Biology subjects

Darshan, R.

Publications and source records attributed to Darshan, R..

12 recordsLinked to original sources

bioRxiv2026

One Circuit, Many Flow Fields: Mechanistic Models of Single-Trial Neural Dynamics

A single neural circuit can exhibit qualitatively different dynamics across trials: one circuit, many flow fields. Standard models treat this trial-to-trial variability as noise around a fixed dynamical system or as discrete switches between regimes, yet neither captures how continuous internal-state variables, such as arousal or engagement, can gradually deform the circuits flow field. We propose that singletrial fitting can be reframed as inferring the low-dimensional control parameters that reshape a shared circuits flow field. We realize this with a low-rank recurrent network in which trial-specific static input biases act as bifurcation parameters: constant within a trial, they deform the flow field without directly driving activity over time. In a teacher-student setting, the model recovers the underlying dynamical system and its bifurcation structure from activity alone. Applied to large-scale recordings of mouse motor cortex during a delayed movement task, the model identifies a disengagement axis that separates engaged from disengaged trials and, when perturbed in silico, causally shifts the flow field between engaged and disengaged regimes. A generative extension reproduces the distribution of single-trial activity, and the inferred latent structure partially transfers across sessions and animals, suggesting shared low-dimensional structure across motor-cortical circuits. Together, these results reframe a methodological problem of fitting single-trial activity as a scientific opportunity: reading off the control parameters of the underlying dynamics, and connecting data-driven inference of neural dynamics to mechanistic theories of how a single circuit reuses its dynamics for flexible behavior.

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bioRxiv2025

Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification

Linking neural activity to behavior typically involves identifying activity subspaces that encode task information, such as stimuli, memory, and choices. However, it is unclear whether activity in these "coding dimensions" drives behavior or merely reflects the underlying computations. Neural activity in other "residual dimensions" is typically ignored. We developed a recurrent neural network that explicitly models interactions between coding and residual subspaces. Applied to multi-regional Neuropixels recordings from mice performing a delayed movement task, the model reveals that perturbations of residual dimensions reliably alter behavior, whereas perturbations of the choice dimension, which encodes the animals decision, are less effective. These effects arise because residual dimensions drive amplification across an intermediate number ([~]10) of dimensions, before the dynamics settle into discrete attractors corresponding to choice. Our findings show that neural activity previously considered task-irrelevant can have critical roles in driving behavior.

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bioRxiv2023

Connectivity underlying motor cortex activity during naturalistic goal-directed behavior

Neural representations of information are shaped by local network interactions. Previous studies linking neural coding and cortical connectivity focused on stimulus selectivity in the sensory cortex 1-4. Here we study neural activity in the motor cortex during naturalistic behavior in which mice gathered rewards with multidirectional tongue reaching. This behavior does not require training and thus allowed us to probe neural coding and connectivity in motor cortex before its activity is shaped by learning a specific task. Neurons typically responded during and after reaching movements and exhibited conjunctive tuning to target location and reward outcome. We used an all-optical 5,4,6,7 method for large-scale causal functional connectivity mapping in vivo. Mapping connectivity between > 20,000,000 excitatory neuronal pairs revealed fine-scale columnar architecture in layer 2/3 of the motor cortex. Neurons displayed local (< 100 {micro}m) like-to-like connectivity according to target-location tuning, and inhibition over longer spatial scales. Connectivity patterns comprised a continuum, with abundant weakly connected neurons and sparse strongly connected neurons that function as network hubs. Hub neurons were weakly tuned to target-location and reward-outcome but strongly influenced neighboring neurons. This network of neurons, encoding location and outcome of movements to different motor goals, may be a general substrate for rapid learning of complex, goal-directed behaviors.

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bioRxiv2022

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation can train networks of one million neurons, with 100 million plastic synapses and a billion static synapses, about 1000 times faster than an unoptimized reference CPU implementation. We demonstrate the codes utility by training a network, in less than an hour, to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive in-silico study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as in-vivo experiments are being conducted, thus closing the loop between modeling and experiments.

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bioRxiv2022

Sub-threshold neuronal activity and the dynamical regime of cerebral cortex

Cortical neurons exhibit temporally irregular spiking patterns and heterogeneous firing rates. These features arise in model circuits operating in a fluctuation-driven regime, in which fluctuations in membrane potentials emerge from the network dynamics. However, it is still unclear whether the cortex operates in this regime. We evaluated the fluctuation-driven hypothesis by analyzing spiking and sub-threshold membrane potentials of neurons in the sensory and frontal cortex recorded during a decision-making task. Standard fluctuation-driven models account for spiking statistics but fail to capture the heterogeneity in sub-threshold activity. We address this issue by effectively incorporating dendritic conductances into the standard models. Our model suggests that the frontal cortex operates in a fluctuation-driven regime. In contrast, excitatory neurons in layer 4 of the barrel cortex are not fluctuation-driven; they spike in response to occasional synchronous inputs. Our work reveals fundamental differences between cortical areas, suggesting that they operate in different dynamical regimes.

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bioRxiv2022

Distributing task-related neural activity across a cortical networkthrough task-independent connections

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a limited subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. We found that task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

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bioRxiv2022

Expectation-based learning rules underlie dynamic foraging in Drosophila

Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnsteins operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here we discovered operant matching in Drosophila and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a novel behavioral paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each flys sequential choice behavior using a family of biologically-realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synaptic level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.

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bioRxiv2021

Learning to represent continuous variables in heterogeneous neural networks

Animals must monitor continuous variables such as position or head direction. Manifold attractor networks--which enable a continuum of persistent neuronal states--provide a key framework to explain this monitoring ability. Neural networks with symmetric synaptic connectivity dominate this framework, but are inconsistent with the diverse synaptic connectivity and neuronal representations observed in experiments. Here, we developed a theory for manifold attractors in trained neural networks, which approximate a continuum of persistent states, without assuming unrealistic symmetry. We exploit the theory to predict how asymmetries in the representation and heterogeneity in the connectivity affect the formation of the manifold via training, shape network response to stimulus, and govern mechanisms that possibly lead to destabilization of the manifold. Our work suggests that the functional properties of manifold attractors in the brain can be inferred from the overlooked asymmetries in connectivity and in the low-dimensional representation of the encoded variable.

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bioRxiv2019

Neuronal activity and learning in local cortical networks are modulated by the action-perception state

During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

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bioRxiv2018

Idiosyncratic choice bias in decision tasks naturally emerges from neuronal network dynamics

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases that cannot be accounted for by the experimental context. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

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bioRxiv2018

How strong are correlations in strongly recurrent neuronal networks?

Cross-correlations in the activity in neural networks are commonly used to characterize their dynamical states and their anatomical and functional organizations. Yet, how these latter network features affect the spatiotemporal structure of the correlations in recurrent networks is not fully understood. Here, we develop a general theory for the emergence of correlated neuronal activity from the dynamics in strongly recurrent networks consisting of several populations of binary neurons. We apply this theory to the case in which the connectivity depends on the anatomical or functional distance between the neurons. We establish the architectural conditions under which the system settles into a dynamical state where correlations are strong, highly robust and spatially modulated. We show that such strong correlations arise if the network exhibits an effective feedforward structure. We establish how this feedforward structure determines the way correlations scale with the network size and the degree of the connectivity. In networks lacking an effective feedforward structure correlations are extremely small and only weakly depend on the number of connections per neuron. Our work shows how strong correlations can be consistent with highly irregular activity in recurrent networks, two key features of neuronal dynamics in the central nervous system.

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bioRxiv2016

A Canonical Neural Mechanism for Behavioral Variability

The ability to generate variable movements is essential for learning and adjusting complex behaviors. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioral variability is unclear. Here we combine modeling, electrophysiological and behavioral studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviors. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analyzing behavioral data, we find remarkable similarities in the babbling statistics of 5-6 month-old human infants and juveniles from three songbird species, and show that our model naturally accounts for these universal statistics.

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