Neural Computation最新文献_第8页

Column Row Convolutional Neural Network: Reducing Parameters for Efficient Image Processing 列行卷积神经网络：减少参数，实现高效图像处理

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01653

Seongil Im;Jae-Seung Jeong;Junseo Lee;Changhwan Shin;Jeong Ho Cho;Hyunsu Ju

引用次数: 0

Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks 频率传播：非线性物理网络中的多机制学习

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01648

Vidyesh Rao Anisetti;Ananth Kandala;Benjamin Scellier;J. M. Schwarz

{"title":"Frequency Propagation: Multimechanism Learning in Nonlinear Physical Networks","authors":"Vidyesh Rao Anisetti;Ananth Kandala;Benjamin Scellier;J. M. Schwarz","doi":"10.1162/neco_a_01648","DOIUrl":"10.1162/neco_a_01648","url":null,"abstract":"We introduce frequency propagation, a learning algorithm for nonlinear physical networks. In a resistive electrical circuit with variable resistors, an activation current is applied at a set of input nodes at one frequency and an error current is applied at a set of output nodes at another frequency. The voltage response of the circuit to these boundary currents is the superposition of an activation signal and an error signal whose coefficients can be read in different frequencies of the frequency domain. Each conductance is updated proportionally to the product of the two coefficients. The learning rule is local and proved to perform gradient descent on a loss function. We argue that frequency propagation is an instance of a multimechanism learning strategy for physical networks, be it resistive, elastic, or flow networks. Multimechanism learning strategies incorporate at least two physical quantities, potentially governed by independent physical mechanisms, to act as activation and error signals in the training process. Locally available information about these two signals is then used to update the trainable parameters to perform gradient descent. We demonstrate how earlier work implementing learning via chemical signaling in flow networks (Anisetti, Scellier, et al., 2023) also falls under the rubric of multimechanism learning.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 4","pages":"596-620"},"PeriodicalIF":2.9,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066264","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

Learning Korobov Functions by Correntropy and Convolutional Neural Networks 通过熵和卷积神经网络学习 Korobov 函数

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01650

Zhiying Fang;Tong Mao;Jun Fan

引用次数: 0

Lateral Connections Improve Generalizability of Learning in a Simple Neural Network 侧向连接提高了简单神经网络学习的通用性

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01640

Garrett Crutcher

{"title":"Lateral Connections Improve Generalizability of Learning in a Simple Neural Network","authors":"Garrett Crutcher","doi":"10.1162/neco_a_01640","DOIUrl":"10.1162/neco_a_01640","url":null,"abstract":"To navigate the world around us, neural circuits rapidly adapt to their environment learning generalizable strategies to decode information. When modeling these learning strategies, network models find the optimal solution to satisfy one task condition but fail when introduced to a novel task or even a different stimulus in the same space. In the experiments described in this letter, I investigate the role of lateral gap junctions in learning generalizable strategies to process information. Lateral gap junctions are formed by connexin proteins creating an open pore that allows for direct electrical signaling between two neurons. During neural development, the rate of gap junctions is high, and daughter cells that share similar tuning properties are more likely to be connected by these junctions. Gap junctions are highly plastic and get heavily pruned throughout development. I hypothesize that they mediate generalized learning by imprinting the weighting structure within a layer to avoid overfitting to one task condition. To test this hypothesis, I implemented a feedforward probabilistic neural network mimicking a cortical fast spiking neuron circuit that is heavily involved in movement. Many of these cells are tuned to speeds that I used as the input stimulus for the network to estimate. When training this network using a delta learning rule, both a laterally connected network and an unconnected network can estimate a single speed. However, when asking the network to estimate two or more speeds, alternated in training, an unconnected network either cannot learn speed or optimizes to a singular speed, while the laterally connected network learns the generalizable strategy and can estimate both speeds. These results suggest that lateral gap junctions between neurons enable generalized learning, which may help explain learning differences across life span.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 4","pages":"705-717"},"PeriodicalIF":2.9,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066266","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

Probing the Structure and Functional Properties of the Dropout-Induced Correlated Variability in Convolutional Neural Networks 探究卷积神经网络中由辍学引发的相关变异性的结构和功能特性

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-03-21 DOI: 10.1162/neco_a_01652

Xu Pan;Ruben Coen-Cagli;Odelia Schwartz

{"title":"Probing the Structure and Functional Properties of the Dropout-Induced Correlated Variability in Convolutional Neural Networks","authors":"Xu Pan;Ruben Coen-Cagli;Odelia Schwartz","doi":"10.1162/neco_a_01652","DOIUrl":"10.1162/neco_a_01652","url":null,"abstract":"Computational neuroscience studies have shown that the structure of neural variability to an unchanged stimulus affects the amount of information encoded. Some artificial deep neural networks, such as those with Monte Carlo dropout layers, also have variable responses when the input is fixed. However, the structure of the trial-by-trial neural covariance in neural networks with dropout has not been studied, and its role in decoding accuracy is unknown. We studied the above questions in a convolutional neural network model with dropout in both the training and testing phases. We found that trial-by-trial correlation between neurons (i.e., noise correlation) is positive and low dimensional. Neurons that are close in a feature map have larger noise correlation. These properties are surprisingly similar to the findings in the visual cortex. We further analyzed the alignment of the main axes of the covariance matrix. We found that different images share a common trial-by-trial noise covariance subspace, and they are aligned with the global signal covariance. This evidence that the noise covariance is aligned with signal covariance suggests that noise covariance in dropout neural networks reduces network accuracy, which we further verified directly with a trial-shuffling procedure commonly used in neuroscience. These findings highlight a previously overlooked aspect of dropout layers that can affect network performance. Such dropout networks could also potentially be a computational model of neural variability.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 4","pages":"621-644"},"PeriodicalIF":2.9,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066271","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

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