bioRxiv Subject Collection: Neuroscience's Journal
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Sunday, November 9th, 2025
| Time |
Event |
| 2:45p |
Population-level temporal decoding and dynamical structure in frontostriatal circuits during decision making
Perceptual decisions unfold over time, requiring neural circuits to evaluate sensory evidence, track elapsed time, and commit to an action. To investigate how these computations are distributed across corticostriatal circuits, we recorded neural population activity using Neuropixels probes in the rat frontal orienting field (FOF) and anterior dorsal striatum (ADS) during a free-response auditory change detection task. Both regions exhibited interesting dynamical properties during task performance. Using single-trial population decoding, we found that both FOF and ADS robustly encoded retrospective time from stimulus onset and prospective time preceding the decision report. Using a new approach to analyze temporal encoding to identify the dynamical structure supporting decoding, we found that time encoding could be decomposed into two primary dynamical motifs: monotonic ramp-like trajectories and transient bump-like trajectories. While these modes were similarly expressed during early evidence evaluation, FOF exhibited more pronounced decision-aligned changes near the time of the decision report, possibly reflecting a state transition at commitment. Population geometry analyses further revealed stable low-dimensional subspaces during evidence evaluation that transitioned at decision commitment, with significantly larger subspace changes in FOF than ADS. Together, these results demonstrate that FOF and ADS share common dynamical features during evidence evaluation but diverge near the time of decision commitment, with FOF exhibiting stronger state-transition dynamics. | | 2:45p |
Cell surface markers identify astrocyte subpopulations in the adult hippocampus with a heterogeneous response to aging
Astrocyte diversity is currently expanding both between and within specific brain regions. Here, we assessed the spatial distribution and transcriptomic profile of two hippocampal astrocyte subpopulations, defined by combinatorial expression of the cell surface astrocyte markers ACSA-1 or GLAST/SLC1A3, and ACSA-2 or ATP1B2. Fluorescence activated cell sorting and genome-wide transcriptomics by bulk RNAseq uncovered distinct transcriptional signatures of the two astrocyte subsets and highlighted heterogeneous responses during aging. The most abundant ATP1B2/GLAST double-positive astrocytes corresponded to mature glial cells with increased protein glycosylation and stable gene expression patterns. Signatures related to mitochondrial respiration and cholesterol metabolism were induced during aging in ATP1B2 single-positive astrocytes, while cell adhesion genes from the {gamma}-protocadherin cluster were repressed in double-positive astrocytes. Heterochronic co-culture assays with primary neurons show the loss of synaptogenic function of old ATP1B2/GLAST astrocytes. Our results complement previous studies demonstrating the presence of morphological and molecular astrocyte heterogeneity within the hippocampus, and uncover differences among astrocyte subsets in their transcriptomic response to aging. | | 2:45p |
Sensory processing reformats odor coding around valence and dynamics
Extracting relevant features of a complex sensory signal typically involves sequential processing through multiple brain regions. However, identifying the logic and mechanisms of these transformations has been difficult, due to the challenges of measuring both activity within and long-range connectivity between multiple neural populations. Here, we investigate the reformatting of odor information across two stages of the Drosophila olfac-tory system. We measure the odor tuning of 20 types of anatomically-defined third order lateral horn neuron (LHN) and compare to predictions based on the odor tuning of second-order projection neurons (PNs) and PN-LHN connectivity. We find that LHNs reformat PN activity in two distinct ways. First, LHNs selectively discard in-formation about odor identities with similar valence (i.e., attractiveness or aversiveness). This emerges from a precise alignment of PN odor tuning and PN-LHN connectivity, as well as odor-specific inhibition and boosting of LHN activity. This creates a population code for valence that is more explicit than in PNs. Second, a subset of LHNs selectively discard information about continuing odor presence, by responding only transiently to odor on-set. This creates a population code for odor dynamics that is more explicit than in PNs. Across LHNs, valence and dynamics are independent of each other. Thus, feedforward connectivity and local inhibition combine to ex-tract two orthogonal dimensions of olfactory information. | | 2:45p |
Intrinsic ion dynamics underlies the temporal nature of resting-state functional connectivity
The neural mechanisms underlying the emergence of functional connectivity in resting-state fMRI remain poorly understood. Recent studies suggest that resting-state activity consists of brief periods of strong co-fluctuations among brain regions, which reflect overall functional connectivity. Others report a continuum in co-fluctuations over time, with varying degree of correlation to functional connectivity. These findings raise the critical question: what neural processes underlie the temporal structure of resting-state activity? To address this, we used a biophysically realistic whole-brain computational model in which resting-state activity emerged from temporal variations in the ion concentrations of potassium (K+) and sodium (Na+), intracellular chloride (Cl-), and the activity of the Na+/K+ ATPase. The model reproduced transient periods of high co-fluctuations, and the functional connectivity at different co-fluctuation levels correlated to varying degrees with the connectivity measured over the entire simulation, in line with experimental observations. The periods of high co-fluctuations were aligned with large changes in extracellular ion concentrations. Furthermore, critical parameters governing ion dynamics strongly affected both the timing of these transient events and the spatial structure of the resulting functional connectivity. The balance of excitatory and inhibitory activity further modulated their frequency and amplitude. Together, these results suggest that intrinsic fluctuations in ion dynamics could serve as a plausible neural mechanism for the temporal organization of co-fluctuations and resting-state functional connectivity. | | 2:45p |
Neural manifolds that orchestrate walking and stopping
Walking, stopping and maintaining posture are essential motor behaviors, yet the underlying neural processes remain poorly understood. Here, we investigate neural activity behind locomotion and its walk-to-stop transition. Based on a new theory of the lumbar spinal cord(1, 2) we propose and predict that spinal population activity contains limit cycle dynamics to drive walking and fixed-point attractors for stopping. To test these predictions we record neural activity in lumbar cord of freely moving rats using Neuropixels probes(3). To control stopping, we also stimulate a brainstem nucleus, known to induce motor arrest(4-7). We find: During locomotion, the population activity of lumbar spinal neurons exhibits rotational dynamics(8-10). These dynamics unfold within a low-dimensional locomotor manifold(11), a looping trajectory that serves as the repeating signature of locomotion, that also behaves as a limit-cycle attractor. Shortly before stopping, the neural state rapidly changes from the locomotor manifold to a "postural" fixed point attractor. When testing the stability of the fixed point using perturbations, the state shifts to a nearby albeit different fixed point. Repeated stoppings form a local quasi-continuum of fixed points representing various poses - i.e. a postural manifold. These observations are in agreement with our theory, which further indicates mechanistic roles for subpopulations of spinal interneurons for controlling walking and stopping. Besides explaining the data, our theory makes further predictions to be tested in future experiments. | | 2:45p |
Parvalbumin interneurons gate and shape striatal sequences
Loss of striatal interneurons expressing parvalbumin (PV+) is associated with impulsive and uncontrolled behaviors, yet how these cells contribute to striatal information processing is poorly understood. We compared spiking of five identified neuron types in sensorimotor striatum, as unrestrained rats waited for a cue then performed brief, well-practiced actions. During waiting PV+ selectively increased firing, and their suppression increased premature movements. This indicated a role restraining actions, yet PV+ suppression during the cue instead retarded actions. Each action was accompanied by a rapid sequence of spiny projection neuron (SPN) spiking, including both direct and indirect pathways and overlaid by sequential PV+ firing. Pairs of PV+ and SPNs showed millisecond-level synchrony, and PV+ firing inhibited nearby SPNs ~2ms later. PV+ interneurons thus provide both broad restraint and precise sculpting of striatal output to achieve fluid, appropriately timed behavior. | | 2:45p |
Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks
Neuroscience-inspired neural networks bridge biology and technology, offering powerful tools to model brain function while enabling adaptive, efficient control in robotics. In this work, we present a neuroscience-inspired synaptic learning rule based on the synchronization of synaptic inputs to single excitatory neurons within a feedforward spiking neural network. The model consists of three excitatory layers and two feedback inhibitory layers, with initially low connection probabilities and weak synaptic weights assigned to the excitatory neurons. Under an unsupervised learning paradigm, stimulus patterns were presented to the network, allowing synaptic weights and connectivity to evolve dynamically across training trials. We investigated how these dynamics depended on feedback inhibition intensity and identified conditions under which the network achieved stable activity. Furthermore, we evaluated the model pattern separation efficacy and its relationship to network dynamics. The results highlight the critical role of feedback inhibition in both stabilizing the network and enhancing pattern separation. In particular, results show balanced synchronization between excitatory and inhibitory populations maximizes separation efficacy. Beyond providing a novel computational framework for understanding information processing in neural systems, this model also offers insights into cognitive disorders associated with impaired inhibition and pattern separation, such as autism and schizophrenia. Finally, we embedded the trained network within a simulated agent navigating a two-dimensional environment, where it was tasked with identifying a trained stimulus as an obstacle and avoiding it. The model offers a framework for advancing cognitive robotics by enabling novel approaches that mimic natural intelligence and support the learning of complex environmental patterns. | | 2:45p |
Competition between memories for reactivation as a mechanism for long-delay credit assignment
Animals can associate events with their outcomes, even if there is a long delay between the two. For example, in conditioned taste aversion, animals gain an aversion to a taste (the conditioned stimulus, CS) if sickness (an unconditioned stimulus, US) is induced up to 12 hours later. Established correlational plasticity mechanisms, operating on timescales of milliseconds to seconds, do not wholly explain how networks of neurons achieve such long-delay credit assignment. Moreover, if the animal experiences an intervening taste (an interfering stimulus, IS), the IS gains some "credit" for the causality of the outcome, reducing aversion to the CS. We hypothesize that reactivation of prior events at the time of outcome causes specific associative learning between those events and the outcome. We explore the inherent competition underlying credit assignment using a spiking neural network model storing memories through time-decaying synaptic strengthenings in two groups of neurons producing inherently competing attractor states. We show how the later memory can be reactivated more often and reduce the reactivation of a prior memory. Also, we provide a mechanism for the experimental finding of a rebound in association with, and therefore aversion to, the CS if the time between the following IS and US is increased. Such a result can appear paradoxical as associations typically diminish with time, but arises when the IS initially produces a strong decrease in reactivation of the CS, but reactivations of the CS thereafter increase, in spite of weakening synaptic strengths, because competing reactivations of the IS decrease more. By reactivating the memories probabilistically, neural circuits can assign the credit in a biologically plausible way. |
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