Neural Computation最新文献_第2页

Fusing Foveal Fixations Using Linear Retinal Transformations and Bayesian Experimental Design. 利用线性视网膜变换和贝叶斯实验设计融合中央凹固定。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-19 DOI: 10.1162/neco.a.33

Christopher K I Williams

引用次数: 0

Boosting MCTS With Free Energy Minimization. 用自由能最小化来提升MCTS。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-19 DOI: 10.1162/neco.a.31

Mawaba Pascal Dao, Adrian M Peter

引用次数: 0

Estimating Phase From Observed Trajectories Using the Temporal 1-Form. 利用时间1-形式从观测轨迹估计相位。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-19 DOI: 10.1162/neco.a.32

Simon Wilshin, Matthew D Kvalheim, Clayton Scott, Shai Revzen

引用次数: 0

Fast Multigroup Gaussian Process Factor Models 快速多组高斯过程因子模型。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-08-08 DOI: 10.1162/neco.a.22

Evren Gokcen;Anna I. Jasper;Adam Kohn;Christian K. Machens;Byron M. Yu

{"title":"Fast Multigroup Gaussian Process Factor Models","authors":"Evren Gokcen;Anna I. Jasper;Adam Kohn;Christian K. Machens;Byron M. Yu","doi":"10.1162/neco.a.22","DOIUrl":"10.1162/neco.a.22","url":null,"abstract":"Gaussian processes are now commonly used in dimensionality reduction approaches tailored to neuroscience, especially to describe changes in high-dimensional neural activity over time. As recording capabilities expand to include neuronal populations across multiple brain areas, cortical layers, and cell types, interest in extending gaussian process factor models to characterize multipopulation interactions has grown. However, the cubic runtime scaling of current methods with the length of experimental trials and the number of recorded populations (groups) precludes their application to large-scale multipopulation recordings. Here, we improve this scaling from cubic to linear in both trial length and group number. We present two approximate approaches to fitting multigroup gaussian process factor models based on inducing variables and the frequency domain. Empirically, both methods achieved orders of magnitude speed-up with minimal impact on statistical performance, in simulation and on neural recordings of hundreds of neurons across three brain areas. The frequency domain approach, in particular, consistently provided the greatest runtime benefits with the fewest trade-offs in statistical performance. We further characterize the estimation biases introduced by the frequency domain approach and demonstrate effective strategies to mitigate them. This work enables a powerful class of analysis techniques to keep pace with the growing scale of multipopulation recordings, opening new avenues for exploring brain function.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 9","pages":"1709-1782"},"PeriodicalIF":2.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700382","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}

引用次数: 0

Toward Generalized Entropic Sparsification for Convolutional Neural Networks 卷积神经网络的广义熵稀疏化研究。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-08-08 DOI: 10.1162/neco.a.21

Tin Barisin;Illia Horenko

引用次数: 0

Exploring the Architectural Biases of the Cortical Microcircuit 探索皮层微电路的结构偏差。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-08-08 DOI: 10.1162/neco.a.23

Aishwarya Balwani;Suhee Cho;Hannah Choi

{"title":"Exploring the Architectural Biases of the Cortical Microcircuit","authors":"Aishwarya Balwani;Suhee Cho;Hannah Choi","doi":"10.1162/neco.a.23","DOIUrl":"10.1162/neco.a.23","url":null,"abstract":"The cortex plays a crucial role in various perceptual and cognitive functions, driven by its basic unit, the canonical cortical microcircuit. Yet, we remain short of a framework that definitively explains the structure-function relationships of this fundamental neuroanatomical motif. To better understand how physical substrates of cortical circuitry facilitate their neuronal dynamics, we employ a computational approach using recurrent neural networks and representational analyses. We examine the differences manifested by the inclusion and exclusion of biologically motivated interareal laminar connections on the computational roles of different neuronal populations in the microcircuit of hierarchically related areas throughout learning. Our findings show that the presence of feedback connections correlates with the functional modularization of cortical populations in different layers and provides the microcircuit with a natural inductive bias to differentiate expected and unexpected inputs at initialization, which we justify mathematically. Furthermore, when testing the effects of training the microcircuit and its variants with a predictive-coding-inspired strategy, we find that doing so helps better encode noisy stimuli in areas of the cortex that receive feedback, all of which combine to suggest evidence for a predictive-coding mechanism serving as an intrinsic operative logic in the cortex.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 9","pages":"1551-1599"},"PeriodicalIF":2.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700381","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}

引用次数: 0

From Function to Implementation: Exploring Degeneracy in Evolved Artificial Agents 从功能到实现：探索进化人工智能体的退化。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-08-08 DOI: 10.1162/neco.a.19

Zhimin Hu;Oğulcan Cingiler;Clifford Bohm;Larissa Albantakis

{"title":"From Function to Implementation: Exploring Degeneracy in Evolved Artificial Agents","authors":"Zhimin Hu;Oğulcan Cingiler;Clifford Bohm;Larissa Albantakis","doi":"10.1162/neco.a.19","DOIUrl":"10.1162/neco.a.19","url":null,"abstract":"Degeneracy—the ability of different structures to perform the same function—is a fundamental feature of biological systems, contributing to their robustness and evolvability. However, the ubiquity of degeneracy in systems generated through adaptive processes complicates our understanding of the behavioral and computational strategies they employ. In this study, we investigated degeneracy in simple computational agents, known as Markov brains, trained using an artificial evolution algorithm to solve a spatial navigation task with or without associative memory. We analyzed degeneracy at three levels: behavioral, structural, and computational, with a focus on the last. Using information-theoretical concepts, Tononi et al. (1999) proposed a functional measure of degeneracy within biological networks. Here, we extended this approach to compare degeneracy across multiple networks. Using information-theoretical tools and causal analysis, we explored the computational strategies of the evolved agents and quantified their computational degeneracy. Our findings reveal a hierarchy of degenerate solutions, from varied behaviors to diverse structures and computations. Even agents with identical evolved behaviors demonstrated different underlying structures and computations. These results underscore the pervasive nature of degeneracy in neural networks, blurring the lines between the algorithmic and implementation levels in adaptive systems, and highlight the importance of advanced analytical tools to understand their complex behaviors.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 9","pages":"1677-1708"},"PeriodicalIF":2.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700383","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}

引用次数: 0

Synergistic Pathways of Modulation Enable Robust Task Packing Within Neural Dynamics 调制的协同路径使神经动力学中的鲁棒任务打包成为可能。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-08-08 DOI: 10.1162/neco.a.18

Giacomo Vedovati;ShiNung Ching

{"title":"Synergistic Pathways of Modulation Enable Robust Task Packing Within Neural Dynamics","authors":"Giacomo Vedovati;ShiNung Ching","doi":"10.1162/neco.a.18","DOIUrl":"10.1162/neco.a.18","url":null,"abstract":"Understanding how brain networks learn and manage multiple tasks simultaneously is of interest in both neuroscience and artificial intelligence. In this regard, a recent research thread in theoretical neuroscience has focused on how recurrent neural network models and their internal dynamics enact multitask learning. To manage different tasks requires a mechanism to convey information about task identity or context into the model, which from a biological perspective may involve mechanisms of neuromodulation. In this study, we use recurrent network models to probe the distinctions between two forms of contextual modulation of neural dynamics, at the level of neuronal excitability and at the level of synaptic strength. We characterize these mechanisms in terms of their functional outcomes, focusing on their robustness to context ambiguity and, relatedly, their efficiency with respect to packing multiple tasks into finite-size networks. We also demonstrate the distinction between these mechanisms at the level of the neuronal dynamics they induce. Together, these characterizations indicate complementarity and synergy in how these mechanisms act, potentially over many timescales, toward enhancing the robustness of multitask learning.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 9","pages":"1529-1550"},"PeriodicalIF":2.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700386","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}

引用次数: 0

Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace 通过通讯子空间测量神经群体间刺激信息传递。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-08-08 DOI: 10.1162/neco.a.17

Oren Weiss;Ruben Coen-Cagli

{"title":"Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace","authors":"Oren Weiss;Ruben Coen-Cagli","doi":"10.1162/neco.a.17","DOIUrl":"10.1162/neco.a.17","url":null,"abstract":"Sensory processing arises from the communication between neural populations across multiple brain areas. While the widespread presence of neural response variability shared throughout a neural population limits the amount of stimulus-related information those populations can accurately represent, how this variability affects the interareal communication of sensory information is unknown. We propose a mathematical framework to understand the impact of neural population response variability on sensory information transmission. We combine linear Fisher information, a metric connecting stimulus representation and variability, with the framework of communication subspaces, which suggests that functional mappings between cortical populations are low-dimensional relative to the space of population activity patterns. From this, we partition Fisher information depending on the alignment between the population covariance and the mean tuning direction projected onto the communication subspace or its orthogonal complement. We provide mathematical and numerical analyses of our proposed decomposition of Fisher information and examine theoretical scenarios that demonstrate how to leverage communication subspaces for flexible routing and gating of stimulus information. This work will provide researchers investigating interareal communication with a theoretical lens through which to understand sensory information transmission and guide experimental design.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 9","pages":"1600-1647"},"PeriodicalIF":2.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700384","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}

引用次数: 0

Continuous-Time Neural Networks Can Stably Memorize Random Spike Trains 连续时间神经网络稳定记忆随机尖峰列车。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-07-17 DOI: 10.1162/neco_a_01768

Hugo Aguettaz;Hans-Andrea Loeliger

引用次数: 0