Biology subjects

Miller, E. K.

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

At least 19 recordsLinked to original sources

bioRxiv2026

Disruption of interareal control during propofol anesthesia

Anesthetic-induced unconsciousness may arise partly from a change in how brain areas can manipulate each others activity. To quantify this change, we use control-theoretic tools that can precisely characterize how easily subsystems in complex networks can control each other. These tools rely on the Jacobian of the dynamics, an object that fully specifies how inputs to a function affect the outputs. We built on JacobianODE, a method for data-driven Jacobian learning, to enable its application to neural recordings. We developed a deep learning framework that recovers directional, nonlinear control from partially observed multi-area recordings by combining delay-coordinate embedding, a volume-preserving invertible encoder, and latent dynamics via JacobianODE. We validated our framework on the Lorenz system and a partially observed working-memory recurrent neural network. We then applied it to local field potential recordings from posterior parietal (PPC), superior temporal gyrus (STG), frontal eye fields (FEF), and ventrolateral prefrontal cortex (vlPFC) in two non-human primates, comparing wakefulness with propofol anesthesia. Anesthesia pervasively reduced the ability of areas to control each other, both in terms of driving towards novel states and stabilizing along existing trajectories. This change in ease of control was driven by a decrease in the magnitude of interareal coupling. Directional ease of driving control from PPC to vlPFC and FEF to vlPFC was increased under anesthesia, providing a potential mechanism for paradoxical excitation observed during propofol infusion. Together, these results recast anesthetic unconsciousness as a directional breakdown of cortical control.

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bioRxiv2026

Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia

Understanding how receptor-level pharmacological modulation reorganizes large-scale brain circuits remains a central challenge in neuropharmacology. We introduce a multiscale mechanistic model with explicit core-matrix thalamocortical architecture, driven solely by GABA-A modulation without parameter fitting to any anesthesia data, to examine how propofol reorganizes brainwide activity from individual receptors to systems-level circuits. The model exhibits anesthetic effects spanning individual synaptic conductances to widespread changes in spiking, field potentials, and coherence. Without training on any task-specific data, our simulation of sensory processing in a standard auditory oddball paradigm matches independent macaque datasets. The same simulation, unmodified, also reproduces changes to functional connectivity in anesthetized humans, exhibiting selective attenuation of matrix thalamocortical loops relative to core loops. Most importantly, the simulation identified a dose-dependent biomarker of propofol concentration -- elevated residual inter-stimulus cortical activity -- that was subsequently confirmed in empirical macaque data where it had previously gone unnoticed. This simulation-first discovery, arising from mechanistic circuit dynamics rather than statistical comparison of clinical populations, illustrates a generative framework for translating receptor-level modulation into circuit-scale biomarkers with potential applications across predictive neuropharmacology.

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bioRxiv2026

Low-dimensional prefrontal representations of objects during working memory

There is an ongoing debate regarding the dimensionality of neural representations. Some accounts emphasize representation within low-dimensional subspaces or manifolds, while others suggest high-dimensional neural codes where neurons respond independently. Here, we investigate the dimensionality of prefrontal cortex (PFC) representations of visual objects held in working memory. We found that object representations are low-dimensional, occupying only 3-6 effective dimensions during both encoding and maintenance in working memory. Control analyses indicate this dimensionality was not limited by the number of objects tested (40) or neurons sampled ([~]100). We also compared PFC dimensionality to that of a well-established deep neural network model of its inferotemporal (IT) inputs and found an approximately 7-fold dimensionality reduction in PFC. These results suggest object representations are compressed into a low-dimensional manifold in PFC, which might be related to attractor dynamics for working memory, and might facilitate interaction with other variables and cognitive control. Author summaryCompeting accounts of cortical neural coding propose that neurons either activate in structured, coordinated patterns like schools of fish, or that they activate independently, like hunting sharks. A key metric discriminating these schemes is their effective dimensionality -- the number of independent activity patterns needed to characterize information conveyed in a neural population. To test this, we measured the dimensionality of primate prefrontal cortex during working memory for objects, a domain where the inputs are intrinsically complex and high-dimensional. We found that prefrontal dimensionality was surprisingly low, spanning only a handful of dimensions. Using convolutional neural network models of its visual cortex inputs, we found evidence for a substantial prefrontal compression of dimensionality. We hypothesize that this reformatting of high-dimensional sensory information into a compact code can facilitate flexible combination with information from other domains, as proposed by "mixed selectivity" theories of prefrontal coding.

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bioRxiv2026

Theta gates and routes information in the frontal cortex

Theta (4-10 Hz) oscillations seem well-suited for coordinating neural activity. Many studies have focused on thetas role in long-range coordination across brain regions (e.g., connectivity between the prefrontal cortex and the hippocampus). It remains unclear how theta coordinates neural activity more locally within prefrontal subareas. We examined neural activity in three frontal areas (i.e., dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), and frontal eye fields (FEF)) as non-human primates categorized dot patterns. We found that theta flexibly coordinated spiking activity and higher-frequency oscillations within and between frontal areas. First, theta phase in all areas was coupled to spiking information in the FEF and seemed to gate information that was behaviorally relevant. Second, theta influences were routed in opposite directions depending on feedback. Theta flowed in a posterior direction to the FEF during choices and after correct outcomes. Theta influences reversed directionality and flowed in an anterior direction after incorrect outcomes. Third, theta organized nested cross-frequency, phase-amplitude interactions. Theta was coupled to beta (15-30Hz) oscillations, both within and between areas. Beta, in turn, was coupled to gamma (40-90Hz) oscillations, but mainly locally. Together, our results position theta as a critical mechanism that flexibly and dynamically coordinates neural activity within and across the frontal cortex. HIGHLIGHTS- Neural activity was recorded in three frontal areas as non-human primates categorized dot patterns. - Theta (4-10Hz) oscillations gated relevant information in spiking. - Theta influences were routed in opposite directions depending on feedback. - Theta organized nested cross-frequency interactions with beta (15-30Hz) and gamma (40-90Hz) oscillations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/729810v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1b4a6a4org.highwire.dtl.DTLVardef@12886feorg.highwire.dtl.DTLVardef@1b6c08dorg.highwire.dtl.DTLVardef@11ba768_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2026

A biophysical corticostriatal model predicts learning-dependent 7T fMRI dynamics and individual reward bias in humans

Circuit-level computational models can do more than explain existing data; they can generate novel hypotheses and capture individual differences in human populations. We demonstrate this using a biophysical corticostriatal model, transforming simulated neuronal activity from local field potential (LFP) into functional magnetic resonance imaging (fMRI) signals via the balloon model, and finally generating behavioral outcomes. We then validate the results against those obtained from human subjects. The model generates a counterintuitive yet testable prediction. Although prefrontal-striatal coherence demonstrates an increase at the neural LFP scale during learning, the same circuit-- passed through the hemodynamic transform---predicts a decrease in prefrontal-striatal BOLD correlation, driven by category-selective representations that emerge in cortex but not striatum. This prediction is confirmed in fMRI data optimized for single-subject-level detection sensitivity. The model further enables single-subject fitting of a behavioral outcome, classifying individuals into those differentially responding to positive versus negative reward bias. Established biomarkers, including activation measured by the amplitude of low-frequency fluctuations and dopaminergic effects on hemodynamic latency, are also conserved across LFP and fMRI scales. These findings reposition circuit models as generative tools for human neuroscience, capable of producing mechanistically grounded hypotheses and parsing individual variation in ways inaccessible to data-driven approaches alone.

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bioRxiv2026

Evidence for predictive computations in a brain hierarchy during a visual search task

Many lines of evidence suggest that the cortex functions fundamentally as a predictive system, compressing high-dimensional sensory inputs into low-dimensional representations that support internal models of the environment. These models generate top-down signals that shape and filter incoming sensory information, prioritizing unpredicted, and thus more informative, inputs for further processing. Multiple, mutually compatible computational frameworks have been proposed to explain these mechanisms, including predictive coding, predictive routing, and autoencoder-based approaches. However, a key challenge remains in empirically disentangling their relative contributions. Here, we directly compared three candidate algorithms, predictive coding, routing and autoencoders using LFP data from a brain hierarchy acquired during a visual search task. Notably, predictive coding differs from alternative models in its capacity to dynamically optimize signal passing across brain hierarchies in an input-specific manner, while the other algorithms do not consider constraints on feedback or feedforward inputs. It also goes beyond pairwise interactions, by predicting information flow in triplets of brain areas; here, V4, 7A and PFC. We considered the characteristic patterns of neural dynamics and within-area coupling that distinct algorithms produce. These were subsequently used to assess the relative evidence of each algorithm in the face of the LFP data. Our results support a hybrid account: hierarchical message passing consistent with predictive coding appears necessary to explain deep-layer activity, while predictive suppression mechanisms, aligned with predictive routing, account for superficial-layer dynamics without requiring explicit error computations.

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bioRxiv2026

Neural subspace reorganization reflects value-based decision making

Value-based decision making requires maintaining and comparing multiple option representations associated with different values. Then, the chosen option must be communicated to downstream regions to drive behavior. The neural circuits involved in value-based decisions are increasingly well understood, but less understood is how decisions shape option representations. We recorded from lateral prefrontal cortex (LPFC), a central hub for transforming options into actions, while non-human primates (NHPs) held two sequentially presented option-value pairs (spatial targets and abstract reward cues) in working memory, then chose the option with the higher value. This revealed how decisions dynamically reorganize option representations in LPFC. We found that, once decisions could be made, representations of chosen and unchosen options rotated into orthogonal subspaces and the chosen option representation was expanded. Before decisions, the first- and second-presented options were maintained separately. After decisions, the chosen option was rotated into a subspace with a consistent representation of the prescribed action, regardless of its presentation order. This suggests a mechanism for value-based decisions where the decision drives a neural subspace reorganization that facilitates selective and efficient readout of the chosen action.

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bioRxiv2026

Emergent Traveling Waves in Neural Circuits

Artificial neural networks achieve striking performance but do not capture a prominent neural property. Cortical activity is organized by structured spatiotemporal dynamics, traveling waves (TW), which have been implicated in a wide range of functions. Existing computational models often rely on hand-crafted connectivity and imposed dynamics, offering insight into their impact but less into how the waves naturally emerge in biological circuits. Here, we found that TWs emerge in models under biologically plausible constraints (spatially organized, directionally biased connectivity). Under an empirical neural manifold constraint, these wiring principles naturally emerge to support traveling wave dynamics in the recurrent model. We further show that wave propagation provides a robust mechanism for maintaining working memory in the presence of visual distractors. We compared these model predictions to non-human primate prefrontal cortex recordings, revealing a similar mechanism. Together, these results advance our understanding of traveling waves as a substrate for cognition and offer a framework for mechanistic accounts of cortical computation.

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bioRxiv2025

Similar destabilization of neural dynamics under different general anesthetics

Different classes of anesthetics can induce unconsciousness despite acting through distinct biological mechanisms. This raises the possibility that they produce a convergent effect on the dynamics or temporal evolution of neural population activity. To explore this, we analyzed intracortical electrophysiological recordings during infusions of propofol, ketamine, and dexmedetomidine, using a rigorous method to estimate dynamical stability. We found that all three anesthetics, despite their molecular differences, similarly affect cortical states by destabilizing their dynamics. This destabilization matched the slower recovery from sensory perturbations and longer stimulus-induced autocorrelation times observed during the anesthetic infusions. The destabilization was also reflected predominantly in lower-frequency ranges, linking it to the well-known increase in low-frequency power during anesthesia. Finally, destabilization closely tracked real-time fluctuations in consciousness. Together, these findings suggest that cortical destabilization may be a shared neural correlate of anesthetic-induced unconsciousness, offering a mechanistic explanation for low-frequency oscillations observed during anesthesia.

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bioRxiv2025

Oscillatory Control of Cortical Space as a Computational Dimension

Flexible cognition depends on the ability to represent and apply relevant information to the current task at hand. This allows the brain to interpret sensory input and guide behavior in a context-dependent manner. Recent work has proposed Spatial Computing as a mechanism for this flexibility, suggesting that task-related signals organize information processing through spatial patterns of oscillatory activity across the cortical surface. These patterns are proposed to act as "inhibitory stencils" that constrain where sensory-related information (the "content" of cognition) can be expressed in spiking activity. Here, we provide a comprehensive empirical test of Spatial Computing using multi-electrode recordings from the lateral prefrontal cortex in non-human primates performing a range of cognitive tasks (object working memory, sequence working memory, categorization). We found that alpha/beta oscillations encoded task-related information, were organized into spatial patterns that changed with task conditions, and inversely correlated with the spatial expression of sensory-related spiking activity. Furthermore, we found that alpha/beta oscillations reflected misattributions of task conditions and correlated with subjects trial-by-trial decisions. These findings validate core predictions of Spatial Computing suggesting that oscillatory dynamics not only gate information in time but also shape where in the cortex cognitive content is represented. This framework offers a unifying principle for understanding how the brain flexibly coordinates cognition through structured population dynamics.

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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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bioRxiv2025

Recycling of prefrontal subspaces dynamically multiplexes information

Cortical spiking activity in areas like prefrontal cortex (PFC) encodes information only along a small number of dimensions, a subspace of the full space of population activity patterns. PFC often uses distinct subspaces to represent incoming sensory information and to maintain it in working memory. However, its generally assumed that a subspace carries specific information within a specific time frame. Here, in two datasets, we show that neither assumption is correct. The same subspace used during initial encoding was later reactivated during the behavioral response. We also found that subspaces ostensibly dedicated to processing sensory information were recycled to represent behavioral response information later in a trial. We further found a consistent spatial mapping between the original subspace usage and its later recycling. These results demonstrate previously unknown flexibility of PFC subspace coding, both in the deployment of subspaces across time and in their multiplexing of information. This flexibility suggests that PFC subspaces function more like "workspaces" into which different types of information can be written.

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bioRxiv2025

Evidence for an active handoff between cerebral hemispheres during target tracking

The brain has somewhat separate cognitive resources for the left and right sides of our visual field. Despite this lateralization, we have a smooth and unified perception of our environment. This raises the question of how the cerebral hemispheres are coordinated to transfer information between them. We recorded neural activity in the lateral prefrontal cortex, bilaterally, as non-human primates covertly tracked a target that moved from one visual hemifield (i.e., from one hemisphere) to the other. Beta (15 to 30 Hz) power, gamma (30 to 80 Hz) power, and spiking information reflected sensory processing of the target. By contrast, alpha (10 to15 Hz) power, theta (4 to10 Hz) power, and spiking information seemed to reflect an active handoff of attention as target information was transferred between hemispheres. Specifically, alpha power and spiking information ramped up in anticipation of the hemifield cross. Theta power peaked after the cross, signaling its completion. Our results support an active hand-off of information between hemispheres. This handshaking operation may be critical for minimizing information loss, much like how mobile towers handshake when transferring calls between them.

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bioRxiv2025

Working memory readout varies with frontal theta rhythms

Increasing evidence suggests that attention varies rhythmically, phase-locked to ongoing cortical oscillations. Here, we report that the phase of theta oscillations (3-6 Hz) in the frontal eye field (FEF) is associated with temporal and spatial variation of the read-out of information from working memory (WM). Non-human primates were briefly shown a sample array of colored squares. A short time later, they viewed a test array and were rewarded for identifying which square changed color (the target). Better performance (accuracy and reaction time) varied systematically with the phase of local field potential (LFP) theta at the time of test array onset as well as the targets location. This is consistent with theta "scanning" across the FEF and thus visual space from top to bottom. Theta was coupled, on opposing phases, to both spiking and beta (12-20 Hz). These results could be explained by a wave of activity that moves across the FEF, modulating the readout of information from WM.

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bioRxiv2025

Quantifying Differences in Neural Population Activity With Shape Metrics

Quantifying differences across species and individuals is fundamental to many fields of biology. However, it remains challenging to draw detailed functional comparisons between large populations of interacting neurons. Here, we introduce a general framework for comparing neural population activity in terms of shape distances. This approach defines similarity in terms of explicit geometric transformations, which can be flexibly specified to obtain different measures of population-level neural similarity. Moreover, differences between systems are defined by a distance that is symmetric and satisfies the triangle inequality, enabling downstream analyses such as clustering and nearest-neighbor regression. We demonstrate this approach on datasets spanning multiple behavioral tasks (navigation, passive viewing of images, and decision making) and species (mice and non-human primates), highlighting its potential to measure functional variability across subjects and brain regions, as well as its ability to relate neural geometry to animal behavior.

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bioRxiv2024

Anterior cingulate neurons display subregion-specific interaction with frontal eye fields as revealed by combined antidromic stimulation and resting state imaging

The anterior cingulate cortex (ACC) is thought to exert cognitive control over saccade generation in the frontal eye fields (FEF), but the nature of this interaction remains unclear. Although prior imaging studies have suggested ACC interacts with FEF, few studies have confirmed this by electrophysiological recordings. This study aimed to characterize the functional connectivity between ACC and medial and lateral FEF during cognitive saccade tasks. We combined resting-state functional MRI (rs-fMRI) with single-unit electrophysiology in two macaque monkeys performing memory-guided saccade and pro-/anti-saccade tasks. Anti- and ortho-dromic stimulation was used to electrophysiologically identify ACC neurons mono- and polysynaptically connected to FEF. We analyzed ACC neuronal selectivity for different task aspects and correlated these properties with both positive and negative rs-fMRI functional connectivity between ACC and FEF subregions. Anti- and ortho-dromically identified ACC neurons were predominantly connected to medial FEF, which showed stronger positive functional connectivity with ACC compared to lateral FEF. Sites with higher proportions of task-selective neurons yielded stronger functional connectivity with FEF. This stronger functional connectivity was particularly related to the delay and saccadic periods of different cognitive saccade tasks. Using combined imaging and electrophysiology, our findings provide converging evidence for functional interactions between ACC and FEF, predominantly medial FEF regions which encode large amplitude saccades. The correlation between functional connectivity and task-related neuronal selectivity supports ACCs interaction with FEF in the modulation of saccade generation and cognitive control. Additionally, we report suggestive evidence that mono- and poly-synaptic connections may be related to positive functional connectivity, but we found no such relationship for negative functional connectivity (anticorrelations). These results advance our understanding of prefrontal cortical interactions in oculomotor control and the electrophysiological mechanisms of positive and negative resting-state functional connectivity.

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bioRxiv2024

Ketamine can produce oscillatory dynamics by engaging mechanisms dependent on the kinetics of NMDA receptors

Ketamine is an NMDA-receptor antagonist that produces sedation, analgesia and dissociation at low doses and profound unconsciousness with antinociception at high doses. At high and low doses, ketamine can generate gamma oscillations (>25 Hz) in the electroencephalogram (EEG). The gamma oscillations are interrupted by slow-delta oscillations (0.1-4 Hz) at high doses. Ketamines primary molecular targets and its oscillatory dynamics have been characterized. However, how the actions of ketamine at the subcellular level give rise to the oscillatory dynamics observed at the network level remains unknown. By developing a biophysical model of cortical circuits, we demonstrate how NMDA-receptor antagonism by ketamine can produce the oscillatory dynamics observed in human EEG recordings and non-human primate local field potential recordings. We have discovered how impaired NMDA-receptor kinetics can cause disinhibition in neuronal circuits and how a disinhibited interaction between NMDA-receptor-mediated excitation and GABA-receptor-mediated inhibition can produce gamma oscillations at high and low doses, and slow-delta oscillations at high doses. Our work uncovers general mechanisms for generating oscillatory brain dynamics that differs from ones previously reported, and provides important insights into ketamines mechanisms of action as an anesthetic and as a therapy for treatment-resistant depression.

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bioRxiv2024

Decoding region-level visual functions from invasive EEG monkey data

Decoding vision is an ambitious task as it aims to transform scalar brain activity into dynamic images with refined shapes, colors and movements. In familiar environments, the brain may trigger activity that resembles specific pattern, thereby facilitating decoding. Can an artificial neural network (ANN) decipher such latent patterns? Here, we explore this question using invasive electroencephalography data from monkeys. By decoding multiregion brain activity, ANN effectively captures individual regions functional roles as a consequence of minimizing visual errors. For example, ANN recognizes that regions V4 and LIP are involved in visual color and shape processing while MT predominantly handles visual motion, aligning with regional visual functions evident in the brain. ANN likely reconstructs vision by seizing hidden spike patterns, representing stimuli distinctly in a two-dimensional plane. Furthermore, during the encoding process of transforming visual stimuli into neuronal activity, optimal performance is achieved in regions closely associated with vision processing.

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