Neuroscience Radar

★ A minimal E/I circuit model explains differential recovery from amblyopia under TTX and reverse occlusion

Lawrence Udeigwe — Thu, 05 Nov 2026 00:00:00 +0000

97% · Computational models 97%

Recovery from amblyopia beyond the critical period remains a major challenge in visual neuroscience. Experimental studies show that transient inactivation of the fellow eye with tetrodotoxin (TTX) produces stronger and more persistent recovery than reverse occlusion (RO), despite both interventions reducing competition from the fellow eye. The circuit mechanisms underlying this difference remain unclear. We develop a minimal cortical circuit model combining excitatory-inhibitory population dynamics, BCM-like metaplasticity, homeostatic regulation, and eye-specific synaptic plasticity. Monocular deprivation drives the network into an amblyopic operating regime with reduced excitatory activity and fellow-eye dominance. Phase-plane and operating-regime analyses show that TTX and RO subsequently place the circuit in distinct dynamical states. TTX lowers inhibitory activity while maintaining excitatory activity above the BCM modification threshold, creating a larger plasticity-permissive regime than RO. These differences produce stronger recovery of amblyopic-eye responsiveness and greater correction of ocular dominance under TTX. A local robustness analysis across 300 perturbed parameter sets shows that the TTX advantage is not dependent on a narrowly tuned parameter choice. These results provide a mechanistic account of the differential efficacy of fellow-eye inactivation and suggest that recovery from sensory deprivation depends not only on changes in sensory input, but also on the dynamical operating regime of the cortical circuit.

★ SafeLens: A Statistically Controlled Nonlinear Machine Learning Framework for Characterizing Hidden Dynamics in Event-Locked Neural Activity

Minasi, C. B., Hassan, U., Cline, C. C., Keller, C. J. — Thu, 05 Nov 2026 00:00:00 +0000

96% · Methods 96%

Machine learning has advanced rapidly over the past decade, yet event-locked neural activity is still analyzed predominantly with traditional statistics and linear models. While traditional linear methods provide computationally tractable statistical control for pre-selected features or trial-averaged waveforms, they miss subtle nonlinear and trial-varying neural dynamics. Nonlinear models can capture these complex patterns, but their feature reliance is opaque and notoriously difficult to threshold statistically. We introduce SafeLens (Searchlight Analysis of Fluctuation Events, Leveraging Extensive Null Sampling) to combine the power of nonlinear machine learning with the rigorous false-positive control of traditional statistical inference. At each position of a sliding window, a random forest learns to discriminate event-locked activity from interleaved resting-state neural activity using multiscale temporal derivatives. Randomized feature ablation inverts slope polarity in held-out trials to map the random forest models' reliance on specific rising versus falling excursions across the time-frequency space. SafeLens then establishes an empirical null by running the same pipeline on separate resting-state recordings, training forests to discriminate rest from rest. This empirical null translates each channel's complex nonlinear resting dynamics into a model-matched noise floor on the map's linear reliance scale, allowing a single threshold to control the family-wise error rate against the channel's own background structure rather than a summary statistic of its baseline. When we injected simulated cortico-cortical evoked potentials (CCEPs) into resting intracranial EEG recordings, SafeLens recovered ~2.5 times as many polarity-resolved deflections as trial-average thresholding and the closest data-driven method, maintaining its advantage under trial-to-trial variability while yielding ~5-11 times fewer false components, ~2-5 times lower mean boundary error, and superior single-trial latency tracking as a timing prior. In eight patients, CCEPs remained detectable above chance until 1,040 ms after stimulation, compared with 570 and 455 ms for those benchmarks. Together, these findings establish SafeLens as a novel solution for enabling nonlinear models to uncover subtle, trial-varying neural dynamics under strict statistical control.

Learning Time-Varying Connectomes for Stable Behavior Across Development

Christof Fehrman, Jacob J. Morra, Eva A. Naumann — Thu, 05 Nov 2026 00:00:00 +0000

93% · Computational models 93%

Neural circuits must maintain functionality to drive effective behavior while their size, topology, geometry, and intrinsic dynamics change across development. To model developing visuomotor circuits and behavior, we investigated whether a single compact generative encoding could produce functional recurrent controllers across such changes. Building on the HyperNEAT algorithm, we represented this program as a compositional pattern-producing network that received developmental time as an input to generate age-specific connectivity. The developmental substrate prescribed neuronal birth, position, sensory organization, and intrinsic time constants. We tested this approach using two geometrically distinct developmental substrates and two visuomotor tasks: optomotor responses and phototaxis. Controllers optimized at only the initial or mature substrate generally failed when evaluated at other ages. In contrast, controllers optimized across multiple developmental snapshots maintained substantially higher and more consistent performance across trained and unseen ages. Optimization at only three developmental snapshots was often sufficient to produce effective behavior across the tested developmental interval. The resulting connectomes changed substantially with age and did not converge on a single trajectory of sparsity or connection-weight statistics. Controllers also differed in whether they relied on neurons born later in development. These results demonstrate that a shared spatiotemporal time-varying connectivity program can produce recurrent neural networks that preserve behavior across substantial developmental changes. Together, these results provide a framework for studying how functional stability can emerge without requiring structural or dynamical stability.

Cell-type-specific modularity in E/I networks shapes dynamical regimes and computational capabilities

Emmanouil Giannakakis, Victor Buend\'ia, Oleg Vinogradov, Anna Levina — Thu, 05 Nov 2026 00:00:00 +0000

93% · Computational models 93%

Structural modularity is a ubiquitous feature of brain networks that shapes neural dynamics and information processing. Yet excitatory and inhibitory populations can differ in their modular organization, and it remains unclear how this asymmetry affects network dynamics and computation. Here, using dynamical mean-field theory and simulations of networks performing complex temporal tasks, we show that independently tuning excitatory and inhibitory modularity controls the dynamical regimes of balanced E/I networks. In particular, such tuning can generate a dominant spectral outlier despite overall E/I balance, producing a topology-controlled transition between bulk- and low-rank-dominated regimes, with different dynamical bifurcations leading to chaos and bistability, respectively. In chaotic time-series prediction tasks, computational performance is enhanced near these dynamical boundaries, including both the onset of chaos and the transition to bistability. Favorable configurations exhibit strongly modular excitation and comparatively homogeneous inhibition, similar to what is observed in cortical connectivity.

An Eyewear-Based EEG Interface for Temporal-Region Monitoring and Emotion Recognition

Li, X., Chai, L., Xu, S., Ni, J., So, K.-F. — Thu, 05 Nov 2026 00:00:00 +0000

91% · Methods 91%

Conventional electroencephalography (EEG) devices are often impractical for ambulatory settings due to their reliance on specialized hardware and expert operation. To address this, we developed a user-friendly eyewear-based EEG system featuring dry titanium electrodes positioned approximately 5 mm inferoposterior to the T7/T8 sites (near the ears), with metal nose pads serving as references. We evaluated the system across 103 recordings spanning five paradigms: alpha reactivity, blink-related transients, fatigue, attention, and video-elicited emotion. Following quality control, 93.2-100.0% of epochs were retained. The system demonstrated high sensitivity to ocular transients, showing significant amplitude increases during the blinking task. The emotion task yielded the most robust state-related features, with statistically significant differences in differential entropy across negative, neutral, and positive video categories, achieving balanced accuracies of 0.882 for two-class and 0.667 for three-class prediction. Despite challenges at the multiple testing correction stage, the alpha reactivity and fatigue contrast tasks were still noteworthy to some extent. The attention paradigm revealed a consistent increase in differential entropy during focused states, and the eyewear system outperformed the Muse headband in group-held-out classification. These findings provide strong evidence for the feasibility of temporal-region eyewear EEG acquisition, highlighting its potential applications in the real-world, particularly for emotion recognition.

Sensory representation by non-equilibrium flows of neural activity at the mesoscale of cortex

Braun, J., Lamb, N., Ghosh, K. - — Wed, 04 Nov 2026 00:00:00 +0000

94% · Computational models 94%, Neural coding 85%

We present a non-equilibrium theory of sensory perception and the mesoscale of sensory cortex. We propose that local neuron assemblies (cortical columns) toggle spontaneously between activity states (on and off) and that small populations of columns are coupled within and between areas of sensory cortex. The resulting network generates sudden, large-scale fluctuations and represents sensory inputs and perceptions by characteristic patterns of activity flows. The non-equilibrium dynamic is governed by the variational principle of maximal path entropy, or Maximum Caliber. Many current theories, by contrast, posit equilibrium dynamics and representation with activity states Our minimal model of sensory cortex describes populations of columns from two cortical areas and is fully constrained by empirical observations of `reversals' in perceived appearance, which occur spontaneously for visual, auditory, and tactile stimulation. We formalize representation by activity flows with dynamic Hamiltonian functions. Surprisingly, effective representation requires small-number fluctuations of toggling columns, as larger numbers would be dominated by hysteresis. Finally, we detail predictions for the microscale of spiking neurons that follow from on-off toggling and non-equilibrium dynamics. We conclude that the neural levels underlying perception are functionally organized in terms of activity flows. The inherent instability of non-equilibrium ensures active updating and renders all perceptions open to immediate revision. We conjecture that current theories of brain function (such as the Free Energy Principle) could be reformulated in terms of the variational principle of non-equilibrium, Maximum Caliber.

Glider compositionality and information capacity in an E/I-balanced neuronal automaton

Belluscio, M., Pajevic, S., Plenz, D. — Wed, 04 Nov 2026 00:00:00 +0000

94% · Computational models 94%, Neural coding 53%

In cellular automata, localized activity patterns called gliders propagate and interact to carry and transform information. Whether neuronal networks can harness this computational power remains unclear. Here, we demonstrate that a cortical network model with separate excitatory and inhibitory populations, graded cellular activity and uniform local connectivity spontaneously generates diverse, interacting gliders. An analytical reduction identifies the cellular conditions sustaining propagation of the dominant G-glider. Distinct gliders can assemble into growing structures that emit other glider types. Near excitation/inhibition balance, sparse glider dynamics coexist with high computational capacity, measured by input differentiation and generalization, with local capacity varying across space and time. Strong mutual inhibition between interneurons instead permits metastatic proliferation with low computational capacity. These findings connect cellular automata to neuronal information processing and identify interacting gliders as a candidate mechanism for dynamic plasticity: the reorganization of computation within an unchanged synaptic architecture.

PHASOR: An Open-Source Pipeline for Hidden Markov Model Analysis of Stimulus-Locked Retinal Multielectrode-Array Population Activity

Shaik, M. J., Chen, S., Jin, J., Babino, D. — Wed, 04 Nov 2026 00:00:00 +0000

93% · Methods 93%, Neural coding 62%

Multielectrode-array recordings of the retina reveal neural population activity at cellular resolution, but relating recurring population states to stimulus timing and single-unit responses requires several linked analysis steps. We present PHASOR (Population HMM Analysis of Stimulus-locked Output in the Retina), a pipeline that combines stimulus-clock alignment, Fourier-based response classification, hidden Markov modeling with Chow-Liu tree emissions, model-order selection, and state visualization. We evaluated the workflow with synthetic populations and demonstrated its use in ten ex vivo recordings from three wild-type retinas (two male, one female) and one male rd1 retina, including 2 Hz full-field sinusoidal stimulation. Across ten synthetic datasets containing seven generative states, selected model orders ranged from seven to nine, with a mean of 7.9, matching the generative count exactly in three of the ten datasets. A representative confusion matrix showed a near one-to-one correspondence between generative and decoded states, with a single additional low-occupancy state, illustrating the distinction between recovering temporal structure and recovering the exact state count. In a representative retinal recording, response classes differed in stimulus phase and contrast dependence, and decoded states differed in occupancy, dwell time, and active-unit composition. Recordings during L-AP4 application, without projected stimulation, and in a photoreceptor-degenerate rd1 retina illustrated changes in the resulting population-state descriptions. PHASOR integrates established analysis methods into a reusable workflow for relating stimulus-associated population activity to functional response classes. The selected states provide a descriptive model of population activity; their number and correspondence to biological assemblies require separate validation.

Fast rate coding in heterogeneous spiking networks

Smeets, K., Wang, S., Schmutz, V., Gerstner, W. — Wed, 04 Nov 2026 00:00:00 +0000

93% · Computational models 93%, Neural coding 81%

A network of spiking neurons is said to operate in the rate coding regime if its dynamics can be accurately described by a continuous rate network. But how can discrete spike-based communication between neurons give rise to continuous rate-based dynamics? Computational studies showing that heterogeneous spiking networks can approximate the dynamics of recurrent neural networks (RNNs), a special case of rate network, assume that spikes are approximately Poisson or that network dynamics are slow. Here, we show that neither assumption is necessary: heterogeneous networks of non-Poisson neurons can produce fast rate-based dynamics. To demonstrate this, we show that signal transmission in feedforward networks of integrate-and-fire-type neurons can be described by a history-dependent rate network that is asymptotically exact as network size grows, even if heterogeneity persists, while linear-nonlinear rate networks fail. By providing a constructive model of fast rate coding in heterogeneous networks, this work lays the foundation for extending rate-based analyses of neural recordings beyond the assumption of slow dynamics or Poisson rate codes.

Graph neural network approach for inferring neuronal connectivity

Gupta, A., Neveu, C. L., Byrne, J. H. — Wed, 04 Nov 2026 00:00:00 +0000

90% · Methods 90%, Computational models 55%

Understanding the connectivity of neuronal networks is essential for understanding brain function. Current anatomical mapping techniques, such as serial section microscopy and viral tracing, are resource-intensive and cannot capture the efficacy of connectivity. A complementary approach is to infer effective connectivity from single-neuron-resolution population activity. We developed a graph neural network (GNN)-based tool, CircuitGNN, that accurately infers neural effective connectivity from population activity. CircuitGNN accurately inferred the connectivity of several small circuits and detected both the strengthening and weakening of connections, such as those that occur during learning. CircuitGNN outperformed other methods when applied to simulated population activity generated by an invertebrate central pattern-generating circuit and a cortical microcircuit. CircuitGNN was evaluated on networks of varying size, recording duration, and proportion of hidden neurons. Although performance improved with longer recordings, CircuitGNN achieved high precision with recordings as short as 4 min, scaled to large circuits of up to ~750 neurons, and remained robust when 20% or 40% of neurons were hidden. These results establish CircuitGNN as an accurate and robust technique for inferring neural connectivity from activity.

A Bayesian Optimized Biquad Filter-Burg Model for Real-Time EEG Phase Estimation

Wed, 04 Nov 2026 00:00:00 +0000

90% · Methods 90%

Real-time phase estimation on stimulus-aligned transient EEG is critical for closed-loop neuromodulation systems targeting the theta-band, yet most real-time phase estimation methods have been developed and validated on resting-state, high-fidelity signals in the alpha band. Recent work has pursued deep learning architectures, while individualized classical bandpass filter tuning remains underexplored. This study characterizes the spectral and temporal properties of non-anticipative bandpass filtering and demonstrates the performance gains achieved through individualized tuning. We present a Bayesian-Optimized Biquad Filter-Burg (BOB) model evaluated on EEG recorded during peripheral vibrotactile stimulation of the left median nerve (theta, N=20 healthy subjects) and on resting-state recordings from the Max Planck Institute's LEMON dataset (alpha, N=20 healthy subjects). BOB was benchmarked against the Deep Dual-Branch Complex Neural Network (DBCNN), EEG Phase Prediction Network (EPN), Phastimate, and Phase Estimation with Autoregressive Padding (PEAP) under matched testing conditions: channel selection, individualized optimization, evaluation time points, and an offline reference phase obtained through zero-phase filtering and the Hilbert transform. Under these testing conditions, BOB achieved matched or better performance than these models at a fraction of the calibration time.

★ Recurrent Spiking Neural Networks for Continuous Motor Control: A Biologically Plausible Model of Motor Cortex

Imamura, H., Imamura, F., Igarashi, J., Isomura, Y., Hira, R. — Mon, 02 Nov 2026 00:00:00 +0000

96% · Computational models 96%

Motor cortex acquires skilled movements through recurrent spiking dynamics and experience-dependent synaptic plasticity, yet how such circuits learn continuous motor control using biologically local signals remains unclear. Here, we develop a recurrent spiking neural network model of motor cortex that addresses both the computational and biological requirements of motor learning. We first analytically derived the recurrent firing-rate state directly from synaptic input and trained the network by implicit differentiation, thereby avoiding backpropagation through long spike trajectories. We then introduced State-Carry Initialization, which enables closed-loop motor control by continuously carrying the ongoing recurrent state across successive actions. Building on this formulation, we devised a learning rule termed Feedback-Spike Nudging (FSN), in which teaching signals are delivered as spikes through a feedback pathway. Synaptic changes depend only on presynaptic activity and the feedback-evoked change in postsynaptic firing, consistent with biological mechanisms of synaptic plasticity in motor cortex. The resulting networks learned continuous motor control through both supervised and reinforcement learning, which could be mapped respectively onto interactions of motor cortex with the cerebellum and basal ganglia. Moreover, the FSN learning rule was consistent with experimental observations of structural spine plasticity in mouse motor cortex. Thus, our study lays a foundation for recurrent spiking models of motor cortex that are biologically grounded at both the functional and anatomical levels.

★ Multi-region two-photon calcium imaging in freely moving animals

Zong, W., Ma, S., Arbaciauskaite, J., Saxena, R., Camara, A. L., Jenssen, K. J. — Thu, 29 Oct 2026 00:00:00 +0000

97% · Methods 97%

Complex behaviors emerge from the coordinated activity of neural circuits distributed across widely separated brain regions. Existing optical approaches for monitoring neural activity lack the combined capability to record from anatomically distributed regions with high spatiotemporal resolution in freely moving animals. Here we introduce MINI2P-II, a 1.1-g 2P miniscope that achieves large-scale calcium imaging with high spatial resolution, deep imaging depth and dual-color imaging capability. Together with a dual-scope imaging system and a novel implantable periscope lens assembly, MINI2P-II enables simultaneous 2P imaging in widely separated or anatomically adjacent brain regions. In mice, we demonstrate inter-hemisphere coordination in bilateral motor cortex during climbing, egocentric boundary coding in simultaneous retrosplenial and posterior parietal cortex recordings, and in zebra finches, we showed large-scale behavior-dependent activity in both juvenile and adult individuals. MINI2P-II opens the door to multi-region 2P imaging across species and developmental stages in freely moving animals.

Reward changes the use and structure of predictive spatial maps

Oza, S., Cohen, A., Wager, G., Derdikman, D., Recanatesi, S. — Thu, 29 Oct 2026 00:00:00 +0000

90% · Computational models 90%, Neural coding 66%

Reward-related changes in hippocampal place-cell activity may reflect either different use of an existing spatial map or plasticity that reshapes the map. We separated these contributions in a two-pathway model: a recurrent network learned to predict sensory input and developed a spatial representation, while a reinforcement-learning controller combined this representation with an explicit goal signal to guide behavior. Replaying identical trajectories before and after learning isolated representational plasticity. Goal-directed behavior strongly concentrated place fields near reward, whereas changes to the representation were smaller and more variable. A reward-proximity teaching signal made these changes larger and more consistent, and the change was strongly attenuated when recurrent plasticity was restricted. After reward relocation, goal-related directional tuning adapted more fully than place-field organization, while withdrawing reward and proximity teaching reversed the learned field concentration. Thus, reward affects predictive spatial maps through separable mechanisms: goal information changes how an existing map is used, whereas reward-related teaching signals reshape the map itself. Our results provide a concrete framework for future experiments to disentangle how reward information, plasticity and behavior jointly shape hippocampal activity.

Scalable hardware and software for high-density, multi-regional electrophysiology in non-human primates

Dubey, A., Xu, Q., Khazali, M. F., Choi, J., Wingel, K., Haggerty, J. — Wed, 28 Oct 2026 00:00:00 +0000

94% · Methods 94%

Understanding how distributed neural circuits generate complex behaviors requires long-duration recordings from populations of individual neurons across multiple brain regions. High-density Neuropixels probes have revolutionized rodent systems neuroscience. Extending these capabilities to non-human primates (NHPs) for chronic, multi-regional recordings requires compact, implantable microdrive hardware and reliable automated spike-sorting software. NHPs provide access to complex and flexible behaviors that are difficult to study in other model systems. Extending the Neuropixels revolution to NHPs therefore depends on integrating hardware and software for long-duration, multi-regional recordings. Here, we present a scalable hardware-software platform co-designed for high-density neural data acquisition and reliable single-unit identification in NHPs. The Multi-regional Implantable NeuroDrive (MIND) is a lightweight, implantable microdrive that provides stable recordings from multiple independently positioned Neuropixels probes and is designed to support chronic, ambulatory, head-free recordings. We paired MIND with UnitAssay, an automated post-sorting curation framework that identifies and curates characteristic spike-sorting errors with minimal computational cost. Together, MIND and UnitAssay provide an integrated platform for implanted, multi-regional recordings in NHPs performing complex and naturalistic behaviors. We tested MIND and UnitAssay across parietal, prefrontal, and motor cortices in rhesus macaques performing reaching and eye-movement tasks. MIND enabled stable multi-probe recordings across cortical regions and durations. UnitAssay enabled automated curation of large-scale spike-sorting output and improved reliable single-unit identification relative to Kilosort 4 labels and standard SpikeInterface quality control. Together, MIND and UnitAssay provide a scalable solution for high-density, multi-regional, long-duration electrophysiology in NHPs.

Focal neuronal loss and cell replacement in a connectome model of fly steering

Habib, P., Petr, M., Rust, R. — Wed, 28 Oct 2026 00:00:00 +0000

93% · Computational models 93%

Focal neuronal loss after stroke, traumatic brain injury or spinal cord injury can disrupt circuits and result in persistent motor deficits. Which cells and connections must be restored to recover a specific behavior is difficult to establish experimentally. Here we use a connectome-constrained spiking model of the fruit-fly nervous system and decode motor-neuron output to simulate visually guided steering. After loss of 4,000 neurons, path straightness ranged from 0.04 to 0.95 depending on lesion location. At a fixed sensorimotor site, a 2,000-neuron lesion reduced simulated displacement to 6.5% of intact. Across 100 stochastic simulation realizations of the connectome, replacing 100 cells with their original connections increased displacement to 62.5% of intact when motor-control pathways were prioritized, compared with 10.1% after random selection of 100 cells and 48.5% after random selection of 1,000 cells. When connections were instead inferred from cell type, the same 100 intended replacements reached 60.0% of intact with matched identities and 6.7% with non-target identities, indicating that cell identity and type-dependent integration are required for recovery. Our model generates testable predictions for genetic ablation experiments in fruit flies and identifies candidate cellular and circuit requirements for motor repair that can inform cell replacement therapies and future models of mammalian locomotion.

Temporal smoothness constraints on efficient neurobiological codes imply temporal specialization

John J. Vastola, Samuel J. Gershman — Wed, 28 Oct 2026 00:00:00 +0000

91% · Computational models 91%, Neural coding 69%

Neurobiological circuits may be energy efficient in part because there is a division of labor: different subsystems compute or represent different things. In light of this, it is interesting that a major difference between subsystems is the time scale on which they typically vary. Here, we argue that temporal smoothness constraints---the idea that it may be energetically costly for systems to vary much more quickly than their typical time scale---imply a certain division of labor with respect to temporal stimulus features. In particular, slow subsystems ought to represent slowly varying stimulus features, and fast subsystems ought to represent quickly varying features. We formulate a novel efficient coding model that we use to investigate this claim, and exactly solve for the optimal division of labor. In addition to finding that slower subsystems ought to represent more slowly varying temporal features, we find surprising structure of mathematical interest: optimal codes have subsystems which represent orthogonal temporal features, and which correspond to the eigenfunctions of a Sturm-Liouville problem.

MtReNIRS: A Self-Supervised Foundation Model for Transferable Neurovascular Representations

Tony Chae, Kleanthis Avramidis, Dimitra Emmanouilidou, Ivan J. Tashev — Wed, 28 Oct 2026 00:00:00 +0000

91% · Methods 91%

Functional near-infrared spectroscopy (fNIRS) offers a promising approach for monitoring the hemodynamic correlates of neural activity in naturalistic settings. However, existing modeling methods for fNIRS are largely confined to small-scale, single-session data with low sampling rates, driven by high heterogeneity in acquisition devices and optode montages. In addition, the complex spatiotemporal structure of fNIRS signals further restricts model scalability and transferability across settings. As a result, it remains unclear whether a single fNIRS encoder can learn representations that are usable across subjects, tasks, devices, and montages. Here, we show that a montage-aware masked autoencoder, pretrained on 12 public fNIRS datasets spanning 268 subjects and 567 recording hours, learns generalizable neurovascular representations that transfer across three external datasets. Our fNIRS foundation model, MtReNIRS, jointly encodes the slow hemodynamic nature of fNIRS, chromophore identity and cortical location by mapping heterogeneous montages onto a shared anatomical reference. MtReNIRS achieves cross-subject performance on par or better than fully supervised baselines and excels in few-shot within-subject calibration, where it outperforms prior state-of-the-art fNIRS models, while remaining robust to sparse montages. Our results indicate that montage-aware self-supervision yields physiologically grounded, transferable fNIRS representations, providing a scalable foundation for label-efficient brain-computer interfaces.

p-Adic statistical field theory of fluctuation effects in hierarchical neural networks

W. A. Z\'u\~niga-Galindo — Wed, 28 Oct 2026 00:00:00 +0000

91% · Computational models 91%

The Buice-Cowan field theory [PRE 75, 051919 (2007)] describes fluctuations and correlations of neural activity through a path integral whose mean-field limit is the Wilson-Cowan equation and whose critical behavior belongs to the directed percolation class. In its continuum limit the neurons are the points of a Euclidean space and activity spreads by ordinary diffusion, which encodes neither the hierarchical organization of cortical networks nor their small-world property. We formulate a version of the theory in which the neurons are organized in an infinite, self-similar, tree-like structure, described by the p-adic numbers. The path integral carries over unchanged, and finite networks are exact restrictions of the continuum theory. The main change is in the linear response: in the Euclidean theory every short-range kernel reduces to the Laplacian, while in the p-adic theory each connectivity kernel gives its own nonlocal p-adic diffusion operator. For couplings that decay by a fixed factor per level of the hierarchy this is the Taibleson-Vladimirov operator, and the critical theory is an ultrametric Reggeon field theory whose upper critical dimension is twice the decay exponent of the coupling. Hence the decay of the coupling up the hierarchy, and not the dimension of space, decides whether mean-field exponents apply. We obtain a Ginzburg-type criterion, loop-corrected hierarchical Wilson-Cowan equations, log-periodic corrections to scaling and power-law correlations. Exact simulations on a tree support the predictions: the avalanche-size exponent takes the mean-field value 3/2 when the coupling decays slowly and decreases toward the directed-percolation value when it decays fast. The hierarchical network is also small-world when the decay exponent is smaller than the dimension, a property that the Euclidean theory obtains only by assuming long-range coupling.

Heterogeneous plasticity rules generate diverse structures in recurrent networks

Cusseddu, C., Festa, D., Miehl, C., Gjorgjieva, J. — Wed, 28 Oct 2026 00:00:00 +0000

90% · Computational models 90%

How do local changes at individual synapses give rise to structured recurrent connectivity? Experiments have revealed diverse forms of spike-timing-dependent plasticity (STDP) across the brain, making it difficult to predict which circuit structures different rules will generate. We developed an analytical framework mapping excitatory Hebbian, anti-Hebbian, and symmetric STDP rules to directed three-neuron motifs. Individual homogeneous rules generate restricted subsets of motifs, and no single rule produces all motifs. Combining different rules generates motifs inaccessible to individual rules and yields the full motif repertoire, and these predictions generalize to larger recurrent networks. The mapping is nevertheless highly non-unique, with distinct rules and parameters producing similar motif distributions, including distributions compatible with experimental connectivity. Thus, circuit structure constrains candidate plasticity rules but does not uniquely identify them. Finally, networks with similar motif distributions can nonetheless differ in their firing rates and pairwise correlations, suggesting that activity may provide an additional constraint on candidate plasticity rules.

Every route counts, but some count more: a max-plus view of the C. elegans brain

Wed, 28 Oct 2026 00:00:00 +0000

90% · Computational models 90%, Methods 62%

Brains do not simply add up every signal they receive: they select which routes dominate. Yet connectomes are usually analyzed with tools that sum over all paths and place neural states in Euclidean spaces, blurring the feedback circuits that may sustain internal states. Here we treat neural signaling as routing instead of diffusion and describe it with max-plus algebra, in which signals accumulate along a path and the strongest route prevails. We propose a model in which the functional connectome of Caenorhabditis elegans emerges from the time-varying combination of electrical, synaptic, monoaminergic, neuropeptidergic and hormonal layers, and analyze five networks within this framework. Each collapses onto a handful of dominant feedback loops, which recover well-characterized circuits and point to new candidates for experimental study. Unlike linear spectra, these structures keep the biological meaning of the data, and their order-based description suggests that brain states live in lattices rather than Euclidean spaces.

★ Mathematical model for visual cortical development based on molecular mechanisms underlying the generation and elimination of dendritic spines

Tanaka, S., Miyashita, M. — Tue, 27 Oct 2026 00:00:00 +0000

96% · Computational models 96%

In the developing brain, neural circuits are elaborated by the activity-dependent rewiring of synaptic connections. Our earlier self-organization model, which was built on the assumption that axon terminals compete with each other to form synaptic contacts to single dendritic spines, successfully reproduced spatiotemporal receptive fields and orientation-direction joint maps (Tanaka et al., 2020). It has been reported that most synaptic contacts are en passant types rather than axonal terminals (Turegano-Lopez et al., 2024). This finding contradicts our assumption that each axon terminal selects an appropriate dendritic spine. Recent microscopic observations have shown that dendritic spines are more motile than presynaptic boutons (Bonhoeffer and Yuste, 2002; Deng and Dunaevsky, 2005). In addition, the current view of synaptic rewiring in early development is that filopodia spontaneously generated along dendrites transform into spines by establishing contacts with neighboring axonal boutons to form synapses whereas spines of inappropriate synapses are eliminated in an activity-dependent manner (Deng and Dunaevsky, 2005). In this theoretical study, we mainly focus on the processes of spine generation, maturation, and elimination to rebuild a mathematical model of synaptic rewiring, incorporating recent experimental findings on actin polymerization and depolymerization (Ridley, 2006) under the activation of Rho-GTPases (Mackay and Hall, 1998) mediated by the binding of variant forms of brain-derived neurotrophic factor (BDNF) (Binder and Scharfman, 2004; Lesmann and Brigadski, 2009; Kuczewski et al., 2009) to neurotrophin receptors such as tropomyosin kinase receptor B (TrkB) (Klein et al., 1989) and the low-affinity-nerve growth factor receptor (p75NTR) (Yamashita et al., 1999). TrkB receptors are dephosphorylated by chondroitin sulphate proteoglycans (CSPGs) (de Vivo et al., 2013) mediated by protein-tyrosine phosphatase sigma (PTPsigma (Kurihara and Yamashita, 2012) in an age-dependent manner. The model describes the generation or elimination of dendritic spines of visual cortical neurons under actin polymerization and depolymerization to establish appropriate synaptic contacts on afferent fibers of cells in the lateral geniculate nucleus. The simulations based on the new model reproduced not only simple-cell-like receptive fields (DeAngelis et al., 1999) and orientation-direction maps (Shmuel and Grinvald, 1996) but also a characteristic profile of the total number of synapses, which shows an early increase and a delayed decrease during the critical period (Li et al., 2006; White and Fitzpatrick, 2007). It is also demonstrated that whereas orientation selectivity is elevated almost monotonically throughout the critical period, the formation of direction selectivity is delayed behind the formation of orientation selectivity, which is likely promoted by the pruning of inappropriate synapses (Grigonis et al., 1988; Bourgeois et al., 1993; Elston et al., 2009; Faust et al., 2021).

★ High-dimensional hippocampus representation facilitates learning of low-dimensional abstract representation and supports generalization

Wang, B., Johnston, W. J., Fusi, S. — Tue, 27 Oct 2026 00:00:00 +0000

95% · Computational models 95%, Neural coding 73%

Semantic knowledge encompasses facts, concepts, and their relationships. Recent work has proposed that some forms of semantic knowledge could be encoded in low-dimensional neural representations, such as the abstract, disentangled representational geometries observed in many cognitive brain areas. Experimental work has shown that hippocampal (HPC) lesions impair the formation of flexible and generalizable semantic knowledge. However, HPC representations are often high-dimensional and sparse. How can these representations contribute to the formation of low-dimensional abstract representation across these cognitive cortical areas? Here, we reconcile these observations using multiregional neural network models. Modeling HPC as a sparse, decorrelating autoencoder, we show that its high-dimensional representations promote the robust and efficient formation of low-dimensional abstract representations in downstream regions such as prefrontal cortex. This effect is most pronounced when the geometry of sensory inputs is misaligned with the geometry of desired outputs, requiring a more complex input-output transformation. Mathematical analysis shows that hippocampal expansion recoding suppresses misleading low-dimensional input structure and reshapes the loss landscape of downstream networks, allowing abstract semantic geometry to emerge reliably. The resulting abstract representations provide an inductive bias for sample-efficient learning of novel semantic-to-action mappings. Finally, we show that consolidating hippocampus-dependent representations into a hippocampus-independent pathway improves out-of-distribution generalization based on input structure. Together, these results suggest a circuit mechanism by which high-dimensional hippocampal representations can support, rather than oppose, the emergence of low-dimensional abstract representations.

Sensorimotor behaviour from an untrained brain-and-nerve-cord connectome

Tue, 27 Oct 2026 00:00:00 +0000

92% · Computational models 92%

Connectomes now map the neurons and synapses of an adult fly's brain and nerve cord, but how much of behaviour follows from the wiring is unknown. Here we turn one such connectome into a spiking network of 172,433 neurons with a published brain model's untuned parameters, and run it alone and inside a simulated body. Two fixed rules join them: odours and looming objects drive sensory neurons, and motor neurons move the legs. Alone, the network passes a signal to the motor neurons it is known to drive when the pathway supplies about 2% or more of their input. In the body, the fly navigates around obstacles to 95% of the odour sources. A rewiring that keeps each neuron's connection counts and signs reaches 18%, and the fly's leg commands replayed with the senses off reach almost none, so navigation needs the measured wiring and sensory feedback. Dopamine-gated weakening of the mushroom body's output synapses turns approach to an odour into avoidance, which requires the pairing and is odour-specific once one olfactory synapse class is scaled. An untrained connectome thus supports a behaviour and its learned reversal, and the pathways it cannot drive mark what the diagram leaves out.

Whole-brain normative modeling across multiple functional connectivity metrics in MEG

Kobbersmed, J. R. L., Gohil, C., Woolrich, M. W., Vidaurre, D. — Tue, 27 Oct 2026 00:00:00 +0000

91% · Methods 91%

Normative modeling has recently emerged in structural and functional neuroimaging as a promising approach to characterize neuropathology at the individual level. Trained on large healthy cohorts, these models predict expected measurements from covariates such as age and sex while quantifying normal population variability. Individual measurements can then be compared against the model to identify deviations associated with pathology. While most normative models of brain function have been based on functional MRI, EEG and MEG offer more direct measurements of brain activity at higher temporal resolution. Despite the promise, how to adequately perform normative modelling at the whole-brain level on these modalities remains challenging. We here propose M/EEG-FUNCOIN to identify age- and sex-related dimensions of change in functional connectivity from a plethora of FC measures from electrophysiology: amplitude envelope correlation, phase-locking value, and time-delay embedding covariance. Using resting-state MEG data from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) dataset, we show that M/EEG-FUNCOIN can: (i) characterize age- and sex-related whole-brain functional connectivity patterns in greater detail than existing methods; and (ii) effectively capture meaningful deviations in connectivity in individuals that rank high and low in cognitive ability. Altogether, these results show great potential for applications in complex, network-level brain pathology.

★ Disentangling evoked and induced activity from single-trial neurophysiological time series

Ahola, O., Haxel, L., Ziemann, U., Ilmoniemi, R., Lioumis, P. — Mon, 26 Oct 2026 00:00:00 +0000

95% · Methods 95%

Evoked and induced responses are fundamental neurophysiological readouts in the development and assessment of treatments for disorders with impaired neural function. Distinguishing these responses is essential for interpreting single-trial variability, identifying the determinants of these responses, and enabling real-time probing of cortical responsiveness. However, because of dynamic overlap, separating evoked and induced activity remains unsolved at the single-trial level when instantaneous fluctuations of cortical responsiveness are of interest. We developed the Evoked-Ongoing/Induced Separator (EOIS), a framework for separating event-evoked, ongoing, and event-modulated induced activity from single-trial data. EOIS recovers evoked and ongoing/induced components by leveraging their distinct phase-consistency features in MEG and real-time EEG, enabling subject-agnostic single-trial time-series analysis and attribution of cortical response variability to specific components. By delivering component-resolved time series, EOIS enables biomarker refinement, pharmacodynamic readouts, and closed-loop neuromodulation, facilitating translational deployment across individuals and care settings.

★ `Silent' mitral cells emerge from common feature subtraction

Tootoonian, S., Kollo, M., Schaefer, A. T. — Mon, 26 Oct 2026 00:00:00 +0000

95% · Computational models 95%, Neural coding 78%

Whole-cell recordings from olfactory bulb mitral/tufted cells in awake mice revealed an inverse relationship between baseline activation and odour responses wherein cells that were spontaneously active tended to be inhibited by odours, while `silent' cells, quiet at baseline, tended to have vigorous odour responses. In this work we provide a normative explanation for these findings by extending previous sparse coding models of the olfactory system to include `common feature subtraction,' an odour-triggered variant of background subtraction in which the receptor contribution of features common to the odours being discriminated is subtracted. Using a circuit-level formulation of this computation consistent with the anatomy of the olfactory bulb we show how baseline activity cancels the effect of the common olfactory features on receptor activations, promoting inference of the target odours. We provide simulation results showing qualitative agreement with experimental findings. The model also predicts in closed form, without fitting, the slopes of the relationships between a cell's baseline and its common input and between its baseline and its odour response, and these predictions agree with simulations. Our model makes the experimentally testable prediction that a cell's baseline activation is not an intrinsic property, but reflects the negative of its recent activity.

Phase-based Edge Coupling: Modeling Overlapping Functional Architectures and High-Order Edge-to-Edge Interactions in EEG Networks

Chengcheng Wu, Zichen Shi, Jiayang Xu, Shanbao Tong, Xiaoli Guo — Mon, 26 Oct 2026 00:00:00 +0000

94% · Methods 94%, Computational models 39%

Network neuroscience has traditionally relied on node-centric approaches, which are inherently limited in capturing the brain's overlapping functional architectures and higher-order edge-to-edge interactions. The emerging edge-centric paradigm addresses this gap by shifting the analytical unit from nodes to edges. However, while edge-centric models have been established for DTI and fMRI, comparable EEG methodologies have yet to be developed. In this study, we proposed a Phase-based Edge-centric Coupling (PEC) algorithm for EEG network modelling, where edge time series are derived from instantaneous phase differences of EEG signals and edge-to-edge coupling is quantified via normalized inner product. Simulation tests demonstrated the robustness of PEC algorithm, achieving high accuracy (<10% relative error) in coupling estimation and discriminative capacity (AUROC>0.7) against spurious coupling.When provided sufficient data length(>=16 cycles), PEC outperformed traditional Phase Node-centric Coupling (PNC) in both individual identifiability and cognitive-state identifiability, enabling superior characterization of stable individual neural signatures and dynamic task-dependent functional configurations. Furthermore, unlike PNC's hard-partitioned modularity, PEC captured highly overlapping yet functionally specialized modular structures and connector hubs, aligning with task-engaged cognitive processes. Applied to dyadic interactions, PEC identified distinct cross-brain modules encoding symmetric and asymmetric neural coordination. These findings establish PEC as a valid and powerful tool for EEG network modelling, providing new insights into the brain's complex, multifunctional organization at both intra-brain and inter-brain networks.

Reduced-observable dynamic mode decomposition for identifying theta and beta activity in working-memory EEG

Chavez-Huerta, D., Khosravi, M. — Mon, 26 Oct 2026 00:00:00 +0000

93% · Methods 93%

The analysis of transient oscillatory activity in neural recordings remains challenging for linear methods, particularly when signals are nonlinear, noisy, and distributed across multiple channels. This paper introduces a Reduced-Observable Extended Dynamic Mode Decomposition (RO-eDMD) algorithm using a Random Fourier Features dictionary to characterize high-dimensional data in electroencephalography (EEG) recordings. The method extends the Koopman operator framework by incorporating rank reduction directly in the observable space and employing a randomized feature map that approximates the Gaussian kernel, enabling the capture of transient components without explicit dictionary optimization. To assess the method and demonstrate applicability, a logistic regression model is trained using a working memory paradigm as a test case, using dominant power values extracted via RO-eDMD in the theta band as a predictor and the type of event (encoding vs.\ fixation) as the dependent variable. We find correlations between oscillatory power and memory encoding events in the analyzed bands. Additionally, we find an increase in correlation associated with theta power and memory load, replicating similar results in the literature. The presented algorithm offers a computationally efficient, interpretable framework applied to a working memory paradigm dataset, but is relevant to the analysis of neural time series from diverse experimental contexts.

★ iFLiP: accessible and low-cost lifetime fiber photometry for robust longitudinal and quantitative measurements of neural dynamics

Yahiro, T., Chen, Y., Bayless-Edwards, L., An, S., Mao, T., Zhong, H. — Fri, 23 Oct 2026 00:00:00 +0000

96% · Methods 96%

Fluorescence lifetime measurements overcome the limitation of widely used intensity-based measurements in quantifying tonic activities and longitudinal changes in neural dynamics. However, while lifetime sensors are rapidly emerging, their applications have been limited by the complexity and expense of required hardware and software. Here, we report a simple, low-cost lifetime fiber photometry system, named iFLiP, that uses entirely off-the-shelf products and can be assembled without specialized expertise. We also develop software that enables turn-key operation. The integrated system can accurately monitor lifetime changes at up to 5000 Hz for days. We demonstrate the utility of iFLiP in monitoring the activity of protein kinase A (PKA) in vitro and in vivo, and uncover striatal subregion- and cell-type-specific PKA dynamics elicited by locomotion in mice. Additionally, multi-day continuous measurements in the basal forebrain reveal previously unappreciated PKA activity associated with sleep/wakefulness. Overall, iFLiP may make lifetime measurements broadly accessible in neuroscience research.

Localising the Seizure Onset Zone from Routine Interictal and Ictal Scalp EEG Via a Factorised Spatiotemporal Ensemble Deep Learning Framework

Shi, S. — Fri, 23 Oct 2026 00:00:00 +0000

93% · Methods 93%

Background: Drug-resistant focal epilepsy requires precise localisation of the epileptogenic zone (EZ) for surgical resection. Current practice depends on invasive recordings or spontaneous seizure capture, a costly and risky bottleneck. Interictal scalp EEG can exhibit localising features like spikes, but scalp EEG suffers from volume conduction (the smearing of brain signals across multiple electrodes) and low signal-to-noise ratio (SNR), which obscure subtle spatial dynamics. Neurophysiologist review of long, often multi-day recordings is time-consuming and expertise-dependent, and while computational methods exist, they tend to rely heavily on human-designed features. Deep learning for EEG localisation could therefore help to improve clinical efficiency and sensitivity. Objectives: To develop and validate a non-invasive deep learning approach for lobar-level localisation of the seizure onset zone (SOZ), the primary electrophysiological proxy for the epileptogenic zone (EZ), using interictal and ictal segments from routine scalp EEG. The model is trained on expert-annotated clinical labels and is intended to augment, not supplant, clinical judgement in EZ delineation. Methods: We curated 262 sessions (6,000+ hours) from 100 patients with unilateral focal epilepsy, tested with an extra 28-patient internal and a 9-patient external dataset. The framework integrates: (1) an online augmentation pipeline (Gaussian noise, amplitude scaling, temporal cropping, and hemispheric channel swap) to mitigate overfitting. (2) a multi-scale temporal block for 20-200 ms discharge morphologies; (3) a factorised spatiotemporal Transformer decoupling temporal and spatial attention to counter volume conduction; and (4) a hemispheric mutual exclusivity penalty encoding unilateral priors. A five-model heterogeneous ensemble, fused via model soup, was evaluated on held-out, seizure-free, and cross-institutional cohorts. Results: The ensemble achieved 88.46% session-level exact match on internal test data, outperforming EEGNet (50.00%). Performance generalised to the seizure-free holdout set (82.76%) and to cross-institutional data (84.62%). Rare occipital class (RO) recall rose from 0.500 to 1.000, with zero false positives on healthy controls (n=108). On unlabelled random segments, accuracy was 66.67% (vs. 14% chance). A full 24-hour recording was processed in 4.26 minutes, with inference under 8 seconds, highlighting localising windows for targeted expert clinical review Conclusion: Clinically actionable SOZ spatial information is encoded in routine interictal scalp EEG. Our factorised ensemble provides a safe, interpretable, and efficient tool to expedite presurgical evaluation, potentially reducing reliance on prolonged monitoring and invasive implantation.

★ The limbic navigation system as a hierarchical RNN

Ji, Z., Zhang, H., Gorantla, K., Burgess, N. — Thu, 22 Oct 2026 00:00:00 +0000

97% · Computational models 97%, Neural coding 54%

Path-integration-trained recurrent neural networks (RNNs) can reproduce grid-like and head-direction-like representations, but most existing models rely on low-dimensional velocity inputs and do not capture the hierarchical organisation of the brain's self-localisation circuit. We introduce a hierarchical recurrent neural network (HRNN) that separately processes angular velocity and linear speed to perform simultaneous angular and positional path integration. Trained on these tasks, the HRNN generalises to longer timescales than seen during training and develops neural response properties resembling all the key cell types: angular velocity cells, head direction cells, speed cells, conjunctive and pure grid cells. The learned circuit also recovers key motifs of the navigation system, including attractor dynamics and asymmetric connectivity patterns. Perturbation analyses reveal functional information flow across the hierarchy, consistent with experimental observations. Finally, introducing theta-paced mutual inhibition between two learned angular-velocity subpopulations enables the HRNN to reproduce the bidirectional theta sweeps observed in empirical data. These results establish hierarchical task-driven RNNs as a biologically interpretable framework for linking neural representations, circuit motifs, and population dynamics in spatial navigation, while enabling targeted in silico perturbations and the generation of experimentally testable predictions.

Generalized prefrontal task-progress sequences efficiently encode task structure

Tarahi, O., Koshy, K., Sauer, J.-F. — Thu, 22 Oct 2026 00:00:00 +0000

91% · Neural coding 91%

The medial prefrontal cortex (mPFC) occupies a central role in executive function including decision-making, rule learning and cognitive flexibility. Here, we investigated how mPFC populations represent the animal's progression along sequential task stages. Freely moving mice performed an odour-guided two-choice task requiring sensory sampling, action selection, navigation, and reward collection. Population activity in the mPFC formed sequences that evolved through cue sampling, movement towards the reward sites, and reward-related epochs. These sequences exhibited a shared temporal organization across trial types, pointing to a generalized encoding of task progress by prefrontal neurons. Artificial neural networks trained on the same task developed task-progress sequences, and experimentally disrupting task-progress tuning in these networks impaired learning efficiency. Our results indicate that mPFC activity organizes behavioural episodes along correlated trajectories and suggest that task-progress tuning offers an efficient solution for the encoding of task space during multi-stage behaviour.

Development of py-mea-flow for comprehensive analysis of multi-electrode array data and its application to LGI2-deficient neurons

Han, Z. Z., Deane, J. E. — Wed, 21 Oct 2026 00:00:00 +0000

94% · Methods 94%

Multi-electrode arrays (MEAs) generate mid- to high-throughput electrophysiological data widely used to characterise neural network activity in vitro. Downstream analysis of recordings from Axion Biosystems instruments, however, has long depended on the manufacturers closed-source pipeline (AxIS Navigator, NeuralMetric Tool, AxIS Metric Plotting Tool), limiting customisation and integration into flexible Python workflows. We present py-mea-flow, an open-source Python package providing end-to-end analysis of Axion Biosystems spk binary files. Our package enables spike extraction, inter-spike interval burst detection, network burst detection and spike synchrony. We also provide tools for preparation of publication-ready visualisations including raster plots with spike-detection-rate histograms, plate-layout heatmaps, time-course trajectories, single timepoint violin plots and statistical comparisons. Outputs reproduce the manufacturers NeuralMetric Tool reference with near perfect accuracy in all benchmarked metrics. A mea-flow command-line interface enables batch processing without writing Python code. As a proof of concept, we applied py-mea-flow to human iPSC-derived cortical neurons with CRISPRi-mediated LGI2 knockdown. This revealed a reproducible network hyperexcitability phenotype in which burst and network burst frequencies were elevated while spike synchrony remained unchanged. Availability and implementation: The package is distributed on PyPI (pip install py-mea-flow), and version-controlled at https://github.com/Molecularbiologyworld/py-mea-flow. User feedback can be provided via the GitHub issues tab.

★ A Mechanistic Theory Unifying Value and Schema in the Prefrontal Cortex

Warren, J., Jensen, K. T., Behrens, T., Whittington, J. C. — Tue, 20 Oct 2026 00:00:00 +0000

95% · Computational models 95%

Animals exhibit behavioural flexibility by making reward-optimal decisions in unfamiliar situations. Computing value from minimal reward feedback requires leveraging known structure in the world. The Prefrontal Cortex (PFC) plays a pivotal role in both value computation and learning world structure, however mechanistic explanations for each---within the frameworks of Reinforcement Learning and Cognitive Maps---are largely distinct. We develop a mechanistic model of PFC that unifies value and structure through behavioural flexibility. In this model, value acts as a control signal that gates structured working memory representations of the current context in order to ultimately control behaviour. This is a significant departure from the conventional perspective of value, in which it is used for action readout. Critically, this model generalises to novel contexts without the need for additional synaptic plasticity. We demonstrate that recurrent neural networks (RNNs) meta-trained with either reinforcement learning or supervised learning recapitulate this exact model on various value-based tasks. These results not only provide a parsimonious explanation of both the value and schema frameworks of PFC function, but also propose a new mechanistic interpretation of the role of PFC in flexible decision making.

★ NeuroPilot: Longitudinal intravital neural imaging across scales and modalities through an agentic platform

Zhang, G., Luo, Y., Ju, F., Liu, D., Zhang, D., Guo, C. — Tue, 20 Oct 2026 00:00:00 +0000

95% · Methods 95%

Longitudinal intravital imaging is essential for understanding how neural populations evolve across behavioral states, yet converting large-scale recordings into biological insight remains a fundamental challenge because neural signals are degraded by noise, non-rigid motion, and heterogeneous recording conditions. Here we introduce NeuroPilot, an agentic framework that transforms neural imaging analysis into a closed-loop, context-aware process, integrating a multimodal large language model with callable computational imaging modules to adaptively coordinate workflows from raw recordings to population-level readouts. Across heterogeneous imaging and experimental regimes, NeuroPilot restores motion-degraded recordings and supports consistent neural signal recovery, detection, and tracking in challenging deep-brain, freely moving, and longitudinal settings. These enable neuronal population tracking across spinal stimulation paradigms, deep-brain imaging, and continuous 24-hour mPFC recordings aligned with spontaneous sleep-wake behavior. By converting heterogeneous intravital recordings into stable population activity maps, NeuroPilot supports behavioral decoding and reveals structured neural dynamics across behavioral states and transitions. NeuroPilot establishes a scalable agentic paradigm for neural imaging analysis, expanding experimental access to the measurement, visualization, and interpretation of neural population activity.

Cellular-resolution Three-photon Ultra-Deep Imaging (TrUDI) of neuronalpopulation activity at 2 mm depth in the mammalian brain

Santra, K., Logiacco, F., Frechou, A., Barber, K., Bellafard, A., Liou, J.-y. — Tue, 20 Oct 2026 00:00:00 +0000

92% · Methods 92%

Extending the depth reach of multi-photon microscopy for imaging neuronal population activity in the scattering mammalian brain is an exponentially hard problem. Three-photon (3p) microscopy has enabled imaging of neuroactivity at depths up to ~1.2 mm in the mouse brain. However, the fundamental depth limit of 3p imaging, as set by the depth at which the out-of-focus background fluorescence exceeds the in-focus fluorescence signal, is well beyond 2 mm. Reaching this fundamental depth limit in 3p imaging has been hampered by insufficient in-focus fluorescence signal arising from suboptimal optical design and acquisition conditions. Here, we demonstrate a Three-photon Ultra-Deep Imaging (TrUDI) system that approaches this fundamental depth limit via systematic and principled optimization of the in-focus fluorescence signal. TrUDI optimally matches the parameters of the excitation laser, the optical system, and the acquisition to linear and non-linear limits for safe laser exposure. At the same time, TrUDI remains compatible with the spatial and temporal requirements for single-cell resolution calcium imaging, and its parameters are tuned to the photo-physics of 3p excitation and the scattering properties of the mouse brain tissue. Our approach results in a ~250-fold increase in detected fluorescence photons at the same laser power as in previous approaches. We use TrUDI to demonstrate in vivo cellular-resolution recordings of neuronal population activity within fields of view of ~230 x 230 um2 at 8.6 Hz at tissue depths of up to ~2 mm in mouse cortex and hippocampus.