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Lakshminarasimhan, K. J.

Publications and source records attributed to Lakshminarasimhan, K. J..

11 recordsLinked to original sources

A corticostriatal learning mechanism linking excess striatal dopamine and auditory hallucinations

1Auditory hallucinations are linked to elevated striatal dopamine, but their underlying computational mechanisms have been obscured by regional heterogeneity in striatal dopamine signaling. To address this, we developed a normative circuit model in which corticostriatal plasticity in the ventral striatum is modulated by reward prediction errors to drive reinforcement learning while that in the sensory-dorsal striatum is modulated by sensory prediction errors derived from internal belief to drive self-supervised learning. We then validate the key predictions of this model using dopamine recordings across striatal regions in mice, as well as human behavior across various tasks including a new hybrid learning task. Finally, we find that changes in learning resulting from optogenetic stimulation of the sensory-dorsal striatum in mice and individual variability in hallucinations in humans are best explained by selectively enhancing dopamine levels in the model sensory-dorsal striatum. These findings identify plasticity mechanisms underlying biased learning of sensory expectations as a biologically plausible link between excess dopamine and hallucinations.

neuroscience↗

A computational principle of habit formation

1Actions are influenced by multiple decision-making systems - including a goal-directed system that favors rewarded actions and a habit system that repeats past actions - but precisely when one system prevails is not known. We show that when competition between these systems is resolved by a winner-take-all mechanism, the precise condition for the emergence of habits can be cast in terms of the well-known probability matching principle. The theory embodies a trade-off in which exploitation, or overmatching, maximizes reward but strengthens habits, while paradoxically, exploration preserves goal-directed behavior by sacrificing rewards. This tradeoff can be averted if learning operates on abstract latent state representations whereby knowing the broader context allows for switching between two habits instead of avoiding one, thus maximizing rewards without forfeiting flexibility. The theory explains a range of animal behaviors as well as task-dependent effects of striatal manipulation, and suggests that neural mechanisms governing exploration implicitly control arbitration between decision-making systems.

animal behavior and cognition↗

Belief embodiment through eye movements facilitates memory-guided navigation

Neural network models optimized for task performance often excel at predicting neural activity but do not explain other properties such as the distributed representation across functionally distinct areas. Distributed representations may arise from animals strategies for resource utilization, however, fixation-based paradigms deprive animals of a vital resource: eye movements. During a naturalistic task in which humans use a joystick to steer and catch flashing fireflies in a virtual environment lacking position cues, subjects physically track the latent task variable with their gaze. We show this strategy to be true also during an inertial version of the task in the absence of optic flow and demonstrate that these task-relevant eye movements reflect an embodiment of the subjects dynamically evolving internal beliefs about the goal. A neural network model with tuned recurrent connectivity between oculomotor and evidence-integrating frontoparietal circuits accounted for this behavioral strategy. Critically, this model better explained neural data from monkeys posterior parietal cortex compared to task-optimized models unconstrained by such an oculomotor-based cognitive strategy. These results highlight the importance of unconstrained movement in working memory computations and establish a functional significance of oculomotor signals for evidence-integration and navigation computations via embodied cognition.

neuroscience↗

Dynamical Latent State Computation in the Posterior Parietal Cortex

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkeys displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

neuroscience↗

Eye movements reveal spatiotemporal dynamics of active sensing and planning in navigation

Goal-oriented navigation is widely understood to depend upon internal maps. Although this may be the case in many settings, humans tend to rely on vision in complex, unfamiliar environments. To study the nature of gaze during visually-guided navigation, we tasked humans to navigate to transiently visible goals in virtual mazes of varying levels of difficulty, observing that they took near-optimal trajectories in all arenas. By analyzing participants eye movements, we gained insights into how they performed visually-informed planning. The spatial distribution of gaze revealed that environmental complexity mediated a striking trade-off in the extent to which attention was directed towards two complimentary aspects of the world model: the reward location and task-relevant transitions. The temporal evolution of gaze revealed rapid, sequential prospection of the future path, evocative of neural replay. These findings suggest that the spatiotemporal characteristics of gaze during navigation are significantly shaped by the unique cognitive computations underlying real-world, sequential decision making.

neuroscience↗

Sensory evidence accumulation using optic flow in a naturalistic navigation task

Sensory evidence accumulation is considered a hallmark of decision-making in noisy environments. Integration of sensory inputs has been traditionally studied using passive stimuli, segregating perception from action. Lessons learned from this approach, however, may not generalize to ethological behaviors like navigation, where there is an active interplay between perception and action. We designed a sensory-based sequential decision task in virtual reality in which humans and monkeys navigated to a memorized location by integrating optic flow generated by their own joystick movements. A major challenge in such closed-loop tasks is that subjects actions will determine future sensory input, causing ambiguity about whether they rely on sensory input rather than expectations based solely on a learned model of the dynamics. To test whether subjects performed sensory integration, we used three independent experimental manipulations: unpredictable optic flow perturbations, which pushed subjects off their trajectory; gain manipulation of the joystick controller, which changed the consequences of actions; and manipulation of the optic flow density, which changed the reliability of sensory evidence. Our results suggest that both macaques and humans relied heavily on optic flow, thereby demonstrating a critical role for sensory evidence accumulation during naturalistic action-perception closed-loop tasks.

neuroscience↗

Influence of sensory modality and control dynamics on human path integration

Path integration is a sensorimotor computation that can be used to infer latent dynamical states by integrating self-motion cues. We studied the influence of sensory observation (visual/vestibular) and latent control dynamics (velocity/acceleration) on human path integration using a novel motion-cueing algorithm. Sensory modality and control dynamics were both varied randomly across trials, as participants controlled a joystick to steer to a memorized target location in virtual reality. Visual and vestibular steering cues allowed comparable accuracies only when participants controlled their acceleration, suggesting that vestibular signals, on their own, fail to support accurate path integration in the absence of sustained acceleration. Nevertheless, performance in all conditions reflected a failure to fully adapt to changes in the underlying control dynamics, a result that was well explained by a bias in the dynamics estimation. This work demonstrates how an incorrect internal model of control dynamics affects navigation in volatile environments in spite of continuous sensory feedback.

neuroscience↗

Increased variability but intact integration during visual navigation in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions from perception to cognition. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects dynamic belief states. Both groups showed a previously documented bias pattern, by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a non-uniform speed prior, rather than imperfect integration. We tracked participants beliefs and found no difference in the speed prior, but heightened variability in the ASD group. Both end-point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.

neuroscience↗

Tracking the mind’s eye: Primate gaze behavior during virtual visuomotor navigation reflects belief dynamics

To take the best actions, we often need to maintain and update beliefs about variables that cannot be directly observed. To understand the principles underlying such belief updates, we need tools to uncover subjects belief dynamics from natural behaviour. We tested whether eye movements could be used to infer subjects beliefs about latent variables using a naturalistic, visuomotor navigation task. We observed eye movements that appeared to continuously track the goal location even when no visible target was present there. Accurate goal-tracking was associated with improved task performance, and inhibiting eye movements in humans impaired navigation precision. By using passive stimulus playback and manipulating stimulus reliability, we show that subjects eye movements are likely voluntary, rather than reflexive. These results suggest that gaze dynamics play a key role in action-selection during challenging visuomotor behaviours, and may possibly serve as a window into the subjects dynamically evolving internal beliefs.

animal behavior and cognition↗

Speed tuning in the macaque posterior parietal cortex

Neurons in the macaque posterior parietal cortex are known to encode the direction of self-motion. But do they also encode ones speed? To test this, we performed neural recordings from area 7a while monkeys were passively translated or rotated at various speeds. Visual stimuli were delivered as optic flow fields and vestibular stimuli were generated by a motion platform. Under both conditions, the responses of a fraction of neurons scaled linearly with self-motion speed, and speed-selective neurons were not localized to specific layers or columns. We analyzed ensembles of simultaneously recorded neurons and found that the precision of speed representation was sufficient to support path integration over modest distances. Our findings describe a multisensory neural code for linear and angular self-motion speed in the posterior parietal cortex of the macaque brain, and suggest a potential role for this representation.

neuroscience↗

A Dynamic Bayesian Observer Model Reveals Origins of Bias in Visual Path Integration

Path integration is a navigation strategy by which animals track their position by integrating their self-motion velocity over time. To identify the computational origins of bias in visual path integration, we asked human subjects to navigate in a virtual environment using optic flow, and found that they generally travelled beyond the goal location. Such a behaviour could stem from leaky integration of unbiased self-motion velocity estimates, or from a prior expectation favouring slower speeds that causes underestimation of velocity. We tested both alternatives using a probabilistic framework that maximizes expected reward, and found that subjects biases were better explained by a slow-speed prior than imperfect integration. When subjects integrate paths over long periods, this framework intriguingly predicts a distance-dependent bias reversal due to build-up of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration performance is limited largely by biases in processing optic flow rather than by suboptimal signal integration.

neuroscience↗