Biology subjects

Kalidindi, H. T.

Publications and source records attributed to Kalidindi, H. T..

6 recordsLinked to original sources

bioRxiv2026

When anticipation is not enough: a mixture of robust and adaptive feedback control strategies improve reaching in dynamic environments

Successful goal-directed movements depend on the central nervous systems (CNS) ability to handle diverse physical interactions. The CNS is thought to handle different dynamical contexts through three mechanisms: (i) trial-by-trial adaptation when forces are predictable, (ii) a model-free robust control strategy, and (iii) online adaptation of feedback responses. While each has been studied independently, their relative contributions and the possibility that they are recruited to different extents across contexts is unknown. Here, we quantified all three strategies within the same individuals to examine how CNS exploits them under varying environmental conditions. Participants (19 female, 15 male) performed reaching tasks while interacting with robot-generated force-fields that were either consistent or varied unpredictably. Trial-by-trial adaptation was measured using standard force channels to isolate anticipatory compensation. Robust control was assessed through movement velocity and corrective force magnitude. Online adaptive control was quantified by the temporal alignment between commanded and measured forces within a movement. Results showed that participants improved anticipatory compensation in consistent environments and relied on both robust and online adaptation when perturbations were unpredictable. Crucially, markers of robust control dominated the early movement phase, whereas online adaptation dominated later corrections. This temporal dissociation was confirmed by electromyographic recordings. Markers of robust and online adaptive feedback strategies also statistically predicted participants ability to adapt across trials in consistent environments, revealing a common trait linking online control and adaptation. These findings reveal a rich and flexible combination of control mechanisms, offering a new framework for understanding the neurophysiological bases of reaching control. Significance StatementHuman reaching control is a complex behavior resulting from several mechanisms that orchestrate feedback responses to mechanical perturbations and adaptation to changes in the environment. Here we combine previously studied paradigms to highlight within the same groups of healthy volunteers that three major components are recruited to different extents dependent on the context: unpredictable environment promote concomitant use of robust control and online adaptation whereas predictable environments recruit standard adaptation based on anticipatory compensation. Remarkably, individuals adaptive capabilities correlated across consistent and inconsistent environments, suggesting a key involvement of adaptive mechanisms in both online control and trial-by-trial adaptation. Robust control, online adaptation, and anticipatory compensation are dissociable behaviorally, and are used to varying levels as a result of individual traits.

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bioRxiv2025

Neural Population Mechanisms for Flexible Sensorimotor Control

Modern large-scale recordings have revealed that motor cortex activity during reaching follows low-dimensional dynamics, thought to reflect sensorimotor computations underlying muscle activation. However, the origin of these low dimensional patterns, and how they flexibly reorganize across different tasks remain unclear. Here we demonstrate that the key features of neural activity can naturally emerge in a linear model combining a random network with a biomechanical system. Remarkably, this model shows explicitly how a fixed network can achieve flexible control of multiple behaviours through optimal mapping of sensory feedback onto the network. Finally, analytical decomposition of the controller reveals that low-dimensional network dynamics follow directly from the propagation of low-dimensional feedback signals from the biomechanical plant through the network. This formalism provides a computational framework to interpret flexible motor control in the nervous system, which directly links neural population dynamics to sensorimotor behavior.

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bioRxiv2024

Parallel feedback processing for voluntary control: knowledge of spinal feedback in motor cortex

How hierarchical feedback pathways coordinate goal-directed motor corrections remains unknown. We show that systematic increases in spinal reflex responses to applied loads lead to a reciprocal reduction in the neural response in primary motor cortex. We use a neural network model to highlight how knowledge on lower-level feedback processing (i.e. spinal reflexes) can be embedded into a network to generate goal-directed corrections through parallel feedback pathways.

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bioRxiv2023

Task dependent coarticulation of movement sequences

Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co-articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.

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bioRxiv2022

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and vigor of saccadic eye movements.

Saccadic eye-movements play a crucial role in visuo-motor control by allowing rapid foveation onto new targets. However, the neural processes governing saccades adaptation are not fully understood. Saccades, due to the short-time of execution (20-100 ms) and the absence of sensory information for online feedback control, must be controlled in a ballistic manner. Incomplete measurements of the movement trajectory, such as the visual end-point error, are supposedly used to form internal predictions about the movement kinematics resulting in predictive control. In order to characterize the synaptic and neural circuit mechanisms underlying predictive saccadic control, we have reconstructed the saccadic system in a digital controller embedding a spiking neural network of the cerebellum with spike timing-dependent plasticity (STDP) rules driving parallel fiber - Purkinje cell long-term potentiation and depression (LTP and LTD). This model implements a control policy based on a dual plasticity mechanism, resulting in the identification of the roles of LTP and LTD in optimizing saccade movement control: it turns out that LTD regulates the accuracy and LTP the speed (vigor) of the ballistic eye movement. The control policy also required cerebellar PCs to be divided into two subpopulations, characterized by burst or pause responses. To our knowledge, this is the first model that explains in mechanistic terms the accuracy and vigor regulation of ballistic eye movements in forward mode exploiting spike-timing to regulate firing in different populations of the neuronal network. This elementary model of saccades could be extended and applied to other more complex cases in which single jerks are concatenated to compose articulated and coordinated movements. Author summaryIt has been suggested that the cerebellum plays a crucial role in oculomotor adaptation. Computationally, the cerebellum is described as a supervised learner whose activity can be adjusted by synaptic changes proportional to the amount of mismatch between expected and actual movements outcomes (sensorimotor-errors). However, the spike-timing-dependent-plasticity (STDP) underlying adaptation has been so far modeled in behaviors where the error is continuously available as tracking-error. Such models depend on detailed tracking-errors to improve movement quality. We asked if the cerebellum can maintain good motor control even if the error is not completely available? This is important considering that error-dependent-STDP is only a subset among a family of STDP processes in the cerebellum. Moreover, even the physiological signals that were generally thought to carry sensorimotor-errors (called complex-spikes) are shown to encode multiple types of information regarding the movement. In this context, we characterize the role of cerebellar STDPs in saccade control, where the error information is constrained because of sensory suppression. We show that even in the absence of detailed error, the cerebellum can leverage two of its STDPs to increase movement quality. Hence, we emphasize the need to go beyond error-centric view to understand how the cerebellum improves motor behavior.

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bioRxiv2020

Rotational dynamics in motor cortex are consistent with a feedback controller

Recent studies hypothesize that motor cortical (MC) dynamics are generated largely through its recurrent connections based on observations that MC activity exhibits rotational structure. However, behavioural and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback about the limb. Networks were trained to counteract perturbations to the limb and to reach towards spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may reflect dynamics throughout voluntary motor circuits involved in online control of motor actions. HighlightsO_LINeural networks with sensory feedback generate rotational dynamics during simulated posture and reaching tasks C_LIO_LIRotational dynamics are observed even without recurrent connections in the network C_LIO_LISimilar dynamics are observed not only in motor cortex, but also in somatosensory cortex of non-huma n primates as well as sensory feedback signals C_LIO_LIResults highlight rotational dynamics may reflect internal dynamics, external inputs or any combination of the two. C_LI

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