Biology subjects

O'Leary, T.

Publications and source records attributed to O'Leary, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Hippocampal brain-machine interface-based navigation reveals CA1 representations of intended actions

The hippocampus integrates external cues and self-motion to construct cognitive maps. Activating these maps independently of immediate sensory and motor signals can support navigation by predicting future locations. The neural basis of such internally driven activation remains poorly understood. To address this, we had mice use a brain-machine interface (BMI) to directly control navigation from real-time hippocampal activity. In this condition, CA1 responses encoding running movement were not navigationally relevant, and place codes rapidly reconfigured to form new representations that discarded locomotion-related signals. By comparing neural representations across BMI-controlled navigation, locomotion-controlled navigation, and passive playback of predetermined routes, we found evidence of response patterns that were specific to conditions in which neural activity could causally influence an animals travel. This suggests the existence of CA1 responses that are not tied to external stimuli or self-motion, and that are suppressed when animals are merely passive observers.

neuroscience↗

bioRxiv2026

Cell-type specific sensing and control of firing rate statistics via channel dynamics

Neurons maintain functionality through homeostatic regulation of spiking activity over extended timescales. Calcium dependent conductance expression is known to regulate mean firing rate, but this is not sufficient to ensure dynamic range in spiking activity and sensitivity to input. This raises the question of whether firing rate variance can be sensed and controlled intracellularly. Using conductance-based models, we demonstrate that time-averaged intracellular calcium dynamics inherently provide a direct readout of both the mean and variance of spiking activity. We show that calcium-based feedback regulation of membrane conductance density can therefore jointly stabilize firing rate mean and variance against input disturbances. Because tuning maximal conductances modulates the underlying relationship between rate statistics, a cells homeostatic response is statedependent rather than fixed. As a consequence, cell-type-specific mixtures of ionic conductances yield distinct homeostatic modalities, implying that cell type dictates homeostatic behaviour as well as spiking and integrative properties.

neuroscience↗

bioRxiv2025

Learning in nonstationary environments with minimal neuralcircuitry

Animals must learn and make predictions in a world that is not only uncertain, but shaped by unmodelled contingencies: unknown unknowns. Despite this, current theories of how this is achieved are based on optimal (Bayesian) strategies in simple settings, where it is possible to fully represent a model of the world and infer its state based on sensory data. This could be impractical in situations where an animal has limited prior experience and cognitive capacity, particularly in short-lived animals with small brains. Using the neural circuitry of Drosophila as inspiration, we derive a learning framework, which we call Hetlearn, for coping in such situations. Hetlearn sacrifices asymptotic optimality on idealised inference tasks, making it less fragile to abrupt changes in environmental statistics and providing more accurate state information than standard Bayesian procedures when data are limited. Hetlearn explains key physiological and anatomical features of the Drosophila Mushroom body (MB), and predicts behavioural features of learning that are conserved across species, such as second-order conditioning and reversal learning.

neuroscience↗

bioRxiv2025

Information theoretic measures of neural and behavioural coupling predict representational drift

In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. We investigate how a neurons tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. We quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed redundant, and information requiring knowledge of the state of both neurons is termed synergistic. We found that a neurons tuning stability is positively correlated with the strength of its average pairwise redundancy with the population, and that these high-redundancy neurons also tend to show high average pairwise synergy. We hypothesize that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. Regardless of the mechanistic implications of our work, the stability-redundancy relationship may support improved longitudinal neural decoding in technology that has to track population dynamics over time, such as brain-machine interfaces. Author summaryActivity in the brain represents information about the outside world and how we interact with it. Recent evidence shows that these representations slowly change day to day, while memories and learned behaviours stay stable. Individual neurons change their relationship to external variables at different rates, and we explore how interactions with other neurons in the population relates to this neuron-to-neuron variability. We find that more stable neurons tend to share information about external variables with many other neurons. Our results suggest there are constraints on how representations can change over time, and that these constraints are exhibited in shared fluctuations in activity among neurons in the population.

neuroscience↗

bioRxiv2024

An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation

Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouses physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.

neuroscience↗

bioRxiv2024

Rapid, interpretable, data-driven models of neural dynamics using Recurrent Mechanistic Models

Obtaining predictive models of a neural system is notoriously challenging. Detailed models suffer from excess model complexity and are difficult to fit efficiently. Simplified models must negotiate a tradeoff between tractability, predictive power and ease of interpretation. We present a new modelling paradigm for estimating predictive mechanistic-like models of neurons and small circuits that navigates these issues using methods from systems theory. The key insight is that membrane currents can be modelled using two scalable system components optimized for learning: linear state space models, and nonlinear artificial neural networks (ANNs). Combining these components, we construct two types of membrane currents: lumped currents, which are flexible, and data-driven conductance-based currents, which are interpretable. The resulting class of models -- which we call Recurrent Mechanistic Models (RMMs) -- can be trained in a matter of seconds to minutes on intracellular recordings during an electrophysiology experiment, representing a step change in performance over previous approaches. As a proof-of-principle, we use RMMs to learn the dynamics of two groups of neurons, and their synaptic connections, in the Stomatogastric Ganglion (STG), a well-known central pattern generator. We show that RMMs are efficiently trained using teacher forcing and multiple shooting. Due to their reliability, efficiency and interpretability, RMMs enable qualitatively new kinds of experiments using predictive models in closed-loop neurophysiology and online estimation of neural properties in living preparations. Significance StatementOur ability to understand the nervous system has been hindered by the difficulty of constructing good predictive models of neurons and circuits. This difficulty persists despite vast accumulated knowledge of how the basic components of the nervous system work, marking a gap in our ability to explain neural dynamics in terms of underlying mechanisms. This work describes a new data-driven modelling approach for neurons and small circuits that combines predictive power with mechanistic interpretation.

neuroscience↗

bioRxiv2024

Heavy-tailed statistics of cortical representational drift are advantageous for stabilised downstream readouts

Representational drift of fixed stimuli, learned tasks and familiar environments is observed in many brain areas, leading to reconfiguration of population codes over days to weeks. This raises the question of whether downstream brain regions employ mechanisms to track changes in population activity and thus preserve the fidelity of the information they extract. We show that the statistical properties of drift have a significant impact on such mechanisms. Over an extended period, a net change in population tuning due to drift can arise from an accumulation of small changes distributed across the population, or via abrupt jumps that affect smaller subsets of cells at each time point. We demonstrate that an adaptive readout can exploit the heavy-tailed statistics of abrupt jumps to maintain a more stable readout using a simple inference mechanism. Using experimental data, we investigate the extent to which heavy-tailed drift statistics are observed during representational drift in the posterior parietal cortex and visual cortex. We find that experimentally measured drift does not conform to a Gaussian random walk. Instead, we find sudden jumps in neural tuning that would be advantageous for a downstream observer adapting to changes in representation. These observations motivate future study to determine whether adaptive decoding mechanisms exist in the brain and to determine the physiological mechanisms that shape the statistics of representational drift.

neuroscience↗

bioRxiv2024

Structural insights into IMP2 dimerization and RNA binding

IGF2BP2 (IMP2) is an RNA-binding protein that contributes to cancer tumorigenesis and metabolic disorders. Structural studies focused on individual IMP2 domains have provided important mechanistic insights into IMP2 function; however, structural information on full-length IMP2 is lacking but necessary to understand how to target IMP2 activity in drug discovery. In this study, we investigated the behavior of full-length IMP2 and the influence of RNA binding using biophysical and structural methods including mass photometry, hydrogen-deuterium exchange coupled to mass spectrometry (HDX-MS), and small angle x-ray scattering (SAXS). We found that full-length IMP2 forms multiple oligomeric states but predominantly adopts a dimeric conformation. Molecular models derived from SAXS data suggest the dimer is formed in a head-to-tail orientation by the KH34 and RRM1 domains. Upon RNA binding, IMP2 forms a pseudo-symmetric dimer different from its apo/RNA-free state, with the KH12 domains of each IMP2 molecule forming the dimer interface. We also found that the formation of IMP2 oligomeric species, which includes dimers and higher-order oligomers, is sensitive to ionic strength and RNA binding. Our findings provide the first insight into the structural properties of full-length IMP2, which may lead to novel opportunities for disrupting its function with more effective IMP2 inhibitors.

molecular biology↗

bioRxiv2023

Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells

Grid cells and place cells constitute the basic building blocks of the medial entorhinal-hippocampal spatial cognitive map by representing the spatiotemporal continuum of an animals past, present and future locations. However, the spatiotemporal relationship between these different cell types is unclear. Here we co-recorded grid and place cells in freely foraging rats. We show that average time shifts in grid cells tend to be prospective and are proportional to their spatial scale, providing a nearly instantaneous readout of a spectrum of progressively increasing time horizons ranging hundreds of milliseconds. Average time shifts of place cells are generally larger compared to grid cells and also increase with place field sizes. Moreover, time shifts displayed nonlinear modulation by the animals trajectories in relation to the local boundaries and locomotion cues. Finally, long and short time shifts occurred at different parts of the theta cycle, which may facilitate their readout. Together, these findings suggest that progressively increasing time horizons of grid and place cells may provide a basis for calculating animal trajectories essential for goal-directed navigation and planning.

neuroscience↗

bioRxiv2023

Active Dendrites Enable Robust Spiking Computations despite Timing Jitter

Dendritic action potentials exhibit long plateaus of many tens of milliseconds, outliving axonal spikes by an order of magnitude. The computational role of these slow events seems at odds with the need to rapidly integrate and relay information throughout large nervous systems. We propose that the timescale of dendritic potentials allows for reliable integration of asynchronous inputs. We develop a physiologically grounded model in which the extended duration of dendritic spikes equips each dendrite with a resettable memory of incoming signals. This provides a tractable model for capturing dendritic nonlinearities observed in experiments and in more complex, detailed models. Using this model, we show that long-lived, nonlinear dendritic plateau potentials allow neurons to spike reliably when confronted with asynchronous input spikes. We demonstrate this model supports non-trivial computations in a network solving an association/discrimination task using sparse spiking that is subject to timing jitter. This demonstrates a computational role for the specific time-course of dendritic potentials in situations where decisions occur quickly, reliably, and with a low number of spikes. Our results provide empirically testable hypotheses for the role of dendritic action potentials in cortical function as well as a potential bio-inspired means of realising neuromorphic spiking computations in analog hardware.

neuroscience↗

bioRxiv2023

Variational Log-Gaussian Point-Process Methods for Grid Cells

We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log-Gaussian Poisson models can be calculated quickly. This class of models has closed-form expressions for the evidence lower-bound, and can be estimated rapidly for certain parameterizations of the posterior covariance. We provide an implementation that operates in a low-rank spatial frequency subspace for further acceleration, and demonstrate these methods on experimental data.

neuroscience↗

bioRxiv2022

How cerebellar architecture facilitates rapid online learning

SO_SCPLOWUMMARYC_SCPLOWThe cerebellum has a distinctive architecture in which inputs undergo a massive size expansion in dimensionality in early layers. Marr and Albuss classic codon theory and more recent extensions1-4 argue that this architecture facilitates learning via pattern separation. The essence of this idea is that sparsely active clusters -- codons-- of inputs are more easily separable in a higher dimensional representation. However, recent physiological data indicate that cerebellar activity is not sparse in the way anticipated by codon theory. Moreover, there is a conceptual gap between static pattern separation and the critical role of the cerebellum in dynamic tasks such as motor learning. We use mathematical analysis and simulations of cerebellar learning to identify specific difficulties inherent to online learning of dynamic tasks. We find that size expansions directly mitigate these difficulties, and that this benefit is maximised when granule cell activity is dense.

neuroscience↗

bioRxiv2021

Autoregulation of switching behavior by cellular compartment size

Many kinds of cellular compartments comprise decision making mechanisms that control growth and shrinkage of the compartment in response to external signals. Key examples include synaptic plasticity mechanisms that regulate the size and strength of synapses in the nervous system. However, when synaptic compartments and postsynaptic densities are small such mechanisms operate in a regime where chemical reactions are discrete and stochastic due to low copy numbers of the species involved. In this regime, fluctuations are large relative to mean concentrations, and inherent discreteness leads to breakdown of mass action kinetics. Understanding how synapses and other small compartments achieve reliable switching in the low copy number limit thus remains a key open problem. We propose a novel self regulating signaling motif that exploits the breakdown of mass action kinetics to generate a reliable size-regulated switch. We demonstrate this in simple two and three-species chemical reaction systems and uncover a key role for inhibitory loops among species in generating switching behavior. This provides an elementary motif that could allow size dependent regulation in more complex reaction pathways and may explain discrepant experimental results on well-studied biochemical pathways.

systems biology↗

bioRxiv2021

Metabolic and Proteomic Defects in Human Hypertrophic Cardiomyopathy

RationaleImpaired cardiac energetics in hypertrophic cardiomyopathy (HCM) is thought to result from increased ATP utilization at the sarcomere and is believed to be central to pathophysiology. However, the precise defects in cardiac metabolism and substrate availability in human HCM have not been defined. ObjectiveThe purpose of this study is to define major disease pathways and determine the pool sizes of intermediary metabolites in human HCM. Methods and ResultsWe conducted paired proteomic and metabolomic analyses of septal myectomy samples from patients with HCM and compared results to non-failing control human hearts. Increased abundance of extracellular matrix and intermediate filament / Z-disc proteins, and decreased abundance of proteins involved in fatty acid oxidation and cardiac energetics was evident in HCM compared to controls. Acyl carnitines, byproducts of fatty acid oxidation, were markedly depleted in HCM samples. Conversely, the ketone body 3-hydroxybutyrate, lactate, and the 3 branched chain amino acids, were all significantly increased in HCM hearts, suggesting that they may serve as alternate fuel sources for the production of ATP. ATP, nicotinamide adenine dinucleotide (NADH), NADP and NADPH, and acetyl CoA were also severely depleted in HCM hearts. Based on measurements from human skinned muscle fibers, the magnitude of observed reduction in ATP content in the HCM hearts would be expected to decrease the rate of cross-bridge detachment, implying a direct effect of energy depletion on myofilament function that could contribute to diastolic dysfunction. ConclusionsHCM hearts display profound deficits in cardiac energetics, marked by depletion of fatty acid derivatives and compensatory increases in other metabolites that could serve as alternate fuel sources. These results lend support to the paradigm that energy depletion contributes to the pathophysiology of HCM and also have important therapeutic implications for the future design of metabolic modulators to treat HCM.

physiology↗

bioRxiv2021

Chemical Editing of Proteoglycan Architecture

Proteoglycans are heterogeneous macromolecular glycoconjugates that orchestrate many important cellular processes. While much attention has focused on the poly-sulfated glycosaminoglycan chains that decorate proteoglycans, other important elements of proteoglycan architecture, such as their core proteins and cell surface localization, have garnered less emphasis. Hence, comprehensive structure-function relationships that consider the replete proteoglycan architecture as glycoconjugates are limited. Here, we present a comprehensive approach to study proteoglycan structure and biology by fabricating defined semi-synthetic modular proteoglycans that can be tailored for cell surface display. To do so, we integrate amber codon reassignment in the expression of sequence-fined proteoglycan core proteins, metabolic oligosaccharide engineering to produce functionalizable glycosaminoglycans, and bioorthogonal click chemistry to covalently tether the two components. These materials permit the methodical dissection of the parameters required for optimal binding and function of various proteoglycan-binding proteins, and they can be modularly displayed on the surface of any living cell. We demonstrate that these sophisticated materials can recapitulate the functions of native proteoglycans in mouse embryonic stem cell differentiation and cancer cell spreading, while permitting the identification of the most important contributing elements of proteoglycan architecture toward function. This technology platform will confer structural resolution toward the investigation of proteoglycan structure-function relationships in cell biology.

biochemistry↗

bioRxiv2021

Self-Healing Neural Codes

As an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days, and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such "representational drift" raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits, and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term, and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations. SignificanceThe brain is capable of adapting while maintaining stable long-term memories and learned skills. Recent experiments show that neural responses are highly plastic in some circuits, while other circuits maintain consistent responses over time, raising the question of how these circuits interact coherently. We show how simple, biologically motivated Hebbian and homeostatic mechanisms in single neurons can allow circuits with fixed responses to continuously track a plastic, changing representation without reference to an external learning signal.

neuroscience↗

bioRxiv2020

Optimal synaptic dynamics for memory maintenance in the presence of noise

Synaptic connections in many brain areas have been found to fluctuate significantly, with substantial turnover and remodelling occurring over hours to days. Remarkably, this flux in connectivity persists in the absence of overt learning or behavioural change. What proportion of these ongoing fluctuations can be attributed to systematic plasticity processes that maintain memories and neural circuit function? We show under general conditions that the optimal magnitude of systematic plasticity is typically less than the magnitude of perturbations due to internal biological noise. Thus, for any given amount of unavoidable noise, 50% or more of total synaptic turnover should be effectively random for optimal memory maintenance. Our analysis does not depend on specific neural circuit architectures or plasticity mechanisms and predicts previously unexplained experimental measurements of the activity-dependent component of ongoing plasticity.

neuroscience↗

bioRxiv2020

Paradoxical relationships between active transport and global protein distributions in neurons

Neural function depends on continual synthesis and targeted trafficking of intracellular components, including ion channel proteins. The detailed biophysics active ion channel transport are increasingly well understood, along with the steady-state distribution of functional channels in the membrane. However we lack a quantitative understanding of how transport mechanisms give rise to stable expression patterns, and how live measurements of active transport relate to static estimates of channel density in neurites. We experimentally measured neuronal transport and expression densities of Kv4.2, a voltage-gated transient potassium channel. Kv4.2 is known to have a highly specific dendritic expression and little or no reported functional expression in axons. Surprisingly, in over 500 hours of quantitative live imaging, we found substantially higher microtubule-based transport of Kv4.2 subunits in axons compared to dendrites. We show that this paradoxical result is expected using a mass action trafficking model of intracellular transport that we calibrate to experimental measurements. Furthermore, we find qualitative differences in axonal and dendritic active transport that are captured in a stochastic model of puncta transport. This reveals that active transport is tuned to efficiently move cargo through axons while promoting mixing in dendrites. Finally, our data reveals trends in transport parameters that can explain the functional density profile of Kv4.2. Puncta velocity bias is directed distally and the magnitude of this bias increases with distance from the soma. These trends are consistent with an analytical solution of a linear transport PDE, corroborating previously unexplained distributions of Kv4.2 subunit localization and A-type current density. Together, our results provide new quantitative data on ion channel trafficking and reveal counterintuitive but mathematically consistent relationships between the distribution of cargo that is in transit and its functional expression. SIGNIFICANCEThis study of ion channel transport reveals a seemingly counterintuitive result: the majority of subunit transport occurs in axons for a cargo whose static distribution is concentrated in dendrites. This disparity is reconciled by a simple mathematical model of transport, which reveals that the local density of actively transported intracellular cargo can show an inverse relationship with its static expression density. Mass action models also reconcile the previously unexplained, highly asymmetric, increasing distribution of Kv4.2 with its measured trafficking density that resembles diffusion with minimal drift. The generality of our analysis prompts caution in how static snapshots of intracellular cargo distributions should be interpreted for any type of intracellular cargo.

neuroscience↗