Neural Computation最新文献

Encoding of Numerosity With Robustness to Object and Scene Identity in Biologically Inspired Object Recognition Networks. 生物启发的目标识别网络中具有目标和场景识别鲁棒性的数字编码。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-10-10 DOI: 10.1162/neco.a.30

Thomas Chapalain, Bertrand Thirion, Evelyn Eger

{"title":"Encoding of Numerosity With Robustness to Object and Scene Identity in Biologically Inspired Object Recognition Networks.","authors":"Thomas Chapalain, Bertrand Thirion, Evelyn Eger","doi":"10.1162/neco.a.30","DOIUrl":"10.1162/neco.a.30","url":null,"abstract":"Number sense, the ability to rapidly estimate object quantities in a visual scene without precise counting, is a crucial cognitive capacity found in humans and many other animals. Recent studies have identified artificial neurons tuned to numbers of items in biologically inspired vision models, even before training, and proposed these artificial neural networks as candidate models for the emergence of number sense in the brain. But real-world numerosity perception requires abstraction from the properties of individual objects and their contexts, unlike the simplified dot patterns used in previous studies. Using novel synthetically generated photorealistic stimuli, we show that deep convolutional neural networks optimized for object recognition encode information on approximate numerosity across diverse objects and scene types, which could be linearly read out from distributed activity patterns of later convolutional layers of different network architectures tested. In contrast, untrained networks with random weights failed to represent numerosity with abstractness to other visual properties and instead captured mainly low-level visual features. Our findings emphasize the importance of using complex, naturalistic stimuli to investigate mechanisms of number sense in both biological and artificial systems, and they suggest that the capacity of untrained networks to account for early-life numerical abilities should be reassessed. They further point to a possible, so far underappreciated, contribution of the brain's ventral visual pathway to representing numerosity with abstractness to other high-level visual properties.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1975-2010"},"PeriodicalIF":2.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857070","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

Modeling Higher-Order Interactions in Sparse and Heavy-Tailed Neural Population Activity. 稀疏和重尾神经群体活动的高阶交互建模。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-10-10 DOI: 10.1162/neco.a.35

Ulises Rodríguez-Domínguez, Hideaki Shimazaki

{"title":"Modeling Higher-Order Interactions in Sparse and Heavy-Tailed Neural Population Activity.","authors":"Ulises Rodríguez-Domínguez, Hideaki Shimazaki","doi":"10.1162/neco.a.35","DOIUrl":"10.1162/neco.a.35","url":null,"abstract":"Neurons process sensory stimuli efficiently, showing sparse yet highly variable ensemble spiking activity involving structured higher-order interactions. Notably, while neural populations are mostly silent, they occasionally exhibit highly synchronous activity, resulting in sparse and heavy-tailed spike-count distributions. However, its mechanistic origin-specifically, what types of nonlinear properties in individual neurons induce such population-level patterns-remains unclear. In this study, we derive sufficient conditions under which the joint activity of homogeneous binary neurons generates sparse and widespread population firing rate distributions in infinitely large networks. We then propose a subclass of exponential family distributions that satisfy this condition. This class incorporates structured higher-order interactions with alternating signs and shrinking magnitudes, along with a base-measure function that offsets distributional concentration, giving rise to parameter-dependent sparsity and heavy-tailed population firing rate distributions. Analysis of recurrent neural networks that recapitulate these distributions reveals that individual neurons possess threshold-like nonlinearity, followed by supralinear activation that jointly facilitates sparse and synchronous population activity. These nonlinear features resemble those in modern Hopfield networks, suggesting a connection between widespread population activity and the network's memory capacity. The theory establishes sparse and heavy-tailed distributions for binary patterns, forming a foundation for developing energy-efficient spike-based learning machines.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"2011-2078"},"PeriodicalIF":2.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126446","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

Feature Normalization Prevents Collapse of Noncontrastive Learning Dynamics. 特征归一化防止非对比学习动态的崩溃。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-10-10 DOI: 10.1162/neco.a.27

Han Bao

{"title":"Feature Normalization Prevents Collapse of Noncontrastive Learning Dynamics.","authors":"Han Bao","doi":"10.1162/neco.a.27","DOIUrl":"10.1162/neco.a.27","url":null,"abstract":"Contrastive learning is a self-supervised representation learning framework where two positive views generated through data augmentation are made similar by an attraction force in a data representation space, while a repulsive force makes them far from negative examples. Noncontrastive learning, represented by BYOL and SimSiam, gets rid of negative examples and improves computational efficiency. While learned representations may collapse into a single point due to the lack of the repulsive force at first sight, Tian et al. (2021) revealed through learning dynamics analysis that the representations can avoid collapse if data augmentation is sufficiently stronger than regularization. However, their analysis does not take into account commonly used feature normalization, a normalizer before measuring the similarity of representations, and hence excessively strong regularization may still collapse the dynamics, an unnatural behavior under the presence of feature normalization. Therefore, we extend the previous theory based on the L2 loss by considering the cosine loss instead, which involves feature normalization. We show that the cosine loss induces sixth-order dynamics (while the L2 loss induces a third-order one), in which a stable equilibrium dynamically emerges even if there are only collapsed solutions with given initial parameters. Thus, we offer a new understanding that feature normalization plays an important role in robustly preventing the dynamics collapse.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"2079-2124"},"PeriodicalIF":2.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857071","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

A Chimera Model for Motion Anticipation in the Retina and the Primary Visual Cortex. 视网膜和初级视觉皮层运动预期的嵌合体模型。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-10-10 DOI: 10.1162/neco.a.34

Jérôme Emonet, Selma Souihel, Frédéric Chavane, Alain Destexhe, Matteo di Volo, Bruno Cessac

引用次数: 0

Firing Rate Models as Associative Memory: Synaptic Design for Robust Retrieval 触发率模型作为联想记忆：稳健检索的突触设计。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-22 DOI: 10.1162/neco.a.28

Simone Betteti;Giacomo Baggio;Francesco Bullo;Sandro Zampieri

{"title":"Firing Rate Models as Associative Memory: Synaptic Design for Robust Retrieval","authors":"Simone Betteti;Giacomo Baggio;Francesco Bullo;Sandro Zampieri","doi":"10.1162/neco.a.28","DOIUrl":"10.1162/neco.a.28","url":null,"abstract":"Firing rate models are dynamical systems widely used in applied and theoretical neuroscience to describe local cortical dynamics in neuronal populations. By providing a macroscopic perspective of neuronal activity, these models are essential for investigating oscillatory phenomena, chaotic behavior, and associative memory processes. Despite their widespread use, the application of firing rate models to associative memory networks has received limited mathematical exploration, and most existing studies are focused on specific models. Conversely, well-established associative memory designs, such as Hopfield networks, lack key biologically relevant features intrinsic to firing rate models, including positivity and interpretable synaptic matrices reflecting the action of long-term potentiation and long-term depression. To address this gap, we propose a general framework that ensures the emergence of rescaled memory patterns as stable equilibria in the firing rate dynamics. Furthermore, we analyze the conditions under which the memories are locally and globally asymptotically stable, providing insights into constructing biologically plausible and robust systems for associative memory retrieval.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 10","pages":"1807-1838"},"PeriodicalIF":2.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857072","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

Rapid Reweighting of Sensory Inputs and Predictions in Visual Perception 视觉感知中感官输入和预测的快速重加权。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-22 DOI: 10.1162/neco.a.26

William Turner;Oh-Sang Kwon;Minwoo J.B. Kim;Hinze Hogendoorn

引用次数: 0

Sequential Learning in the Dense Associative Memory 密集联想记忆中的顺序学习。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-22 DOI: 10.1162/neco.a.20

Hayden McAlister;Anthony Robins;Lech Szymanski

{"title":"Sequential Learning in the Dense Associative Memory","authors":"Hayden McAlister;Anthony Robins;Lech Szymanski","doi":"10.1162/neco.a.20","DOIUrl":"10.1162/neco.a.20","url":null,"abstract":"Sequential learning involves learning tasks in a sequence and proves challenging for most neural networks. Biological neural networks regularly succeed at the sequential learning challenge and are even capable of transferring knowledge both forward and backward between tasks. Artificial neural networks often totally fail to transfer performance between tasks and regularly suffer from degraded performance or catastrophic forgetting on previous tasks. Models of associative memory have been used to investigate the discrepancy between biological and artificial neural networks due to their biological ties and inspirations, of which the Hopfield network is the most studied model. The dense associative memory (DAM), or modern Hopfield network, generalizes the Hopfield network, allowing for greater capacities and prototype learning behaviors while still retaining the associative memory structure. We give a substantial review of the sequential learning space with particular respect to the Hopfield network and associative memories. We present the first published benchmarks of sequential learning in the DAM using various sequential learning techniques and analyze the results of the sequential learning to demonstrate previously unseen transitions in the behavior of the DAM. This letter also discusses the departure from biological plausibility that may affect the utility of the DAM as a tool for studying biological neural networks. We present our findings, including the effectiveness of a range of state-of-the-art sequential learning methods when applied to the DAM, and use these methods to further the understanding of DAM properties and behaviors.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 10","pages":"1877-1924"},"PeriodicalIF":2.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700385","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

Transformer Models for Signal Processing: Scaled Dot-Product Attention Implements Constrained Filtering 用于信号处理的变压器模型：缩放点积注意实现约束滤波。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-22 DOI: 10.1162/neco.a.29

Terence D. Sanger

引用次数: 0

Distance-Based Logistic Matrix Factorization 基于距离的Logistic矩阵分解。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-22 DOI: 10.1162/neco.a.25

Anoop Praturu;Tatyana O. Sharpee

{"title":"Distance-Based Logistic Matrix Factorization","authors":"Anoop Praturu;Tatyana O. Sharpee","doi":"10.1162/neco.a.25","DOIUrl":"10.1162/neco.a.25","url":null,"abstract":"Matrix factorization is a central paradigm in matrix completion and collaborative filtering. Low-rank factorizations have been extremely successful in reconstructing and generalizing high-dimensional data in a wide variety of machine learning problems from drug-target discovery to music recommendations. Virtually all proposed matrix factorization techniques use the dot product between latent factor vectors to reconstruct the original matrix. We propose a reformulation of the widely used logistic matrix factorization in which we use the distance, rather than the dot product, to measure similarity between latent factors. We show that this measure of similarity, which can draw nonlinear decision boundaries and respect triangle inequalities between points, has more expressive power and modeling capacity. The distance-based model implemented in Euclidean and hyperbolic space outperforms previous formulations of logistic matrix factorization on three different biological test problems with disparate structure and statistics. In particular, we show that a distance-based factorization (1) generalizes better to test data, (2) achieves optimal performance at lower factor space dimension, and (3) clusters data better in the latent factor space.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 10","pages":"1863-1876"},"PeriodicalIF":2.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857068","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

Diversity Deconstrains Component Limitations in Sensorimotor Control 多样性消除了感觉运动控制中的成分限制。

IF 2.1 4区计算机科学

Neural Computation Pub Date : 2025-09-22 DOI: 10.1162/neco.a.24

Yorie Nakahira;Quanying Liu;Xiyu Deng;Terrence J. Sejnowski;John C. Doyle

引用次数: 0