Neural Computation最新文献

{"title":"The Quasi-Systematic Nature of Splitter Cells.","authors":"Naomi Chaix-Eichel, Snigdha Dagar, Fréderic Alexandre, Thomas Boraud, Nicolas P Rougier","doi":"10.1162/NECO.a.1582","DOIUrl":"10.1162/NECO.a.1582","url":null,"abstract":"<p><p>During the past decades, hippocampal formation has undergone extensive studies, leading researchers to identify a vast collection of cells with functional properties. Several investigations, supported by carefully crafted models, have examined the origin of such cells. The most recent models hypothesize that temporal sequences underlie the observed spatial properties. We aim at investigating whether a random recurrent structure is sufficient to allow such latent sequence to appear. To do so, we simulated an agent with egocentric sensory inputs that must navigate and alternate choices at intersections. We were subsequently able to identify several splitter cells inside the model. Remarkably, when we systematically lesioned the identified splitter cells, the model's behavioral performance remained intact: in the vast majority of cases, new splitter cells reemerged through network reorganization, while in the remaining cases, the task was solved without any detectable splitter cells, demonstrating that splitter cells are not necessary to the task resolution. Position, orientation, and decision representations could also be successfully decoded from the reservoir activity, even after repeated lesioning. Subspace alignment analysis further revealed that this reorganization preserves the task-relevant population geometry while redistributing activity within the null subspace, with the trajectory-encoding dimension rotating in neuron space across lesions. Together, these findings demonstrate that splitter cell activity is primarily task driven and does not derive from a specific architecture or learning rule: splitter cells emerge generically across random recurrent networks that successfully solve the task, across a broad and robust range of dynamical parameters and are not necessary for task performance. Our results therefore challenge the notion of functional necessity for specific neural populations.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"2079-2110"},"PeriodicalIF":2.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Geometry-Aware Bio-Inspired Sparse Reservoir With Online Learning.","authors":"Alfio Borzì","doi":"10.1162/NECO.a.1578","DOIUrl":"10.1162/NECO.a.1578","url":null,"abstract":"<p><p>This article presents a cortically motivated framework for geometry-aware sparse reservoir computing with efficient online readout adaptation. The architecture is based on FitzHugh-Nagumo units coupled through sparse connectivity motifs informed by cortical statistics (reciprocity, triadic closure, and Dale's law), resulting in a structured and biologically plausible recurrent matrix. The readout weights are trained by means of an anchor-proximal ridge objective applied to streaming time windows, ensuring temporally stable online learning with a low memory footprint. It presents the results of numerical experiments on a temporal decision-making benchmark and an image-based long-range segmentation task. The proposed sparse reservoir, combined with the windowed proximal readout, is shown to achieve competitive accuracy while substantially reducing recurrent and state-storage requirements. A favorable trade-off of biological plausibility, predictive performance, and computational efficiency is thereby demonstrated.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1982-2007"},"PeriodicalIF":2.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Thousand Brains Theory 2.0: An Extension for the Long-Range Connections of the Neocortical Heterarchy.","authors":"Jeff Hawkins, Niels Leadholm, Viviane Clay","doi":"10.1162/NECO.a.1579","DOIUrl":"10.1162/NECO.a.1579","url":null,"abstract":"<p><p>Vernon Mountcastle hypothesized that the basis for intelligence in mammals is the replication of a general computational unit, the cortical column. The Thousand Brains Theory proposed that each column is a sensorimotor system, capable of learning structured models of objects by integrating sensory input over multiple movements. Previous papers on the Thousand Brains Theory focused on the computations that occur within individual cortical columns and how columns can use long-range connections to rapidly reach a consensus. However, several prominent long-range connection types in the neocortex were not addressed by the theory. These include hierarchical feedforward and feedback connections, as well as those that go through the thalamus. In addition, several theoretical requirements were not addressed. These include how the cortex learns compositional objects and how information is converted from the egocentric perspective of sensors to the allocentric perspective of models in the cortex. In this letter, we extend the Thousand Brains Theory to address these issues. We begin by reviewing the anatomy of long-range neocortical connections, arguing that they form a heterarchy, rather than hierarchy, which has made their functions challenging to understand through existing theoretical models. We then propose specific roles for each of these connections. First, the thalamus converts the orientation of features and movement information from an egocentric perspective to the allocentric perspective of learned models. Second, hierarchical feedforward, feedback, and cortico-thalamo-cortical projections enable columns to learn compositional models. We discuss the relationship of our proposals to experimental findings at the levels of anatomy, neurophysiology, and behavior, along with testable predictions for future experimental work. We conclude by discussing the implications of the extended Thousand Brains Theory for the design of artificial intelligence.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"2042-2078"},"PeriodicalIF":2.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shaping Slow Manifolds in Equivariant Recurrent Neural Networks.","authors":"Arianna Di Bernardo, Adrian Valente, Francesca Mastrogiuseppe, Srdjan Ostojic","doi":"10.1162/NECO.a.1577","DOIUrl":"10.1162/NECO.a.1577","url":null,"abstract":"<p><p>Recordings of increasingly large neural populations have revealed that the firing of individual neurons is highly coordinated. When viewed in the space of all possible patterns, the collective activity forms nonlinear structures called neural manifolds. Because such structures are observed even at rest or during sleep, an important hypothesis is that activity manifolds may correspond to continuous attractors shaped by recurrent connectivity between neurons. Classical models of recurrent networks have shown that continuous attractors can be generated by specific symmetries in the connectivity. Although a variety of attractor network models have been studied, general principles linking network connectivity and the geometry of attractors remain to be formulated. Here, we address this question by using group representation theory to formalize the relationship between the symmetries in recurrent connectivity and the resulting fixed-point manifolds. We start by revisiting the classical ring model, a continuous attractor network generating a circular manifold. Interpreting its connectivity as a circular convolution, we draw a parallel with feedforward convolutional neural networks. Building on principles of geometric deep learning, we then generalize this architecture to a broad range of symmetries using group representation theory. Specifically, we introduce a new class of equivariant recurrent neural networks, where the connectivity is based on group convolution. Using the group Fourier transform, we reduce such networks to low-rank models. This formulation gives us a low-dimensional nonlinear description that can be fully analyzed to determine the symmetry, dimensionality, and stability of fixed-point manifolds. Our results underline the importance of stability considerations: for a connectivity with a given symmetry, depending on parameters, several manifolds with different topology can coexist, some stable and others consisting of saddle points. Our framework unifies a variety of existing models and offers a principled approach to build new types of continuous attractor models of neural systems.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1913-1981"},"PeriodicalIF":2.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Dendritic Voltage Surrogate-Based Synaptic Learning Framework for Biophysically Detailed Neurons and Networks.","authors":"Gan He, Mengdi Zhao, Kai Du, Tiejun Huang","doi":"10.1162/NECO.a.1583","DOIUrl":"10.1162/NECO.a.1583","url":null,"abstract":"<p><p>A key challenge in biophysically detailed modeling is determining functionally effective synaptic weights. Here, we introduce a dendritic voltage surrogate that couples passive dendritic transfer impedances with the exact active currents computed from the original model, yielding explicit, differentiable approximations of membrane voltage. The resulting gradients can achieve effective synaptic learning in active dendrites under transient conditions. In detailed pyramidal neuron models with somatic bursting and dendritic calcium plateau, the surrogate enables reconstruction of subthreshold and bursting membrane potentials and supports supervised fitting of target responses. At network scale, we train a detailed 136-neuron model of Caenorhabditis elegans to match an experimentally measured whole-brain correlation matrix using a multi-GPU framework. Our approach links interpretable dendritic computation with scalable optimization, providing an effective synaptic learning framework for detailed neurons and networks.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"2111-2139"},"PeriodicalIF":2.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanism of an Intrinsic Oscillation in Rat Geniculate Interneurons.","authors":"Erica Y Griffith, Mohamed ElSayed, Salvador Dura-Bernal, Samuel A Neymotin, Daniel J Uhlrich, William W Lytton, J Julius Zhu","doi":"10.1162/NECO.a.1581","DOIUrl":"10.1162/NECO.a.1581","url":null,"abstract":"<p><p>Depolarizing current injections produced a rhythmic bursting of action potentials, a bursting oscillation, in a set of local interneurons in the lateral geniculate nucleus (LGN) of rats. The current dynamics underlying this firing pattern have not been determined, though this cell type constitutes an important cellular component of thalamocortical circuitry and contributes to both pathologic and nonpathologic brain states. We thus investigated the source of the bursting oscillation using pharmacological manipulations in LGN slices in vitro and in silico. We found that selective blockade of calcium channel subtypes revealed that high-threshold calcium currents IL and IP contributed strongly to the oscillation. In addition, increased extracellular K+ concentration (decreased K+ currents) eliminated the oscillation. In addition, selective blockade of K+ channel subtypes demonstrated that the calcium-sensitive potassium current (IAHP) was of primary importance. A morphologically simplified, multicompartment model of the thalamic interneuron characterized the oscillation as follows, First, the low-threshold calcium current (IT) provided the strong initial burst characteristic of the oscillation. In addition, alternating fluxes through high-threshold calcium channels and IAHP then provided the continuing oscillation's burst and interburst periods, respectively. This interplay between IL and IAHP contrasts with the current dynamics underlying oscillations in thalamocortical and reticularis neurons, which primarily involve IT and hyperpolarization-activated cation current IH, or IT and IAHP, respectively. These findings thus point to a novel electrophysiological mechanism for generating intrinsic oscillations in a major thalamic cell type. Because local interneurons can sculpt the behavior of thalamocortical circuits, these results suggest new targets for the manipulation of ascending thalamocortical network activity.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"2008-2041"},"PeriodicalIF":2.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Exact Gaussian Process Inference via Incremental Block-Matrix Identity Updates.","authors":"Brooks M Musangu, Jan Drugowitsch","doi":"10.1162/NECO.a.1592","DOIUrl":"https://doi.org/10.1162/NECO.a.1592","url":null,"abstract":"<p><p>Exact gaussian process (GP) inference scales cubically in data size, posing a severe bottleneck for applications that require repeated GP kernel inversions, such as time series models like gaussian process factor analysis (GPFA), where data size can vary across trials. To address this, we introduce an algorithmic variant of GPFA that shares computations across trials of different lengths. It does so by leveraging block-matrix identities to incrementally reuse inverses and determinants from smaller GP kernel matrices to accelerate computations on larger ones without sacrificing numerical precision. By casting successive trial-length extensions as Schur-complement updates, our method reduces the cost of each new inversion to depend only on the size difference among matrices. We demonstrate that this approach yields more than two times speed-ups over direct and persymmetric inversion baselines on both synthetic data sets and data from neural recordings. In an additional comparison to sparse inducing-point GPFA, sparse inference reduced inference time for small inducing sets at the expense of worse model fits, while optimizing inducing locations was slower than our exact GPFA variant when the optimization cost was included. We also provide a scikit-learn-compatible Python GPFA package, BlockInverseGPFA, and introduce a variance-explained metric for model evaluation.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-28"},"PeriodicalIF":2.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Inference Limits in Plastic Recurrent Networks Under Invasive Measurement.","authors":"Davide Anniballi","doi":"10.1162/NECO.a.1593","DOIUrl":"https://doi.org/10.1162/NECO.a.1593","url":null,"abstract":"<p><p>We derive a lower bound on the error of parameter inference from invasive measurement in recurrent adaptive networks with online plasticity. The bound decomposes into a variance contribution and a bias contribution, arising from physically distinct components of measurement back-action: stochastic fluctuations injected into the state dynamics and the systematic shift of the state operating point under repeated observation. Under generic measurement families, the bias term and the measurement-dependent component of the variance channel increase with measurement precision through distinct mechanisms and cannot generally be traded off against each other. Near marginal stability, both contributions are amplified by resolvent operators that diverge as their corresponding spectral radii approach unity, so that the bound becomes more restrictive in the regime in which cortical circuits are believed to operate. Evaluated at biologically realistic scale, the bound implies that the per-neuron disturbance required for noninvasiveness falls orders of magnitude below ambient biological fluctuations, making the constraint plausibly operative for standard invasive measurement modalities. Beyond the stationary regime, a finite-time analysis converts the bound into an experimentally relevant timescale, distinguishing the ballistic accumulation of measurement-induced bias from intrinsic representational drift and yielding testable temporal and geometric signatures for optogenetic and chronic-recording protocols. The result identifies a structural limit on parameter inference in adaptive recurrent systems, induced by the dynamical coupling between measurement, recurrent amplification, and online plasticity.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-55"},"PeriodicalIF":2.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Mechanisms of Gratitude Practice.","authors":"Elizabeth L Fisher, Lars Sandved Smith, Jakob Hohwy","doi":"10.1162/NECO.a.1591","DOIUrl":"https://doi.org/10.1162/NECO.a.1591","url":null,"abstract":"<p><p>Positive psychology interventions, such as gratitude practice, aim to improve well-being through simple techniques. One widely used practice involves reflecting daily on three things one is grateful for. Although this practice is promising, empirical evidence for its efficacy is mixed, and the underlying mechanisms remain poorly understood. Here, we develop a computational model of gratitude practice to gain insight into the potential mechanisms of the practice. Employing the active inference framework, we present three simulation studies. First, we formalize gratitude practice as the deliberate allocation of high precision (i.e., attention) to three positive observations. This allows us to show how agents form beliefs about their environment and how these beliefs shift following gratitude practice. Second, to link the model to empirical findings, we simulate an optimism assessment task before and after the intervention, demonstrating an increase in optimism following gratitude practice. Finally, we simulate an abstract proof-of-concept task inspired by the Yarbus paradigm to model attention and find that gratitude practice may affect habitual attention patterns, where agents attend to positive elements over neutral or negative elements in a painting. Our model provides a conceptual understanding of gratitude practice that can be used in future research to gain insights into who may benefit from gratitude practice.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-23"},"PeriodicalIF":2.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Metric Slingshot: Navigational Reuse as Width-Optimal Structural Decoupling in Continual Learning.","authors":"Xin Li","doi":"10.1162/NECO.a.1590","DOIUrl":"https://doi.org/10.1162/NECO.a.1590","url":null,"abstract":"<p><p>The mammalian brain, most extensively studied in rodents and bats, solves an enormous variety of nonspatial cognitive tasks using neural circuitry, including grid cells, place cells, and hippocampal indexing, that originally evolved for physical navigation. We formalize the above observation within the local Urysohn width (LUW) framework for continual learning. The central construct is the metric slingshot, a learned embedding ϕ: X→Z that maps an arbitrary learning problem into a navigational latent space Z where pre-evolved contraction maps (grid cells) already provide the metric machinery, so that only the topological indexing subproblem must be solved de novo. We prove three results. First, the optimal spacing of multiscale grid cell modules is a geometric series whose ratio is determined by the Ω(w log w) sample complexity bound of the LUW framework; for ecologically plausible parameters, optimality yields r*≈ 1.4-1.7, matching electrophysiological measurements in rodent medial entorhinal cortex. Second, the slingshot preserves the width hierarchy with a Lipschitz-controlled transfer bound: a contractive embedding into a fine-resolution navigational space reduces the effective number of contexts the learner must discover. Third, the anatomical separation of the ventral (\"what\") and dorsal (\"where\") visual streams achieves the structural decoupling required by metric-topology factorization (MTF) by architecture, without gradient-routing mechanisms. We demonstrate that a metric slingshot is applicable to both perception cognition and motor control. Together, these results provide a unified, complexity-theoretic account of navigation in nonspatial domain, grid cell multimodularity, and hippocampal-neocortical complementary learning as consequences of a single exaptation principle: metric slingshot.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-33"},"PeriodicalIF":2.1,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}