Biology subjects

Chandrasekaran, C.

Publications and source records attributed to Chandrasekaran, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Distinct neural dynamics in prefrontal and premotor cortex during flexibleperceptual decisions

The neural mechanisms underlying flexible perceptual decisions, where the mapping between sensory input and motor action changes depending on context, remain unclear. Here we show that prefrontal and premotor cortex use distinct neural dynamics to implement different computations during flexible decisions. We trained monkeys to discriminate the dominant color of a red-green checkerboard and report their decision by choosing one of two targets1;2. By randomizing the target configuration on a trial-by-trial basis, we ensured a flexible mapping between color (red vs. green) and action choice (left vs. right), necessitating a nonlinear exclusive- or (XOR) computation3-5. We found that neural dynamics in dorsolateral prefrontal cortex (DLPFC) led to higher-dimensional population representations than those in dorsal premotor cortex (PMd). Neural activity in DLPFC first separated by target configuration, then by color choice and action choice after stimulus onset, reflecting the XOR computation. In contrast, neural dynamics in PMd led to lower-dimensional representations that only reflected action choice, the output of the XOR computation. These higher-dimensional representations in DLPFC enabled earlier decoding of both color choice and action choice compared to PMd, and were strongest in anterior and ventral DLPFC6. These findings reveal distinct computations by neural dynamics: prefrontal cortex implements flexible sensorimotor mappings through high-dimensional representations while dorsal premotor cortex reflects only the selected action.

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bioRxiv2025

A multimodal approach for visualization and identification of electrophysiological cell types in vivo

Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. To identify cell types, we developed PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that such alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can be used to infer multiple cell types simultaneously in unannotated datasets and show that the properties of inferred types are consistent with the known properties of these cell types. Finally, we provide a first-of-its-kind demonstration of how PhysMAP can help understand how multiple cell types interact to drive circuit dynamics. Collectively, these results demonstrate that multimodal representations from PhysMAP enable the study of multiple cell types simultaneously, thus providing insight into neural circuit dynamics.

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bioRxiv2025

High-resolution laminar recordings reveal structure-function relationships in monkey V1

The relationship between structural properties of diverse neuronal populations in monkey primary visual cortex (V1) and their in vivo functional responses is not fully understood. We combined high-density Neuropixels recordings across cortical layers of macaque V1 with non-linear dimensionality reduction on waveform shape to delineate nine putative cell classes: 4 narrow-spiking (NS), 4 broad-spiking (BS) and 1 tri-phasic (TP). Using targeted analyses of laminar organization, spike amplitude, multichannel waveforms, functional properties, and network connectivity of these cell classes, we demonstrate four aspects of the V1 microcircuit predicted by anatomical studies but never fully demonstrated in vivo. First, NS neurons were concentrated in layer 4. Second, a large-amplitude NS cell class in layer 4B showed strong direction selectivity. Third, another layer 4B NS class exhibited robust bursting and orientation selectivity. Finally, cross-correlation analysis revealed functional interactions between cells in different layers. Our results highlight how high-resolution electrophysiology can reveal novel relationships between in vivo function of neurons and the underlying circuit. TeaserHigh-resolution electrophysiology used with machine learning reveals links between function and the underlying neural circuitry.

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bioRxiv2025

A ubiquitous spectrolaminar motif across independent studies, including in the Mackey et al. dataset

Our study (Mendoza-Halliday et al., 2024) made two contributions: (1) discovery of a ubiquitous cortical motif and (2) a tool derived from it--the Frequency-based Layer Identification Procedure (FLIP and vFLIP). Mackey et al. critique the tool, questioning its advantage over classic current source density (CSD) analysis, and reason backwards to challenge the motifs ubiquity. In our rebuttal, we confirm the spectrolaminar motif in diverse cortical areas using data from multiple research groups (who joined us in this rebuttal) as well as Mackey et al.s own dataset. Additionally, we introduce vFLIP2, an improved version of our tool that addresses their comments. It reliably identified and localized the motif in our data and Mackey et al.s data. Our findings reaffirm the motifs ubiquity. We value Mackey et al.s comments, which helped refine our tool.

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bioRxiv2024

In vivo cell-type and brain region classification via multimodal contrastive learning

AO_SCPLOWBSTRACTC_SCPLOWCurrent electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this approach to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, Neuronal Embeddings via MultimOdal contrastive learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for two opto-tagged datasets and brain region classification for the public International Brain Laboratory Brain-wide Map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings. The project page and code are available at https://ibl-nemo.github.io/.

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bioRxiv2024

PhysMAP - interpretable in vivo neuronal cell type identification using multi-modal analysis of electrophysiological data

Cells of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological approaches can measure the activity of many neurons simultaneously, assigning cell type labels to these neurons is an open problem. Here, we develop PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously in an unsupervised manner and obtain an interpretable representation that separates neurons by cell type. PhysMAP is superior to any single electrophysiological modality in identifying neuronal cell types such as excitatory pyramidal, PV+ interneurons, and SOM+ interneurons with high confidence in both juxtacellular and extracellular recordings and from multiple areas of the mouse brain. PhysMAP built on ground truth data can be used for classifying cell types in new and existing electrophysiological datasets, and thus facilitate simultaneous assessment of the coordinated dynamics of multiple neuronal cell types during behavior.

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bioRxiv2024

A doubly stochastic renewal framework for partitioning spiking variability

The firing rate is a prevalent concept used to describe neural computations, but estimating dynamically changing firing rates from irregular spikes is challenging. An inhomogeneous Poisson process, the standard model for partitioning firing rate and spiking irregularity, cannot account for diverse spike statistics observed across neurons. We introduce a doubly stochastic renewal point process, a flexible mathematical framework for partitioning spiking variability, which captures the broad spectrum of spiking irregularity from periodic to super-Poisson. We validate our partitioning framework using intracellular voltage recordings and develop a method for estimating spiking irregularity from data. We find that the spiking irregularity of cortical neurons decreases from sensory to association areas and is nearly constant for each neuron under many conditions but can also change across task epochs. A spiking network model shows that spiking irregularity depends on connectivity and can change with external input. These results help improve the precision of estimating firing rates on single trials and constrain mechanistic models of neural circuits.

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bioRxiv2023

Separating cognitive and motor processes in the behaving mouse

The cognitive processes supporting complex animal behavior are closely associated with ubiquitous movements responsible for our posture, facial expressions, ability to actively sample our sensory environments, and other critical processes. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes, making it challenging to dissociate the neural dynamics that support cognitive processes from those supporting related movements. In such cases, a critical issue is whether cognitive processes are separable from related movements, or if they are driven by common neural mechanisms. Here, we demonstrate how the separability of cognitive and motor processes can be assessed, and, when separable, how the neural dynamics associated with each component can be isolated. We establish a novel two-context behavioral task in mice that involves multiple cognitive processes and show that commonly observed dynamics taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories. Further, properly accounting for movement revealed that largely separate populations of cells encode cognitive and motor variables, in contrast to the mixed selectivity often reported. Accurately isolating the dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function and evaluating the function of the cell types of which neural circuits are composed.

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bioRxiv2023

The dynamics and geometry of choice in premotor cortex

The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code. Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations. Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a common geometric principle for neural encoding of sensory and dynamic cognitive variables.

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bioRxiv2023

A cortical information bottleneck during decision-making

Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) and form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism for how cortex may form these optimal representations, we trained a multi-area recurrent neural network (RNN) to perform the task. Remarkably, DLPFC and PMd resembling representations emerged in the early and late areas of the multi-area RNN, respectively. The DLPFC-resembling area partially orthogonalized choice information and task inputs and this choice information was preferentially propagated to downstream areas through selective alignment with inter-area connections, while remaining task information was not. Our results suggest that cortex uses multi-area computation to form minimal sufficient representations by preferential propagation of relevant information between areas. SignificanceThe brain uses multiple areas for cognition, decision-making, and action, but it is unclear why cortical activity differs by brain area. Machine learning and information theory suggests that one benefit of multiple areas is that it provides an "information bottleneck" that compresses inputs into an optimal representation that is minimal and sufficient to solve the task. Combining experimental recordings from behaving animals and computational simulations, we show that later brain areas have a tendency to form such minimal sufficient representations of task inputs through preferential propagation of task-relevant information present in earlier areas. Our results thus provide insight into one possible reason why the brain uses multiple brain areas for supporting decision-making and action.

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bioRxiv2022

Neural population dynamics in dorsal premotor cortex underlying a reach decision

We investigated if a dynamical systems approach could help understand the link between decision-related neural activity and decision-making behavior, a fundamentally unresolved problem. The dynamical systems approach posits that neural dynamics can be parameterized by a state equation that has different initial conditions and evolves in time by combining at each time step, recurrent dynamics and inputs. For decisions, the two key predictions of the dynamical systems approach are that 1) initial conditions substantially predict subsequent dynamics and behavior and 2) inputs should combine with initial conditions to lead to different choice-related dynamics. We tested these predictions by investigating neural population dynamics in the dorsal premotor cortex (PMd) of monkeys performing a red-green reaction time (RT) checkerboard discrimination task where we varied the sensory evidence (i.e., the inputs). Prestimulus neural state, a proxy for the initial condition, predicted poststimulus neural trajectories and showed organized covariation with RT. Furthermore, faster RTs were associated with faster pre- and poststimulus dynamics as compared to slower RTs, with these effects observed within a stimulus difficulty. Poststimulus dynamics depended on both the sensory evidence and initial condition, with easier stimuli and "fast" initial conditions leading to the fastest choice-related dynamics whereas harder stimuli and "slow" initial conditions led to the slowest dynamics. Finally, changes in initial condition were related to the outcome of the previous trial, with slower pre- and poststimulus population dynamics and RTs on trials following an error as compared to trials following a correct response. Together these results suggest that decision-related activity in PMd is well described by a dynamical system where inputs combine with initial conditions that covary with eventual RT and previous outcome, to induce decision-related dynamics.

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bioRxiv2021

Non-linear Dimensionality Reduction on Extracellular Waveforms Reveals Physiological, Functional, and Laminar Diversity in Premotor Cortex

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits. SignificanceHow different cell types sculpt activity patterns in brain areas associated with decision-making is a fundamentally unresolved problem in neuroscience. In monkeys, and other species where transgenic access is not yet possible, identifying physiological types in vivo relies on only a few discrete user-specified features of extracellular waveforms to identify cell types. Here, we show that non-linear dimensionality reduction with graph clustering applied to the entire extracellular waveform can delineate many different putative cell types and does so in an interpretable manner. We show that this method reveals previously undocumented physiological, functional, and laminar diversity in the dorsal premotor cortex of monkeys, a key brain area implicated in decision-making.

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bioRxiv2019

Recurrent neural network models of multi-area computation underlying decision-making

Cognition emerges from coordinated computations across multiple brain areas. However, elucidating these computations within and across brain regions is challenging because intra- and inter-area connectivity are typically unknown. To study coordinated computation, we trained multi-area recurrent neural networks (RNNs) to discriminate the dominant color of a checker-board and output decision variables reflecting a direction decision, a task previously used to investigate decision-related dynamics in dorsal premotor cortex (PMd) of monkeys. We found that multi-area RNNs, trained with neurophysiological connectivity constraints and Dales law, recapitulated decision-related dynamics observed in PMd. The RNN solved this task by a dynamical mechanism where the direction decision was computed and outputted, via precisely oriented dynamics, on an axis that was nearly orthogonal to checkerboard color inputs. This orthogonal direction information was preferentially propagated through alignment with inter-area connections; in contrast, color information was filtered. These results suggest that cortex uses modular computation to generate minimal sufficient representations of task information. Finally, we used multi-area RNNs to produce experimentally testable hypotheses for computations that occur within and across multiple brain areas, enabling new insights into distributed computation in neural systems.

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bioRxiv2019

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment. The process of deliberation on the evidence can be described by a time-varying decision variable (DV), decoded from neural activity, that predicts a subjects decision at the end of a trial. However, within trials, large moment-to-moment fluctuations of the DV are observed. The behavioral significance of these fluctuations and their role in the decision process remain unclear. Here we show that within-trial DV fluctuations decoded in real time from motor cortex are tightly linked to choice behavior, and that robust changes in DV sign have the statistical regularities expected from behavioral studies of changes-of-mind. Furthermore, we find single-trial evidence for absorbing decision bounds. As the DV builds up, heavily favoring one or the other choice, moment-to-moment variability in the DV is reduced, and both neural DV and behavioral decisions become resistant to additional pulses of sensory evidence as predicted by diffusion-to-bound and attractor models of the decision process.

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bioRxiv2019

CHaRTr: An R toolbox for modeling Choices and Response Times in decision-making tasks

Decision-making is the process of choosing and performing actions in response to sensory cues so as to achieve behavioral goals. A sophisticated research effort has led to the development of many mathematical models to describe the response time (RT) distributions and choice behavior of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques. Although some of these problems have been surmounted in existing software packages, the packages have often focused on the classical decision-making model, the diffusion decision model. Recent theoretical advances in decision-making that posit roles for \"urgency\", time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence, have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluations among these competing classes of decision-making models. Here, we present a toolbox -- Choices and Response Times in R, or CHaRTr -- that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries. Earlier versions of CHaRTr have been instrumental in a number of recent studies of humans and monkeys performing perceptual decision-making tasks. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

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bioRxiv2018

Brittleness in model selection analysis of single neuron firing rates

Models of complex heterogeneous systems like the brain are inescapably incomplete, and thus always falsified with enough data. As neural data grow in volume and complexity, absolute measures of adequacy are being replaced by model selection methods that rank the relative accuracy of competing theories. Selection still depends on incomplete mathematical instantiations, but the implicit expectation is that ranking is robust to their details. Here we highlight a contrary finding of \"brittleness,\" where data matching one theory conceptually are ranked closer to an instance of another. In particular, selection between recent models of decision making is conceptually misleading when data are simulated with minor distributional mismatch, with mixed secondary signals, or with non-stationary parameters; and decision-related responses in macaque cortex show features suggesting that these effects may impact empirical results. We conclude with recommendations to mitigate such brittleness when using model selection to study neural signals.

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bioRxiv2018

Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known

How deliberation on sensory cues and action selection interact in decision-related brain areas is still not well understood. Here, monkeys reached to one of two targets, whose colors alternated randomly between trials, by discriminating the dominant color of a checkerboard cue composed of different numbers of squares of the two target colors in different trials. In a \"Targets First\" task the colored targets appeared first, followed by the checkerboard; in a \"Checkerboard First\" task, this order was reversed. After both cues appeared in both tasks, responses of dorsal premotor cortex (PMd) neurons covaried with action choices, strength of evidence for action choices, and RTs--- hallmarks of decision-related activity. However, very few neurons were modulated by checkerboard color composition or the color of the chosen target, even in the Checkerboard First task. These findings implicate PMd in the action-selection but not the perceptual components of the decision-making process in these tasks.

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bioRxiv2018

Frequency shifts and depth dependence of premotor beta band activity during perceptual decision-making

Neural activity in the premotor and motor cortex shows prominent structure in the beta frequency range (13-30 Hz). Currently, the behavioral relevance of beta band activity (BBA) in premotor and motor regions is not well understood. The underlying source of motor BBA and how it changes as a function of cortical depth is also unknown. Here, we addressed these unresolved questions by investigating BBA recorded using laminar electrodes in the dorsal premotor cortex (PMd) of two male rhesus macaques performing a visual reaction time (RT) reach discrimination task. We observed robust BBA before and after the onset of the visual stimulus but not during the arm movement. While post-stimulus BBA was positively correlated with RT throughout the beta frequency range, pre-stimulus correlation varied by frequency. Low beta frequencies (~15 to 20 Hz) were positively correlated with RT and high beta frequencies (~25 to 30 Hz) were negatively correlated with RT. Simulations suggested that these frequency-dependent correlations could be due to a shift in the component frequencies of the pre-stimulus BBA as a function of RT, such that faster RTs are accompanied by greater power in high beta frequencies. We also observed a laminar dependence of BBA, with deeper electrodes demonstrating stronger power in low beta frequencies both pre- and post-stimulus. The heterogeneous nature of BBA and the changing relationship between BBA and RT in different task epochs may be a sign of the differential network dynamics involved in expectation, decision-making, and motor preparation.

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