions. To investigate whether the brain employs such control strategies, we recorded from the dorsomedial frontal cortex (DMFC) of monkeys trained to measure time intervals and subsequently produce timed motor responses according to multiple context-specific stimulus-response rules. Analysis of the geometry of neural states revealed a control mechanism that relied on the system s inputs and initial conditions. A tonic input specified by the behavioral context adjusted firing rates throughout each trial, while the dynamics in the measurement epoch allowed the system to establish initial conditions for the ensuing production epoch. This initial condition in turn set the speed of neural dynamics in the production epoch allowing the animal to aim for the target interval. These results provide evidence that the language of dynamical systems can be used to parsimoniously link brain activity to sensorimotor computations.

neuroscience↗

bioRxiv2017

Flexible control of speed of cortical dynamics

Musicians can perform at different tempos, speakers can control the cadence of their speech, and children can flexibly vary their temporal expectations of events. To understand the neural basis of such flexible timing, we recorded from the medial frontal cortex of primates trained to produce different time intervals with different effectors. The activity of neurons was heterogeneous, nonlinear and complex. However, responses were unified under a remarkable form of invariance: firing rate profiles were temporally stretched for longer intervals and compressed for short ones. At the network level, this phenomenon was evident by flexible changes in the speed with which the population activity traced an invariant trajectory. To identify the origin of speed control, we recorded from both downstream caudate neurons and thalamic neurons projecting to the medial frontal cortex. Speed adjustments were a prominent feature in the caudate but not in the thalamus suggesting that this phenomenon originates within cortical networks. To understand the underlying mechanisms, we created recurrent neural network models at different levels of complexity that could explain flexible timing with speed control. Analysis of the models revealed that the key to flexible speed control was the action of an external input upon the nonlinearities of individual neurons whose recurrent interactions set the networks relaxation dynamics. These findings demonstrate a simple and general mechanism for conferring temporal flexibility upon sensorimotor and cognitive functions.

neuroscience↗

bioRxiv2017

A cerebellar mechanism for learning prior distributions of time intervals

Knowledge about the statistical regularities of the world is essential for cognitive and sensorimotor function. In the domain of timing, prior statistics are crucial for optimal prediction, adaptation and planning. Where and how the nervous system encodes temporal statistics, however, is not known. Deriving from physiological and anatomical evidence for cerebellar learning, we develop a computational model that demonstrates how the cerebellum could learn prior distributions of time intervals and support Bayesian temporal estimation. The model shows that salient features observed in human Bayesian time interval estimates can be readily captured by learning in the cerebellar cortex and circuit level computations in the cerebellar deep nuclei.

neuroscience↗