Neural Computation最新文献

{"title":"Competition Between Memory Updating and Differentiation Emerges From Intrinsic Network Dynamics","authors":"Julia Pronoza;Nina Liedtke;Marius Boeltzig;Ricarda I. Schubotz;Sen Cheng","doi":"10.1162/NECO.a.1535","DOIUrl":"10.1162/NECO.a.1535","url":null,"abstract":"When an event occurs that is similar to a previous experience, the original episodic memory can be modified with new information (updating) or a new memory can be encoded separately (differentiation). Prediction errors, the deviation between expected and actual stimuli, are believed to mediate the competition between updating and differentiation, but the underlying mechanisms remain unclear. Here, we present a new analysis of experiments examining recognition memory and cued recall of similar conversations. We find that the original version is recognized more confidently than the modified version, the recognition confidence for modified versions shows a U-shaped dependence on the prediction error, and larger prediction errors lead to participants’ more frequently recalling two versions. These results can be accounted for by a computational model based on a modified Hopfield network, which encodes the original and modified versions sequentially and weights the encoding of new patterns based on the prediction error. The model shows that (1) similar new memories interfere with previous ones (updating), while dissimilar ones are stored separately (differentiation); (2) interference from similar representations leads to reduced memory accuracy and lower-confidence recognition; and (3) modulation of the encoding strength is critical for accounting for the experimental data. Our modeling results show that prediction-error-driven competition between updating and differentiation can emerge from intrinsic network dynamics alone.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 7","pages":"1233-1260"},"PeriodicalIF":2.1,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148207106","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":"Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.","authors":"Issey Sukeda, Takeru Matsuda","doi":"10.1162/NECO.a.1536","DOIUrl":"10.1162/NECO.a.1536","url":null,"abstract":"<p><p>Identifying phase coupling from electrophysiological signals recorded by multiple electrodes, such as electroencephalogram (EEG) and electrocorticography (ECoG), helps neuroscientists and clinicians understand underlying brain structures or mechanisms. From a statistical perspective, these signals are multidimensional circular measurements that are correlated with one another and can be effectively modeled using a torus graph model designed for circular random variables. Using the torus graph model avoids the issue of detecting spurious correlations. However, the naive estimation of this model tends to lead to a dense network structure that is difficult to interpret. Therefore, to enhance the interpretability of the brain network structure, this review proposes a sparse estimation method for the torus graph model using regularized score matching combined with information criteria. In numerical simulations, our method successfully recovered the true dependence structure from a synthetic data set. Furthermore, we present analyses of two real data sets, one involving human EEG and the other marmoset ECoG, demonstrating that our method can be widely applied to phase-coupling analysis across different types of neural data. We found that when we used our proposed method, the modularity of the estimated network structure revealed more resolved brain structures and demonstrated differences in trends among individuals.</p>","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 7","pages":"1135-1179"},"PeriodicalIF":2.1,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148207137","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":"Toward a Computational Phenomenology of Meditative Deconstruction: “Letting Go” and the Deconstruction of Experience With Active Inference","authors":"Shawn Prest","doi":"10.1162/NECO.a.1534","DOIUrl":"10.1162/NECO.a.1534","url":null,"abstract":"Meditative experience has long been associated with conceptual attenuation, reduced reactivity to phenomena, increased present moment perception, and more pleasant experience. However, the computational mechanisms underlying such meditative deconstruction are not well understood, with no formal computational models available to explicate how deconstruction alters perception and action during meditation. Using the active inference framework, I demonstrate that the phenomenology of deconstruction—in terms of conceptual attenuation, reduced reactivity, and shorter temporal scale perception—naturally emerges from the dynamics of hierarchical inference when the deconstructive notion of letting go is cast as a reduction in precision of beliefs about hidden states at a specific level of the generative model. I present a formal hierarchical three-level generative model and simulate deconstruction as an intervention in a facial recognition task, where the agent selects a letting-go policy when perceived affective valence becomes excessively negative. The results demonstrate that the capacity to deconstruct permits agents to self-regulate experienced affect via letting go. The model offers a novel perspective within the paradigm of computational phenomenology on conceptual attenuation, equanimity, stillness, and affect during meditative deconstruction.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 7","pages":"1261-1298"},"PeriodicalIF":2.1,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11569404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148207093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infinite Horizon Control With Nonlinear Dynamics Models Reproduces Temporal Modulation of Reaching Movements","authors":"Antoine De Comite;Hari Teja Kalidindi;J. Andrew Pruszynski;Frédéric Crevecoeur","doi":"10.1162/NECO.a.1515","DOIUrl":"10.1162/NECO.a.1515","url":null,"abstract":"Movement duration, a fundamental aspect of motor control, is often viewed as a preprogrammed parameter requiring dedicated selection mechanisms. An alternative view posits that movement duration emerges from the control policy itself. Here, we demonstrate, using infinite horizon optimal feedback control (IHOFC) and nonlinear limb dynamics, that this alternative hypothesis successfully captures diverse aspects of human reaching behavior, including trade-offs between movement duration and task parameters. Specifically, we reproduced the modulation of movement duration with varying reach distances and accuracy (Fitts's law) in the presence of nonlinear dynamics, and extended the infinite horizon framework to include the effect of rewards and biomechanical costs. Furthermore, our model also featured a temporal evolution of feedback responses to perturbations that resembles experimental observations and naturally accounted for motor decisions observed when participants select one among multiple goals in dynamic environments. Together, these developments show that in many cases, movement duration may not need to be specified a priori, but instead could result from task-dependent control policies. This framework validates a candidate explanation for varied movement durations, which invites a reconsideration of the nature and strength of evidence for the finite horizon formulation.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 5","pages":"823-844"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634991","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":"Echoes of the Past: A Unified Perspective on Fading Memory and Echo States","authors":"Juan-Pablo Ortega;Florian Rossmannek","doi":"10.1162/NECO.a.1510","DOIUrl":"10.1162/NECO.a.1510","url":null,"abstract":"Recurrent neural networks (RNNs) have become increasingly popular in information processing tasks involving time series and temporal data. A fundamental property of RNNs is their ability to create reliable input/output responses, often linked to how the network handles its memory of the information it processed. Various notions have been proposed to conceptualize the behavior of memory in RNNs, including steady states, echo states, state forgetting, input forgetting, and fading memory. Although these notions are often used interchangeably, their precise relationships remain unclear. This work aims to unify these notions in a common language, derive new implications and equivalences between them, and provide alternative proofs to some existing results. By clarifying the relationships between these concepts, this research contributes to a deeper understanding of RNNs and their temporal information processing capabilities.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 5","pages":"765-782"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634950","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":"Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Timescales","authors":"Veronica Centorrino;Francesco Bullo;Giovanni Russo","doi":"10.1162/NECO.a.1509","DOIUrl":"10.1162/NECO.a.1509","url":null,"abstract":"A recent breakthrough in biologically plausible normative frameworks for dimensionality reduction is based on the similarity matching cost function and the low-rank matrix approximation problem. Despite clear biological interpretation, successful application in several domains, and experimental validation, a formal complete convergence analysis remains elusive. Building on this framework, we consider and analyze a continuous-time neural network, the similarity matching network, for principal subspace projection. Derived from a min-max-min objective, this biologically plausible network consists of three coupled dynamics evolving at different timescales: neural dynamics, lateral synaptic dynamics, and feedforward synaptic dynamics at the fast, intermediate, and slow timescales, respectively. The feedforward and lateral synaptic dynamics consist of Hebbian and anti-Hebbian learning rules, respectively. By leveraging a multilevel optimization framework, we prove convergence of the dynamics in the offline setting. Specifically, at the first level (fast timescale), we show strong convexity of the cost function and global exponential convergence of the corresponding gradient-flow dynamics. At the second level (intermediate timescale), we prove strong concavity of the cost function and exponential convergence of the corresponding gradient-flow dynamics within the space of positive definite matrices. At the third and final level (slow timescale), we study a nonconvex and nonsmooth cost function, provide explicit expressions for its global minima, and prove almost sure convergence of the corresponding gradient-flow dynamics to the global minima. These results rely on two empirically motivated conjectures that are supported by thorough numerical experiments. Finally, we validate the effectiveness of our approach via a numerical example.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 5","pages":"725-764"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635025","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":"Signal-Dependent Planning Noise Reduces Task Interference by Assisting in the Formation of Stable Motor Primitives in a Neural Network Model of Muscle Coordination Learning","authors":"Daniel W. Feng;David J. Reinkensmeyer;Juan C. Perez-Ibarra","doi":"10.1162/NECO.a.1512","DOIUrl":"10.1162/NECO.a.1512","url":null,"abstract":"In human motor coordination, learning to coactivate multiple muscles at once to achieve distinct target combinations of forces or tasks remains a fundamental area of study. Task interference, where training on one task degrades performance on previously learned tasks, can slow motor learning. However, the neural mechanisms that reduce interference are not fully understood. We hypothesized that the structure of planning noise, specifically its signal-dependent nature, significantly shapes learning dynamics and limits interference within motor learning systems that rely on variability for exploration. To test this hypothesis, we developed a three-layer neural network model of muscle coordination informed by key neuroanatomical and neurophysiological principles and simulated learning for producing various combinations of muscle forces. Synaptic weights were stochastically altered from trial to trial with either fixed-variance planning noise (FVPN), where each connection's variance was fixed during learning, or signal-dependent planning noise (SDPN), where noise variance depended on the neuron population activity. Weights were reinforced when they reduced output error relative to target forces. An execution noise term, applied to the motor output, modeled peripheral motor variability. However, the learning rule was not informed about how much of the output corresponded to peripheral or central variability. Our results showed that SDPN improved both the rate and accuracy of multitask learning by reducing task interference compared to FVPN across network sizes, training schedules, and execution noise levels. SDPN achieved this by concentrating neural plasticity within the neuron populations engaged by the current task rather than modifying the entire network. This signal-dependent plasticity allowed multiple motor primitives to form, stabilize, and be reused for new tasks. The model replicated the well-documented benefit of interleaved versus blocked training in motor learning. As a computational proof of concept, this work suggests that SDPN can benefit multitask motor training by facilitating the formation of motor primitives.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 5","pages":"783-822"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635007","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":"Cognitive Control Strategies Derive From Dimension Reliability","authors":"William H. Alexander","doi":"10.1162/NECO.a.1513","DOIUrl":"10.1162/NECO.a.1513","url":null,"abstract":"To explain behavioral effects, models of cognitive control frequently rely on task information that the modeler provides. Hard-wired information can include labeling task dimensions as being relevant or irrelevant, defining which task stimuli belong to which task dimensions, or proposing a specific strategy by which control is adjusted during task performance. Although models incorporating hard-wired information of this nature are frequently successful at accounting for observed behavior, their ability to do so often depends on tailoring this information to specific tasks, usually performed in a laboratory setting. Outside of the laboratory, individuals are not usually provided explicit information about how to behave; it thus remains an open question as to how individuals identify, update, and switch task strategies in the real world. Here, we present a new model of cognitive control, learned attention for control (LAC), that not only captures a broad range of control effects but does so using a minimal amount of modeler-supplied information. In a series of simulations, we demonstrate how the LAC model adopts distinct control strategies based on recent trial history and adapts to changing behavioral contexts. The model's ability to do so derives from an ongoing evaluation of how well task stimuli independently predict correct behavior, and the results of this evaluation are used to shift attention among information sources. These results suggest that the reliability of information can serve as a general principle for understanding cognitive control.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 5","pages":"681-724"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11513730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perceptual Processes as Charting Operators","authors":"Peter Neri","doi":"10.1162/NECO.a.1506","DOIUrl":"10.1162/NECO.a.1506","url":null,"abstract":"Sensory operators are classically modeled using small circuits involving canonical computations, such as energy extraction and gain control. Notwithstanding their utility, circuit models do not provide a unified framework encompassing the variety of effects observed experimentally. We develop a novel, alternative framework that recasts sensory operators in the language of intrinsic geometry. We start from a plausible representation of perceptual processes that is akin to measuring distances over a sensory manifold. We show that this representation is sufficiently expressive to capture a wide range of empirical effects associated with elementary sensory computations. The resulting geometrical framework offers a new perspective on state-of-the-art empirical descriptors of sensory behavior, such as first-order and second-order perceptual kernels. For example, it relates these descriptors to notions of flatness and curvature in perceptual space.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 6","pages":"952-1005"},"PeriodicalIF":2.1,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147366910","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":"Toward Enhancing RMSProp With Forward-Looking Gradient Updates for Complex Loss Landscapes","authors":"Rafał Wolniak;Bożena Kostek","doi":"10.1162/NECO.a.1514","DOIUrl":"10.1162/NECO.a.1514","url":null,"abstract":"This letter introduces a novel algorithm for training deep neural networks with many nonlinear layers. Our method uses an approximated integrated gradient that is averaged over the range of the weight update to more accurately capture the loss change resulting from parameter updates. Unlike standard gradients, this average gradient improves learning efficiency in certain scenarios. We incorporate the approximated average gradient into RMSProp and compare the resulting algorithm to conventional RMSProp and Adam. We evaluate the approach on deep models lacking skip connections, such as those with many nonlinear activations and no residual structure, where traditional methods typically encounter difficulties. These models that focus on extracting high-order features create a loss landscape more akin to that of a biological brain. Our method requires significantly fewer iterations to reach a target training loss on MNIST, Fashion MNIST, and IMDb benchmarks for both convolutional and fully connected architectures across different initialization schemes. While our approach incurs moderately higher computational and memory costs compared to standard RMSProp, its performance on shallow models remains comparable. Nevertheless, our main contributions are (1) introducing the average gradient concept as an efficient alternative to computing high–order derivatives, (2) offering a novel factorization formula for approximating the average gradient, accompanied by a formal derivation, and (3) showing an example algorithm that leverages this formula to enhance the efficiency of RMSProp for some models, as validated by our evaluation.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"38 6","pages":"1062-1089"},"PeriodicalIF":2.1,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147635089","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}