Biology subjects

Rubin, J. E.

Publications and source records attributed to Rubin, J. E..

12 recordsLinked to original sources

bioRxiv2026

Learning reweights the decision dynamics of cortico-basal ganglia-thalamic pathways from deliberation to commitment

Mammals flexibly adjust their decision strategies in dynamic environments based on prior experience. The cortico-basal ganglia-thalamic (CBGT) circuit is recognized as a critical driver of this adaptability, yet how plasticity-induced modifications to CBGT dynamics translate into modifications of decision policies remains poorly understood. Here we simulate learning in a biologically-grounded spiking CBGT model that learns to select a rewarded action via dopamine-dependent plasticity at corticostriatal synapses. Relying on three previously identified control ensembles (responsiveness, pliancy, and choice) within CBGT circuits, we disentangle the distinct roles these subnetworks have in reshaping decision trajectories across learning. Control ensemble dynamics were mapped onto the evolution of evidence accumulation, revealing within-trial parameter adjustments that shape decision dynamics and outcomes. Our results emphasize that learning optimizes not only what choice is favored, but also how the phases within the decision unfold. Early in the decision, learning accelerates evidence accumulation by driving the activity of corticothalamic and direct pathways. During later deliberation, this drive is temporarily restrained, and the indirect and pallidostriatal pathways become more critical for maintaining decision thresholds, preventing premature commitment despite the increased choice bias. As the system approaches the decision point, the direct pathway regains dominance, triggering boundary collapse to facilitate action selection. This mechanism effectively shifts decisions from deliberative to committed reward-directed choices, improving both speed and accuracy while preserving system stability and control throughout the process. Author summaryWe investigate how specific subnetworks within cortico-basal ganglia-thalamic (CBGT) circuits reshape decision-making through dopamine-dependent plasticity at corticostriatal synapses. Using simulations of a simple two-choice task with a single rewarded target, we identify phase-specific reconfigurations of subnetwork activity that emerge over the course of learning. We then link these circuit-level changes to an algorithmic description of behavior by mapping them onto decision policies within a dynamic evidence accumulation framework. This analysis reveals a learning-driven shift from deliberative to more committed decision strategies, achieved through flexible coordination of decision boundaries and evidence accumulation rates. Together, these adjustments optimize decision speed and accuracy while preserving caution during ongoing choices. Our findings provide mechanistic insight into how plasticity within CBGT circuits supports adaptive decision-making across different levels of uncertainty and task demands.

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bioRxiv2025

Reduced TRPC3 conductance underlies altered SNr activity under dopamine depletion: predictions from data-driven network models

Sufficient loss of dopamine within the basal ganglia (BG) leads to neuronal activity changes, including altered firing rates and firing patterns, thought to underlie parkinsonian motor symptoms. Yet, within BG neuronal populations, baseline activity and responses to inputs are highly variable, complicating efforts to identify key factors associated with pathological changes. We introduce a novel approach to constructing a computational neuron population model that, when applied to the mouse substantia nigra pars reticulata (SNr), captures the firing heterogeneity observed across slice and in vivo recordings. This model reproduces the diversity of SNr neuron responses to stimulation of GABAergic input terminals, yielding new insights into the mechanisms underlying this variability. Moreover, our modeling pinpoints significant decreases in TRPC3 conductance in SNr dendrites as a key determinant of altered SNr activity in the dopamine depleted state, with important implications for efforts to restore functional SNr activity in this condition. TeaserIn silico model capturing heterogeneity suggests factors behind SNr altered activity in dopamine depletion and responses to input

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bioRxiv2025

On the role of L-type Ca2+ and BK channels in a biophysical model of cartwheel interneurons

Cartwheel interneurons (CWCs) in the auditory system exhibit a range of activity patterns relevant to auditory function and pathologies. Although experiments have shown how these patterns can vary across individual neurons and can change under pharmacological manipulations, the field has lacked a computational framework in which to explore the contributions of particular currents to these observations and to generate new predictions about the effects of manipulations on CWCs. In this work, we address this deficiency by presenting a conductance-based CWC computational model. This model captures the diversity of CWC activity patterns observed experimentally and suggests parameter changes that may underlie differences across cells. Bifurcation analysis of this model provides an explanation of how distinct dynamic mechanisms contribute to these differences, while direct simulations suggest how cells with different baseline dynamics will respond to variations in certain experimentally-accessible potassium and calcium channel conductances. In addition to the full model that we introduce, we present a reduced model that preserves CWC dynamic regimes. We classify the reduced model variables in terms of distinct dynamic timescales and show that the key transitions in dynamic patterns can be explained based on equilibria of the averaged dynamics of the slowest model variables, in a regime where the faster model variables exhibit oscillations. Overall, this study predicts how changes in parameters will influence CWC behavior, suggests how bifurcations contribute to changes in CWC dynamics, and provides a theoretical foundation that supports our simulation findings. Author summaryCartwheel interneurons (CWCs) are the most common class of inhibitory interneurons in an auditory brainstem region involved in sound localization and are believed to be important for auditory processing and pathologies. Distinct patterns of CWC activity have been observed experimentally in a variety of conditions. In this work, we present two novel computational models that simulate the factors contributing to CWC dynamics. By harnessing this framework, we are able to reveal the contributions of key ion currents to modulating CWC activity. Indeed, we find that the factors present in CWC neurons can produce a complicated dynamic landscape, with a wide range of output patterns possible as the relative strengths of these factors are varied. Overall, our models represent useful tools for understanding experimental results and generating new predictions about CWC behavior. In particular, in the more reduced of the two models, we can perform mathematical analysis to make more detailed predictions about the effects of current modulation on whether CWC neurons will exhibit regular spiking or more complex forms of outputs.

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bioRxiv2025

How the dynamic interplay of cortico-basal ganglia-thalamic pathways shapes the time course of deliberation and commitment

Although the cortico-basal ganglia-thalamic (CBGT) network is identified as a central circuit for decision-making, the dynamic interplay of multiple control pathways within this network in shaping decision trajectories remains poorly understood. Here we develop and apply a novel computational framework -- CLAW (Circuit Logic Assessed via Walks) -- for tracing the instantaneous flow of neural activity as it progresses through CBGT networks engaged in a virtual decision-making task. Our CLAW analysis reveals that the complex dynamics of network activity is functionally dissectible into two critical phases: deliberation and commitment. These two phases are governed by distinct contributions of underlying CBGT pathways, with indirect and pallidostriatal pathways influencing deliberation, while the direct pathway drives action commitment. We translate CBGT dynamics into the evolution of decision-related policies, based on three previously identified control ensembles (responsiveness, pliancy, and choice) that encapsulate the relationship between CBGT activity and the evidence accumulation process. Our results demonstrate two contrasting strategies for decision-making. Fast decisions, with direct pathway dominance, feature an early response in both boundary height and drift rate, leading to a rapid collapse of decision boundaries and a clear directional bias. In contrast, slow decisions, driven by indirect and pallidostriatal pathway dominance, involve delayed changes in both decision policy parameters, allowing for an extended period of deliberation before commitment to an action. These analyses provide important insights into how the CBGT circuitry can be tuned to adopt various decision strategies and how the decision-making process unfolds within each regime. Author summaryWe investigate how the cortico-basal ganglia-thalamic (CBGT) network coordinates decision-making through its interconnected pathways. Using a novel Circuit Logic Assessed via Walks (CLAW) framework, we trace instantaneous neural activity through virtual CBGT networks as they engage in forced choice decisions. This analysis uncovers two key phases of a decision: deliberation, shaped by the indirect and pallidostriatal pathways, and commitment, driven by the direct pathway. We also demonstrate that CBGT activity supports two distinct decision styles: fast decisions involve an early decision boundary collapse and strong directional preference, while slow decisions feature minimal changes during an extended deliberation phase. These findings reveal the dynamic mechanisms within the CBGT network that underlie the different decision processes and how these can be tuned to adapt decision-making across varying demands and contexts.

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bioRxiv2024

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles

Various mathematical models have been formulated to describe the changes in synaptic strengths resulting from spike-timing-dependent plasticity (STDP). A subset of these models include a third factor, dopamine, which interacts with spike timing to contribute to plasticity at specific synapses, notably those from cortex to striatum at the input layer of the basal ganglia. Theoretical work to analyze these plasticity models has largely focused on abstract issues, such as the conditions under which they may promote synchronization and the weight distributions induced by inputs with simple correlation structures, rather than on scenarios associated with specific tasks, and has generally not considered dopamine-dependent forms of STDP. In this paper we introduce forms of dopamine-modulated STDP adapted from previously proposed plasticity rules. We then analyze, mathematically and with simulations, their performance in two biologically relevant scenarios. We test the ability of each of the three models to complete simple value estimation and action selection tasks, studying the learned weight distributions and corresponding task performance in each setting. Interestingly, we find that each plasticity rule is well suited to a subset of the scenarios studied but falls short in others. Different tasks may therefore require different forms of synaptic plasticity, yielding the prediction that the precise form of the STDP mechanism present may vary across regions of the striatum, and other brain areas impacted by dopamine, that are involved in distinct computational functions.

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bioRxiv2024

Subject-specific maximum entropy model of resting state fMRI shows diagnostically distinct patterns of energy state distributions

ObjectiveExisting neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. MethodMEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. ResultsThe model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. ConclusionsDiagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

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bioRxiv2021

Putting the theory into 'burstlet theory': A biophysical model of bursts and burstlets in the respiratory preBötzinger complex

Inspiratory breathing rhythms arise from synchronized neuronal activity in a bilaterally distributed brainstem structure known as the preBotzinger complex (preBotC). In in vitro slice preparations containing the preBotC, extracellular potassium must be elevated above physiological levels (to 7 - 9 mM) to observe regular rhythmic respiratory motor output in the hypoglossal nerve to which the preBotC projects. Reexamination of how extracellular K+ affects preBotC neuronal activity has revealed that low amplitude oscillations persist at physiological levels. These oscillatory events are sub-threshold from the standpoint of transmission to motor output and are dubbed burstlets. Burstlets arise from synchronized neural activity in a rhythmogenic neuronal subpopulation within the preBotC that in some instances may fail to recruit the larger network events, or bursts, required to generate motor output. The fraction of subthreshold preBotC oscillatory events (burstlet fraction) decreases sigmoidally with increasing extracellular potassium. These observations underlie the burstlet theory of respiratory rhythm generation. Experimental and computational studies have suggested that recruitment of the non-rhythmogenic component of the preBotC population requires intracellular Ca2+ dynamics and activation of a calcium-activated non-selective cationic current. In this computational study, we show how intracellular calcium dynamics driven by synaptically triggered Ca2+ influx as well as Ca2+ release/uptake by the endoplasmic reticulum in conjunction with a calcium-activated non-selective cationic current can explain all of the key observations underlying the burstlet theory of respiratory rhythm generation. Thus, we provide a mechanistic basis to unify the experimental findings on rhythm generation and motor output recruitment in the preBotC.

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bioRxiv2021

Predictions and experimental tests of a new biophysical model of the mammalian respiratory oscillator

Previously our computational modeling studies (Phillips et al., 2019) proposed that neuronal persistent sodium current (INaP) and calcium-activated non-selective cation current (ICAN) are key biophysical factors that, respectively, generate inspiratory rhythm and burst pattern in the mammalian preBotzinger complex (preBotC) respiratory oscillator. Here, we experimentally tested and confirmed three predictions of the model from new simulations concerning the roles of INaP and ICAN: (1) INaP and ICAN blockade have opposite effects on the relationship between network excitability and preBotC rhythmic activity; (2) INaP is essential for preBotC rhythmogenesis; (3) ICAN is essential for generating the amplitude of rhythmic output but not rhythm generation. These predictions were confirmed via optogenetic manipulations of preBotC network excitability during graded INaP or ICAN blockade by pharmacological manipulations in neonatal mouse slices in vitro. Our results support and advance the hypothesis that INaP and ICAN mechanistically underlie rhythm and inspiratory burst pattern generation, respectively, in the isolated preBotC.

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bioRxiv2020

A computational model explains and predicts substantia nigra pars reticulata responses to pallidal and striatal inputs

As a rodent basal ganglia (BG) output nucleus, the substantia nigra pars reticulata (SNr) is well positioned to impact behavior. SNr neurons receive GABAergic inputs from the striatum (direct pathway) and globus pallidus (GPe, indirect pathway). Dominant theories of action selection rely on these pathways inhibitory actions. Yet, experimental results on SNr responses to these inputs are limited and include excitatory effects. Our study combines experimental and computational work to characterize, explain, and make predictions about these pathways. We observe diverse SNr responses to stimulation of SNr-projecting striatal and GPe neurons, including biphasic and excitatory effects, which our modeling shows can be explained by intracellular chloride processing. Our work predicts that ongoing GPe activity could tune the SNr operating mode, including its responses in decision-making scenarios, and GPe output may modulate synchrony and low-frequency oscillations of SNr neurons, which we confirm using optogenetic stimulation of GPe terminals within the SNr.

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bioRxiv2020

Delta oscillations are a robust biomarker of dopamine depletion severity and motor dysfunction in awake mice

Delta oscillations (0.5-4 Hz) are a robust but often overlooked feature of basal ganglia pathophysiology in Parkinsons disease and their relationship to parkinsonian akinesia has not been investigated. Here, we establish a novel approach to detect spike oscillations embedded in noise to provide the first study of delta oscillations in awake, dopamine depleted mice. We find that approximately half of neurons in the substantia nigra reticulata exhibit delta oscillations in dopamine depletion and that these oscillations are a strong indicator of dopamine loss and akinesia, outperforming measures such as changes in firing rate, irregularity, bursting and synchrony. We further establish that these oscillations are caused by the loss of D2 receptor activation and do not require motor cortex, contrary to previous findings in anesthetized animals. These results give insight into how dopamine loss leads to dysfunction and suggest a reappraisal of delta oscillations as a biomarker in Parkinsons disease.

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bioRxiv2019

Effects of persistent sodium current blockade in respiratory circuits depend on the pharmacological mechanism of action and network dynamics

The mechanism(s) of action of most commonly used pharmacological blockers of voltage-gated ion channels are well understood; however, this knowledge is rarely considered when interpreting experimental data. Effects of blockade are often assumed to be equivalent, regardless of the mechanism of the blocker involved. Using computer simulations, we demonstrate that this assumption may not always be correct. We simulate the blockade of a persistent sodium current (INaP), proposed to underlie rhythm generation in pre-Botzinger complex (pre-BotC) respiratory neurons, via two distinct pharmacological mechanisms: (1) pore obstruction mediated by tetrodotoxin and (2) altered inactivation dynamics mediated by riluzole. The reported effects of experimental application of tetrodotoxin and riluzole in respiratory circuits are diverse and seemingly contradictory and have led to considerable debate within the field as to the specific role of INaP in respiratory circuits. The results of our simulations match a wide array of experimental data spanning from the level of isolated pre-BotC neurons to the level of the intact respiratory network and also generate a series of experimentally testable predictions. Specifically, in this study we: (1) provide a mechanistic explanation for seemingly contradictory experimental results from in vitro studies of INaP block, (2) show that the effects of INaP block in in vitro preparations are not necessarily equivalent to those in more intact preparations, (3) demonstrate and explain why riluzole application may fail to effectively block INaP in the intact respiratory network, and (4) derive the prediction that effective block of INaP by low concentration tetrodotoxin will stop respiratory rhythm generation in the intact respiratory network. These simulations support a critical role for INaP in respiratory rhythmogenesis in vivo and illustrate the importance of considering mechanism when interpreting and simulating data relating to pharmacological blockade.\n\nAuthor summaryThe application of pharmacological agents that affect transmembrane ionic currents in neurons is a commonly used experimental technique. A simplistic interpretation of experiments involving these agents suggests that antagonist application removes the impacted current and that subsequently observed changes in activity are attributable to the loss of that currents effects. The more complex reality, however, is that different drugs may have distinct mechanisms of action, some corresponding not to a removal of a current but rather to a changing of its properties. We use computational modeling to explore the implications of the distinct mechanisms associated with two drugs, riluzole and tetrodotoxin, that are often characterized as sodium channel blockers. Through this approach, we offer potential explanations for disparate findings observed in experiments on neural respiratory circuits and show that the experimental results are consistent with a key role for the persistent sodium current in respiratory rhythm generation.

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bioRxiv2019

Weak modulation of thalamic discharge by basal ganglia output in association with a reaching task

Task-related activity in the ventral thalamus, a major target of basal ganglia output, is often assumed to be permitted or triggered by changes in basal ganglia activity through gating- or rebound-like mechanisms. To test those hypotheses, we sampled single-unit activity from connected basal ganglia output and thalamic nuclei (globus pallidus-internus, GPi, and ventrolateral-anterior nucleus, VLa) in monkeys performing a reaching task. Rate increases were the most common peri-movement change in both nuclei. Moreover, peri-movement changes generally began earlier in VLa than in GPi. Simultaneously-recorded GPi-VLa pairs rarely showed short-timescale spike-to-spike correlations or slow across-trials covariations and both were equally positive and negative. Finally, spontaneous GPi bursts and pauses were both followed by small, slow reductions in VLa rate. These results appear incompatible with standard gating and rebound models. Still, gating or rebound may be possible in other physiological situations: Simulations show how GPi-VLa communication can scale with GPi synchrony and GPi-to-VLa convergence, illuminating how synchrony of basal ganglia output during motor learning or in pathological conditions may render this pathway effective. Thus, in the healthy state, basal ganglia-thalamic communication during learned movement is more subtle than expected, with changes in firing rates possibly being dominated by a common external source.

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