Biology subjects

Buschman, T. J.

Publications and source records attributed to Buschman, T. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Internal state dynamically gates task-specific attractor dynamics in prefrontal cortex

Internal states such as motivation and task engagement influence cognitive functions. Working memory, which maintains information over time, is an essential component of cognition and is modulated by motivation. Here, we show motivational states modulated attractor dynamics that supported working memory. Combining population recordings from mouse medial prefrontal cortex (mPFC) with data-constrained recurrent neural network (RNN) modeling, we found task engagement selectively modulated attractor dynamics within a memory-maintenance subspace, while stimulus-evoked responses remained intact. Reverse-engineering the RNNs revealed that task engagement reorganized the dynamical landscape by stabilizing memory-specific attractors. Specifically, task engagement modulated interactions between neurons to change the attractor dynamics. Finally, gradual changes in behavioral engagement were predicted by continuous modulation of attractor geometry in RNNs and mPFC. Together, these results suggest that internal state modulate working memory function by controlling the dynamical regime of mPFC circuits, providing a mechanistic link between internal state, neural dynamics, and cognitive function.

neuroscience↗

bioRxiv2026

Protracted functional and structural reorganization of human prefrontal cortex supports lateralized category geometries

Despite being visually responsive, how prefrontal cortex represents visual categories across development and how this compares to posterior visual cortex remains unknown. Here, we show that ventrolateral prefrontal cortex (VLPFC) follows a fundamentally distinct developmental trajectory. Using functional and quantitative MRI in children and adults, we demonstrate that VLPFC exhibits reliable and structured category-selective responses in childhood that undergo prolonged reorganization into mature, hemisphere-specific representational geometries. Right VLPFC progressively organizes categories along an animate-inanimate axis, while left VLPFC consolidates a word-selective organization, a lateralization that strengthens across development. This restructuring contrasts sharply with ventral temporal cortex (VTC), which exhibits bilaterally organized, adult-like category structure in childhood. Critically, prefrontal representational structure emerges in alignment with conserved folding patterns of the precentral and inferior frontal sulci, suggesting that cortical anatomy provides a stable scaffold for the maturation of abstract representations. Quantitative MRI further reveals a non-linear trajectory of tissue development in right VLPFC, marked by adolescent reductions in macromolecular content and increased microstructural heterogeneity in adulthood, a pattern not observed in VTC. Together, these findings demonstrate that VLPFC undergoes prolonged, anatomically constrained refinement of category-related geometry characterized by hemisphere-specific taxonomies, rather than simply inheriting representational structure from sensory cortex. O_LIUnlike VTC (which shows early, bilateral, and stable category organization), VLPFC contains reliable, category-selective representations from childhood that undergo prolonged, hemisphere-specific developmental refinement rather than simple amplification C_LIO_LIRight VLPFC progressively consolidates an animate-inanimate representational axis across development, while left VLPFC establishes an early, stable word-selective organization consistent with language network maturation C_LIO_LIFunctional boundaries in VLPFC align with conserved intrasulcal folding motifs (plis de passage), demonstrating that anatomical scaffolding of category-selective responses extends beyond sensory cortex to prefrontal association cortex C_LIO_LIRight VLPFC undergoes adolescent pruning of cortical tissue microstructure diverging sharply from the continuous tissue proliferation observed in ventral temporal cortex C_LI

neuroscience↗

bioRxiv2026

Disrupted cortex-wide dynamics impair motor planning in Shank3-mutant mice

Coordinated cortical activity underlies voluntary movement planning, yet its disruption is implicated in neurodevelopmental disorders like autism spectrum disorder (ASD). Mutations in the SHANK3 gene, a key ASD risk factor, affect synaptic integrity and cortical network function, but their impact on preparatory cortical dynamics remains unclear. Here, we employed wide-field calcium imaging combined with motion tracking and computational motif analysis to examine cortex-wide activity in Shank3b+/- mice during spontaneous forepaw movements. We found reduced correlation between cortical activity and behavior preceding movement onset, alongside decreased expression of specific cortical motifs involving associative and motor areas. These changes contributed to global functional hyperconnectivity and impaired motor planning. Our findings reveal that disrupted spatiotemporal cortical dynamics in Shank3b+/- mice impair preparatory motor processes, providing insight into neural mechanisms underlying ASD-related motor deficits.

neuroscience↗

bioRxiv2025

Reusable modular architecture enables flexible cognitive operations

Complex behaviors are thought to be built by combining simpler cognitive components. Computational modeling has shown that artificial neural networks can perform a variety of tasks by flexibly combining functional modules, each specialized for a specific computation, to construct a complex task. However, it is unknown whether reusable modular networks are found in the brain. Here, we show that mice performing a delayed match-to-sample with delayed report (DMS-dr) task reuse neuronal subspaces that were specialized for stimulus processing and memory maintenance. These subspaces were reused during the task to represent new stimulus inputs and different types of memories, respectively. Clustering analyses showed each subspace was supported by a distinct cluster of neurons in prefrontal cortex and parietal cortex. Studying artificial recurrent networks constrained to neural data found silencing specific clusters disrupted specific computations, consistent with a modular and reusable organization. Altogether, our findings show that the brain can flexibly reuse computational components to perform a complex cognitive task.

animal behavior and cognition↗

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.

neuroscience↗

bioRxiv2024

Building compositional tasks with shared neural subspaces

Cognition is remarkably flexible; we are able to rapidly learn and perform many different tasks1. Theoretical modeling has shown artificial neural networks trained to perform multiple tasks will re-use representations2 and computational components3 across tasks. By composing tasks from these sub-components, an agent can flexibly switch between tasks and rapidly learn new tasks4. Yet, whether such compositionality is found in the brain is unknown. Here, we show the same subspaces of neural activity represent task-relevant information across multiple tasks, with each task compositionally combining these subspaces in a task-specific manner. We trained monkeys to switch between three compositionally related tasks. Neural recordings found task-relevant information about stimulus features and motor actions were represented in subspaces of neural activity that were shared across tasks. When monkeys performed a task, neural representations in the relevant shared sensory subspace were transformed to the relevant shared motor subspace. Subspaces were flexibly engaged as monkeys discovered the task in effect; their internal belief about the current task predicted the strength of representations in task-relevant subspaces. In sum, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations across tasks.

neuroscience↗

bioRxiv2023

The neural basis of swap errors in working memory

When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once - such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms through trial-by-trial analysis of neural population recordings from posterior and frontal brain regions while monkeys perform two multi-stimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location (requiring selection from working memory) or will be shown at the corresponding location (requiring attention to a position). Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected incorrectly from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results suggest an alternative to established views on the neural origins of swap errors, and highlight selection from and manipulation in working memory as crucial - yet surprisingly brittle - neural processes.

neuroscience↗

bioRxiv2023

Learning attentional templates for value-based decision-making

Attention filters sensory inputs to enhance task-relevant information. It is guided by an attentional template that represents the stimulus features that are relevant for the current task. To understand how the brain learns and uses new templates, we trained monkeys to perform a visual search task that required them to repeatedly learn new attentional templates. Neural recordings found templates were represented across prefrontal and parietal cortex in a structured manner, such that perceptually neighboring templates had similar neural representations. When the task changed, a new attentional template was learned by incrementally shifting the template towards rewarded features. Finally, we found attentional templates transformed stimulus features into a common value representation that allowed the same decision-making mechanisms to deploy attention, regardless of the identity of the template. Altogether, our results provide new insight into the neural mechanisms by which the brain learns to control attention and how attention can be flexibly deployed across tasks.

neuroscience↗

bioRxiv2023

Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks

Cognition is flexible. Behaviors can change on a moment-by-moment basis. Such flexibility is thought to rely on the brains ability to route information through different networks of brain regions in order to support different cognitive computations. However, the mechanisms that determine which network of brain regions is engaged are unknown. To address this, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice. Different dimensions within the population activity of each brain region were functionally connected with different cortex-wide subspace networks of regions. These subspace networks were multiplexed, allowing a brain region to simultaneously interact with multiple independent, yet overlapping, networks. Alignment of neural activity within a region to a specific subspace network dimension predicted how neural activity propagated between regions. Thus, changing the geometry of the neural representation within a brain region could be a mechanism to selectively engage different brain-wide networks to support cognitive flexibility.

neuroscience↗

bioRxiv2022

Reduced variability of bursting activity during working memory

Working memories have long been thought to be maintained by persistent spiking. However, mounting evidence from multiple-electrode recording (and single-trial analyses) shows that the underlying spiking is better characterized by intermittent bursts of activity. A counterargument suggested this intermittent activity is at odds with observations that spike-time variability reduces during task performance. However, this counterargument rests on assumptions, such as randomness in the timing of the bursts, that may not be correct. Thus, we analyzed spiking and LFPs from the prefrontal cortex (PFC) of monkeys to determine if task-related reductions in variability can co-exist with intermittent spiking. We found that it does because both spiking and associated gamma bursts were task-modulated, not random. In fact, the task-related reduction in spike variability could be explained by a related reduction in gamma burst variability. Our results provide further support for the intermittent activity models of working memory as well as novel mechanistic insights into how spike variability is reduced during cognitive tasks.

neuroscience↗

bioRxiv2022

Interplay between rule learning and rule switching in a perceptual categorization task

To adapt to a changing world, we must be able to switch between rules already learned and, at other times, learn rules anew. Often we must do both at the same time, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but intertwined computations, namely fast inference and slower incremental learning. To this end, we studied how monkeys switched between three rules. Each rule was compositional, requiring the animal to discriminate one of two features of a stimulus and then respond with an associated eye movement along one of two different response axes. By modeling behavior we found the animals learned the axis of response using fast inference (rule switching) while continuously re-estimating the stimulus-response associations within an axis (rule learning). Our results shed light on the computational interactions between rule switching and rule learning, and make testable neural predictions for these interactions.

neuroscience↗

bioRxiv2022

Variability in sampling of cortex-wide neural dynamics explains individual differences in functional connectivity and behavioral phenotype

Individual differences in behavior are associated with changes in the correlation of neural activity between brain areas. Such differences in functional connectivity are thought to reflect individual differences in brain structure that alter the flow of neural activity between regions. Here, in contrast, we show that individual differences in functional connectivity and behavior can be explained by differences in how frequently an individual expresses distinct cortex-wide spatiotemporal patterns of neural activity. This suggests variability in sampling of cortex-wide neural dynamics may underlie individuals unique behavioral phenotypes.

neuroscience↗

bioRxiv2021

A Hot-Coal theory of Working Memory

Working memory (WM) allows us to selectively remember and flexibly control a limited amount of information. Earlier work has suggested WM control is achieved by interactions between bursts of beta and gamma oscillations. The emerging question is how beta and gamma bursting, reflecting coherent activity of hundreds of thousands of neurons, can underlie selective control of individual items held in WM? Here, we propose a principle for how such selective control might be achieved on the neural network level. It relies on spatial computing, which suggests that beta and gamma interactions cause item-specific activity to flow spatially across the network over the course of a task. This way, control-related information about, for instance, item order can be retrieved from the spatial activity independent of the detailed recurrent connectivity that gives rise to the item-specific activity itself. The spatial flow should in turn be reflected in low-dimensional activity shared by many neurons. We test predictions of the proposed spatial computing paradigm by analysing control-related as well as item-specific activity in local field potentials and neuronal spiking from prefrontal cortex of rhesus macaques performing four WM tasks. As predicted, we find that the low-dimensional activity has a spatial component from which we can read out control-related information. These spatial components were stable over multiple sessions and did not depend on the specific WM items being used. We hypothesize that spatial computing can facilitate generalization and zero-shot learning by utilizing spatial component as an additional information encoding dimension. This offers a new perspective on the functional role of low-dimensional activity that tends to dominate cortical activity.

neuroscience↗

bioRxiv2020

Selective control of working memory in prefrontal, parietal, and visual cortex

Cognitive control guides behavior by controlling what, where, and how information is represented in the brain. Previous work has shown parietal and prefrontal cortex direct attention, which controls the representation of external sensory stimuli1,2. However, the neural mechanisms controlling the selection of representations held in mind, in working memory, are unknown. To address this, we trained two monkeys to switch between two tasks, requiring them to either select an item from a set of items held in working memory or attend to one stimulus from a set of visual stimuli. Simultaneous neural recordings in prefrontal, parietal, and visual cortex found prefrontal cortex played a primary role in selecting an item from working memory, representing selection before parietal and visual cortex. Surprisingly, a common population representation in prefrontal cortex encoded selection of an item in working memory and attention to an external stimulus, suggesting prefrontal cortex may act as a domain-general controller. Selection acted on memory representations in two ways. First, selection improved the accuracy of memory reports by enhancing the selected items representation in prefrontal and parietal cortex. Second, selection transformed memory representations in a task-dependent manner. Before selection, when both items were relevant to the task, the identity of each item was represented in an independent subspace of neural activity. After selection, the representation of only the selected item was transformed into a new subspace that was used to guide the animals behavioral report. Together, our results provide insight into how prefrontal cortex controls working memory representations, selectively enhancing and transforming them to support behavior.

neuroscience↗

bioRxiv2020

Learning to Control the Brain through Adaptive Closed-Loop Patterned Stimulation

Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed an adaptive, closed-loop stimulation (ACLS) system that uses patterned, multi-site electrical stimulation to control the pattern of activity in a population of neurons. Importantly, ACLS is a learning system; it monitors the response to stimulation and iteratively updates the stimulation pattern to produce a specific neural response. In silico and in vivo experiments showed ACLS quickly learns to produce specific patterns of neural activity ([~]15 minutes) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. Altogether, our results show ACLS can learn, in real-time, to generate specific patterns of neural activity, providing a framework for using closed-loop learning to control neural activity.

neuroscience↗

bioRxiv2020

Delay-period activity in frontal, parietal, and occipital cortex tracks different attractor dynamics in visual working memory

One important neural hallmark of working memory is persistent elevated delay-period activity in frontal and parietal cortex. In human fMRI, delay-period BOLD activity in frontal and parietal cortex increases monotonically with memory load and asymptotes at an individuals capacity. Previous work has demonstrated that frontal and parietal delay-period activity correlates with the decline in behavioral memory precision observed with increasing memory load. However, because memory precision can be influenced by a variety of factors, it remains unclear what cognitive processes underlie persistent activity in frontal and parietal cortex. Recent psychophysical work has shown that attractor dynamics bias memory representations toward a few stable representations and reduce the effects of internal noise. From this perspective, imprecision in memory results from both drift towards stable attractor states and random diffusion. Here we asked whether delay-period BOLD activity in frontal and parietal cortex might be explained, in part, by these attractor dynamics. We analyzed data from an existing experiment in which subjects performed delayed recall for line orientation, at different loads, during fMRI scanning. We modeled subjects behavior using a discrete attractor model, and calculated within-subject correlation between frontal and parietal delay-period activity and estimated sources of memory error (drift and diffusion). We found that although increases in frontal and parietal activity were associated with increases in both diffusion and drift, diffusion explained the most variance in frontal and parietal delay-period activity. In comparison, a subsequent whole-brain regression analysis showed that drift rather than diffusion explained the most variance in delay-period activity in lateral occipital cortex. These results provide a new interpretation for the function of frontal, parietal, and occipital delay-period activity in working memory.

neuroscience↗

bioRxiv2020

Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex Wide Neural Activity

Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity ([~]75%) could be explained by a limited set of [~]14 motifs of neural activity. Each motif captured a unique spatio-temporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic, but that these dynamics are restricted to a low-dimensional set of motifs, potentially to allow for efficient control of behavior.

neuroscience↗

bioRxiv2019

Drifting codes within a stable coding scheme for working memory

Working memory (WM) is important to maintain information over short time periods to provide some stability in a constantly changing environment. However, brain activity is inherently dynamic, raising a challenge for maintaining stable mental states. To investigate the relationship between WM stability and neural dynamics, we used electroencephalography to measure the neural response to impulse stimuli during a WM delay. Multivariate pattern analysis revealed representations were both stable and dynamic: there was a clear difference in neural states between time-specific impulse responses, reflecting dynamic changes, yet the coding scheme for memorized orientations was stable. This suggests that a stable subcomponent in WM enables stable maintenance within a dynamic system. A stable coding scheme simplifies readout for WM-guided behaviour, whereas the low-dimensional dynamic component could provide additional temporal information. Despite having a stable subspace, WM is clearly not perfect - memory performance still degrades over time. Indeed, we find that even within the stable coding scheme, memories drift during maintenance. When averaged across trials, such drift contributes to the width of the error distribution.

neuroscience↗