Neural Computation最新文献_第8页

Vector Symbolic Finite State Machines in Attractor Neural Networks 吸引器神经网络中的矢量符号有限状态机

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01638

Madison Cotteret;Hugh Greatorex;Martin Ziegler;Elisabetta Chicca

引用次数: 0

Mathematical Modeling of PI3K/Akt Pathway in Microglia 小胶质细胞中 PI3K/Akt 通路的数学建模

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01643

Alireza Poshtkohi;John Wade;Liam McDaid;Junxiu Liu;Mark L. Dallas;Angela Bithell

{"title":"Mathematical Modeling of PI3K/Akt Pathway in Microglia","authors":"Alireza Poshtkohi;John Wade;Liam McDaid;Junxiu Liu;Mark L. Dallas;Angela Bithell","doi":"10.1162/neco_a_01643","DOIUrl":"10.1162/neco_a_01643","url":null,"abstract":"The motility of microglia involves intracellular signaling pathways that are predominantly controlled by changes in cytosolic Ca2+ and activation of PI3K/Akt (phosphoinositide-3-kinase/protein kinase B). In this letter, we develop a novel biophysical model for cytosolic Ca2+ activation of the PI3K/Akt pathway in microglia where Ca2+ influx is mediated by both P2Y purinergic receptors (P2YR) and P2X purinergic receptors (P2XR). The model parameters are estimated by employing optimization techniques to fit the model to phosphorylated Akt (pAkt) experimental modeling/in vitro data. The integrated model supports the hypothesis that Ca2+ influx via P2YR and P2XR can explain the experimentally reported biphasic transient responses in measuring pAkt levels. Our predictions reveal new quantitative insights into P2Rs on how they regulate Ca2+ and Akt in terms of physiological interactions and transient responses. It is shown that the upregulation of P2X receptors through a repetitive application of agonist results in a continual increase in the baseline [Ca2+], which causes the biphasic response to become a monophasic response which prolongs elevated levels of pAkt.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 4","pages":"645-676"},"PeriodicalIF":2.9,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066268","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

Object-Centric Scene Representations Using Active Inference 使用主动推理进行以对象为中心的场景表示

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01637

Toon Van de Maele;Tim Verbelen;Pietro Mazzaglia;Stefano Ferraro;Bart Dhoedt

引用次数: 0

The Determining Role of Covariances in Large Networks of Stochastic Neurons 随机神经元大型网络中协方差的决定性作用

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-03-10 DOI: 10.1162/neco_a_01656

Vincent Painchaud;Patrick Desrosiers;Nicolas Doyon

{"title":"The Determining Role of Covariances in Large Networks of Stochastic Neurons","authors":"Vincent Painchaud;Patrick Desrosiers;Nicolas Doyon","doi":"10.1162/neco_a_01656","DOIUrl":"10.1162/neco_a_01656","url":null,"abstract":"Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network’s populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 6","pages":"1121-1162"},"PeriodicalIF":2.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805835","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

How Does the Inner Retinal Network Shape the Ganglion Cells Receptive Field? A Computational Study 内视网膜网络如何塑造神经节细胞的感受野？计算研究

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-03-10 DOI: 10.1162/neco_a_01663

Evgenia Kartsaki;Gerrit Hilgen;Evelyne Sernagor;Bruno Cessac

{"title":"How Does the Inner Retinal Network Shape the Ganglion Cells Receptive Field? A Computational Study","authors":"Evgenia Kartsaki;Gerrit Hilgen;Evelyne Sernagor;Bruno Cessac","doi":"10.1162/neco_a_01663","DOIUrl":"10.1162/neco_a_01663","url":null,"abstract":"We consider a model of basic inner retinal connectivity where bipolar and amacrine cells interconnect and both cell types project onto ganglion cells, modulating their response output to the brain visual areas. We derive an analytical formula for the spatiotemporal response of retinal ganglion cells to stimuli, taking into account the effects of amacrine cells inhibition. This analysis reveals two important functional parameters of the network: (1) the intensity of the interactions between bipolar and amacrine cells and (2) the characteristic timescale of these responses. Both parameters have a profound combined impact on the spatiotemporal features of retinal ganglion cells’ responses to light. The validity of the model is confirmed by faithfully reproducing pharmacogenetic experimental results obtained by stimulating excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) expressed on ganglion cells and amacrine cells’ subclasses, thereby modifying the inner retinal network activity to visual stimuli in a complex, entangled manner. Our mathematical model allows us to explore and decipher these complex effects in a manner that would not be feasible experimentally and provides novel insights in retinal dynamics.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 6","pages":"1041-1083"},"PeriodicalIF":2.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805848","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

Gauge-Optimal Approximate Learning for Small Data Classification 小数据分类的量纲最优近似学习

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-03-10 DOI: 10.1162/neco_a_01664

Edoardo Vecchi;Davide Bassetti;Fabio Graziato;Lukáš Pospíšil;Illia Horenko

{"title":"Gauge-Optimal Approximate Learning for Small Data Classification","authors":"Edoardo Vecchi;Davide Bassetti;Fabio Graziato;Lukáš Pospíšil;Illia Horenko","doi":"10.1162/neco_a_01664","DOIUrl":"10.1162/neco_a_01664","url":null,"abstract":"Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems. We prove that the optimal solution of the GOAL algorithm consists in piecewise-linear functions in the Euclidean space and that it can be approximated through a monotonically convergent algorithm that presents—under the assumption of a discrete segmentation of the feature space—a closed-form solution for each optimization substep and an overall linear iteration cost scaling. The GOAL algorithm has been compared to other state-of-the-art machine learning tools on both synthetic data and challenging real-world applications from climate science and bioinformatics (i.e., prediction of the El Niño Southern Oscillation and inference of epigenetically induced gene-activity networks from limited experimental data). The experimental results show that the proposed algorithm outperforms the reported best competitors for these problems in both learning performance and computational cost.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 6","pages":"1198-1227"},"PeriodicalIF":2.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805847","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

Positive Competitive Networks for Sparse Reconstruction 用于稀疏重建的正竞争网络

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-03-10 DOI: 10.1162/neco_a_01657

Veronica Centorrino;Anand Gokhale;Alexander Davydov;Giovanni Russo;Francesco Bullo

{"title":"Positive Competitive Networks for Sparse Reconstruction","authors":"Veronica Centorrino;Anand Gokhale;Alexander Davydov;Giovanni Russo;Francesco Bullo","doi":"10.1162/neco_a_01657","DOIUrl":"10.1162/neco_a_01657","url":null,"abstract":"We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with non-negativity constraints. These problems, which involve approximating a given input stimulus from a dictionary using a set of sparse (active) neurons, play a key role in a wide range of domains, including, for example, neuroscience, signal processing, and machine learning. First, by leveraging the theory of proximal operators, we relate the equilibria of a family of continuous-time firing-rate neural networks to the optimal solutions of sparse reconstruction problems. Then we prove that the PFCN is a positive system and give rigorous conditions for the convergence to the equilibrium. Specifically, we show that the convergence depends only on a property of the dictionary and is linear-exponential in the sense that initially, the convergence rate is at worst linear and then, after a transient, becomes exponential. We also prove a number of technical results to assess the contractivity properties of the neural dynamics of interest. Our analysis leverages contraction theory to characterize the behavior of a family of firing-rate competitive networks for sparse reconstruction with and without non-negativity constraints. Finally, we validate the effectiveness of our approach via a numerical example.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 6","pages":"1163-1197"},"PeriodicalIF":2.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805846","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

Dense Sample Deep Learning 密集样本深度学习

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-03-10 DOI: 10.1162/neco_a_01666

Stephen José Hanson;Vivek Yadav;Catherine Hanson

{"title":"Dense Sample Deep Learning","authors":"Stephen José Hanson;Vivek Yadav;Catherine Hanson","doi":"10.1162/neco_a_01666","DOIUrl":"10.1162/neco_a_01666","url":null,"abstract":"Deep learning (DL), a variant of the neural network algorithms originally proposed in the 1980s (Rumelhart et al., 1986), has made surprising progress in artificial intelligence (AI), ranging from language translation, protein folding (Jumper et al., 2021), autonomous cars, and, more recently, human-like language models (chatbots). All that seemed intractable until very recently. Despite the growing use of DL networks, little is understood about the learning mechanisms and representations that make these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and, of course, the large scale of the data, since not much has changed since 1986. But the nature of deep learned representations remains largely unknown. Unfortunately, training sets with millions or billions of tokens have unknown combinatorics, and networks with millions or billions of hidden units can't easily be visualized and their mechanisms can't be easily revealed. In this letter, we explore these challenges with a large (1.24 million weights VGG) DL in a novel high-density sample task (five unique tokens with more than 500 exemplars per token), which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping. From these results, we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 6","pages":"1228-1244"},"PeriodicalIF":2.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10661260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805850","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

Linear Codes for Hyperdimensional Computing 超维计算线性代码

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-03-10 DOI: 10.1162/neco_a_01665

Netanel Raviv

{"title":"Linear Codes for Hyperdimensional Computing","authors":"Netanel Raviv","doi":"10.1162/neco_a_01665","DOIUrl":"10.1162/neco_a_01665","url":null,"abstract":"Hyperdimensional computing (HDC) is an emerging computational paradigm for representing compositional information as high-dimensional vectors and has a promising potential in applications ranging from machine learning to neuromorphic computing. One of the long-standing challenges in HDC is factoring a compositional representation to its constituent factors, also known as the recovery problem. In this article, we take a novel approach to solve the recovery problem and propose the use of random linear codes. These codes are subspaces over the Boolean field and are a well-studied topic in information theory with various applications in digital communication. We begin by showing that hyperdimensional encoding using random linear codes retains favorable properties of the prevalent (ordinary) random codes; hence, HD representations using the two methods have comparable information storage capabilities. We proceed to show that random linear codes offer a rich subcode structure that can be used to form key-value stores, which encapsulate the most used cases of HDC. Most important, we show that under the framework we develop, random linear codes admit simple recovery algorithms to factor (either bundled or bound) compositional representations. The former relies on constructing certain linear equation systems over the Boolean field, the solution to which reduces the search space dramatically and strictly outperforms exhaustive search in many cases. The latter employs the subspace structure of these codes to achieve provably correct factorization. Both methods are strictly faster than the state-of-the-art resonator networks, often by an order of magnitude. We implemented our techniques in Python using a benchmark software library and demonstrated promising experimental results.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 6","pages":"1084-1120"},"PeriodicalIF":2.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806119","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

Evidence for Multiscale Multiplexed Representation of Visual Features in EEG 脑电图中视觉特征多尺度复用表征的证据

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-02-16 DOI: 10.1162/neco_a_01649

Hamid Karimi-Rouzbahani

引用次数: 0