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Lee, E. K.

Publications and source records attributed to Lee, E. K..

13 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.

neuroscience↗
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.

neuroscience↗
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.

neuroscience↗
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.

neuroscience↗
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/.

neuroscience↗
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.

neuroscience↗
bioRxiv2021

A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex

Identification of the structural connections between neurons is a prerequisite to understanding brain function. We developed a pipeline to systematically map brain-wide monosynaptic inputs to specific neuronal populations using Cre-driver mouse lines and the recombinant rabies tracing system. We first improved the rabies virus tracing strategy to accurately identify starter cells and to efficiently quantify presynaptic inputs. We then mapped brain-wide presynaptic inputs to different excitatory and inhibitory neuron subclasses in the primary visual cortex and seven higher visual areas. Our results reveal quantitative target-, layer- and cell-class-specific differences in the retrograde connectomes, despite similar global input patterns to different neuronal populations in the same anatomical area. The retrograde connectivity we define is consistent with the presence of the ventral and dorsal visual information processing streams and reveals further subnetworks within the dorsal stream. The hierarchical organization of the entire visual cortex can be derived from intracortical feedforward and feedback pathways mediated by upper- and lower-layer input neurons, respectively. This study expands our knowledge of the brain-wide inputs regulating visual areas and demonstrates that our improved rabies virus tracing strategy can be used to scale up the effort in dissecting connectivity of genetically defined cell populations in the whole mouse brain.

neuroscience↗
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.

neuroscience↗
bioRxiv2020

A standardized behavioral event equally impacts the activity of cortical visual areas and layers

Multiple recent studies have shown that motor activity greatly impacts the activity of primary sensory areas like V1. Yet, the role of this motor related activity in sensory processing is still unclear. Here we dissect how these behavior signals are broadcast to different layers and areas of the visual cortex. To do so, we leveraged a standardized and spontaneous behavioral fidget event in passively viewing mice. Importantly, this behavior event had no relevance to any ongoing task allowing us to compare its neuronal correlate with visually relevant behavior like running. A large two-photon Ca2+ imaging database of neuronal responses uncovered four neural response types during fidgets that were surprisingly consistent in their proportion and response patterns across all visual areas and layers of the visual cortex. Indeed, the layer and area identity could not be decoded above chance level based only on neuronal recordings. In contrast to running behavior, fidget evoked neural responses were independent to visual processing. The broad availability of visually orthogonal standardized behavior signals could be a key component in how the cortex selects, learns and binds local sensory information with motor outputs. Contrary to relevant motor outputs, irrelevant motor signals would use a separate neural subspaces. Significance StatementRecent studies have shown contextual and behavioral variables to dominate brain-wide activity, but yet it is unknown how this information is broadcast across cortical layers and areas. Using a large two-photon dataset collected in mice passively viewing a battery of visual stimuli, we characterized the neuronal response of neurons of the visual cortex to a standardized fidget behavior. We found that as much as 47% of excitatory neurons show significant co-activity with fidgets. Throughout all areas and layers we recorded from, those responses were distributed across surprisingly consistent three neural response types. Further analysis showed no interaction between fidget neural events and cells visual stimulus responses, contrasting with feedback neural signals induced by running. These contrasting response distribution patterns suggest that behavioral neuronal correlates are broadly available but will modulate sensory responses depending on their relevance to local sensory inputs.

neuroscience↗
bioRxiv2019

VIP interneurons selectively enhance weak but behaviorally-relevant stimuli.

Vasoactive intestinal peptide-expressing (VIP) interneurons in cortex regulate feedback inhibition of pyramidal neurons through suppression of somatostatin-expressing (SST) interneurons and, reciprocally, SST neurons inhibit VIP neurons. Here, we show that VIP neurons in mouse primary visual cortex have complementary contrast tuning to SST neurons and respond synergistically to front-to-back visual motion and locomotion. Network modeling indicates that this VIP-SST mutual antagonism regulates the gain of cortex to achieve both sensitivity to behaviorally-relevant stimuli and network stability.

neuroscience↗
bioRxiv2017

optSelect: using agent-based modeling and binary PSO techniques for ensemble feature selection and stability assessment

MotivationRecent studies have shown that the ensemble feature selection approaches are essential for generating robust classifiers. Existing methods for aggregating feature lists from different methods require use of arbitrary thresholds for selecting the top ranked features and do not account for classification accuracy while selecting the optimal set. Here we present a two-stage ensemble feature selection framework for finding the optimal set of features without compromising on classification accuracy.\n\nMethods and ResultsWe present herein optSelect, a multi agent-based stochastic optimization approach for nested ensemble feature selection. Stage one involves function perturbation, where ranked list of features are generated using different methods and stage two involves data perturbation, where feature selection is performed within randomly selected subsets of the training data and the optimal set of features is selected within each set using the optSelect. The agents are assigned to different behavior states and move according to a binary PSO algorithm. A multi-objective fitness function is used to evaluate the classification accuracy of the agents. We evaluate the system performance using the random probe method and using five publicly available microarray datasets. The performance of optSelect is compared with single feature selection techniques and existing aggregation methods. The results show that the optSelect algorithm improves the classification accuracy compared to both individual and existing rank aggregation methods. The algorithm is incorporated into an R package, optSelect.\n\nContactkuppal2@emory.edu

bioinformatics↗
bioRxiv2017

SEACOIN2.0: an interactive mining and visualization tool for information retrieval, summarization, and knowledge discovery

MotivationThe rapidly increasing size of biomedical databases such as MEDLINE requires the use of intelligent data mining methods for information extraction and summarization. Existing biomedical text-mining tools have limited capabilities for inferring topological and network relationships between biomedical terms. Very often too much is returned during summarization leading to information overload.\n\nResultsWe present herein SEACOIN 2.0, an interactive knowledge discovery and hypothesis generation tool for biomedical literature.SEACOIN generates k-ary relational networks of biomedical terms using a novel term ranking scheme to facilitate efficient information retrieval, summarization, and visual data exploration. Summarization is presented via multiple dynamic visualization panels. We evaluate the system performance in information retrieval and features extraction using the BioCreative 2013 Track 3 learning corpus. An average F-measure of 94% was achieved for document retrieval and an average precision of 88% was achieved for identification of top co-occurrence terms. The system allows interactive mining of complex implicit and explicit relationships among biomedical entities (genes, chemicals, diseases/disorders, mutations, etc.) and provides a framework for hypothesis generation. It also improves our understanding of various biological processes and disease mechanisms.\n\nContacteva.lee@gatech.edu

bioinformatics↗