Neural Computation最新文献_第6页

Desiderata for Normative Models of Synaptic Plasticity 突触可塑性规范模型的基本要求

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-06-07 DOI: 10.1162/neco_a_01671

Colin Bredenberg;Cristina Savin

引用次数: 0

Data Efficiency, Dimensionality Reduction, and the Generalized Symmetric Information Bottleneck 数据效率、降维与广义对称信息瓶颈

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-06-07 DOI: 10.1162/neco_a_01667

K. Michael Martini;Ilya Nemenman

引用次数: 0

A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays 利用膜片钳和高密度微电极阵列构建单神经元模型的多模态拟合方法

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-06-07 DOI: 10.1162/neco_a_01672

Alessio Paolo Buccino;Tanguy Damart;Julian Bartram;Darshan Mandge;Xiaohan Xue;Mickael Zbili;Tobias Gänswein;Aurélien Jaquier;Vishalini Emmenegger;Henry Markram;Andreas Hierlemann;Werner Van Geit

{"title":"A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays","authors":"Alessio Paolo Buccino;Tanguy Damart;Julian Bartram;Darshan Mandge;Xiaohan Xue;Mickael Zbili;Tobias Gänswein;Aurélien Jaquier;Vishalini Emmenegger;Henry Markram;Andreas Hierlemann;Werner Van Geit","doi":"10.1162/neco_a_01672","DOIUrl":"10.1162/neco_a_01672","url":null,"abstract":"In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 7","pages":"1286-1331"},"PeriodicalIF":2.7,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10661254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082322","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

Associative Learning of an Unnormalized Successor Representation 非规范化继承表征的联想学习

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-06-07 DOI: 10.1162/neco_a_01675

Niels J. Verosky

引用次数: 0

Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks 在尖峰神经网络中提取时空特征的生物无监督延迟学习

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-06-07 DOI: 10.1162/neco_a_01674

Alireza Nadafian;Mohammad Ganjtabesh

引用次数: 0

Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration-Based Approach 稀疏广义典型相关分析：基于分布式交替迭代的方法。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-06-07 DOI: 10.1162/neco_a_01673

Kexin Lv;Jia Cai;Junyi Huo;Chao Shang;Xiaolin Huang;Jie Yang

引用次数: 0

Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits 脊髓运动电路混合中央模式发生器中的稀疏点火功能

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-04-23 DOI: 10.1162/neco_a_01660

Beck Strohmer;Elias Najarro;Jessica Ausborn;Rune W. Berg;Silvia Tolu

{"title":"Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits","authors":"Beck Strohmer;Elias Najarro;Jessica Ausborn;Rune W. Berg;Silvia Tolu","doi":"10.1162/neco_a_01660","DOIUrl":"10.1162/neco_a_01660","url":null,"abstract":"Central pattern generators are circuits generating rhythmic movements, such as walking. The majority of existing computational models of these circuits produce antagonistic output where all neurons within a population spike with a broad burst at about the same neuronal phase with respect to network output. However, experimental recordings reveal that many neurons within these circuits fire sparsely, sometimes as rarely as once within a cycle. Here we address the sparse neuronal firing and develop a model to replicate the behavior of individual neurons within rhythm-generating populations to increase biological plausibility and facilitate new insights into the underlying mechanisms of rhythm generation. The developed network architecture is able to produce sparse firing of individual neurons, creating a novel implementation for exploring the contribution of network architecture on rhythmic output. Furthermore, the introduction of sparse firing of individual neurons within the rhythm-generating circuits is one of the factors that allows for a broad neuronal phase representation of firing at the population level. This moves the model toward recent experimental findings of evenly distributed neuronal firing across phases among individual spinal neurons. The network is tested by methodically iterating select parameters to gain an understanding of how connectivity and the interplay of excitation and inhibition influence the output. This knowledge can be applied in future studies to implement a biologically plausible rhythm-generating circuit for testing biological hypotheses.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 5","pages":"759-780"},"PeriodicalIF":2.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140795919","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

Obtaining Lower Query Complexities Through Lightweight Zeroth-Order Proximal Gradient Algorithms 通过轻量级零阶近似梯度算法降低查询复杂度

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-04-23 DOI: 10.1162/neco_a_01636

Bin Gu;Xiyuan Wei;Hualin Zhang;Yi Chang;Heng Huang

{"title":"Obtaining Lower Query Complexities Through Lightweight Zeroth-Order Proximal Gradient Algorithms","authors":"Bin Gu;Xiyuan Wei;Hualin Zhang;Yi Chang;Heng Huang","doi":"10.1162/neco_a_01636","DOIUrl":"10.1162/neco_a_01636","url":null,"abstract":"Zeroth-order (ZO) optimization is one key technique for machine learning problems where gradient calculation is expensive or impossible. Several variance, reduced ZO proximal algorithms have been proposed to speed up ZO optimization for nonsmooth problems, and all of them opted for the coordinated ZO estimator against the random ZO estimator when approximating the true gradient, since the former is more accurate. While the random ZO estimator introduces a larger error and makes convergence analysis more challenging compared to coordinated ZO estimator, it requires only O(1) computation, which is significantly less than O(d) computation of the coordinated ZO estimator, with d being dimension of the problem space. To take advantage of the computationally efficient nature of the random ZO estimator, we first propose a ZO objective decrease (ZOOD) property that can incorporate two different types of errors in the upper bound of convergence rate. Next, we propose two generic reduction frameworks for ZO optimization, which can automatically derive the convergence results for convex and nonconvex problems, respectively, as long as the convergence rate for the inner solver satisfies the ZOOD property. With the application of two reduction frameworks on our proposed ZOR-ProxSVRG and ZOR-ProxSAGA, two variance-reduced ZO proximal algorithms with fully random ZO estimators, we improve the state-of-the-art function query complexities from Omindn1/2ε2,dε3 to O˜n+dε2 under d>n12 for nonconvex problems, and from Odε2 to O˜nlog1ε+dε for convex problems. Finally, we conduct experiments to verify the superiority of our proposed methods.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 5","pages":"897-935"},"PeriodicalIF":2.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066270","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

An Overview of the Free Energy Principle and Related Research 自由能原理及相关研究概述。

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-04-23 DOI: 10.1162/neco_a_01642

Zhengquan Zhang;Feng Xu

{"title":"An Overview of the Free Energy Principle and Related Research","authors":"Zhengquan Zhang;Feng Xu","doi":"10.1162/neco_a_01642","DOIUrl":"10.1162/neco_a_01642","url":null,"abstract":"The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making—within an agent—are all driven by the objective of “minimizing free energy,” evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 5","pages":"963-1021"},"PeriodicalIF":2.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066224","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

Approximating Nonlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiking Networks 在低函数兴奋-抑制尖峰网络中利用潜在边界逼近非线性函数

IF 2.9 4区计算机科学

Neural Computation Pub Date : 2024-04-23 DOI: 10.1162/neco_a_01658

William F. Podlaski;Christian K. Machens

{"title":"Approximating Nonlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiking Networks","authors":"William F. Podlaski;Christian K. Machens","doi":"10.1162/neco_a_01658","DOIUrl":"10.1162/neco_a_01658","url":null,"abstract":"Deep feedforward and recurrent neural networks have become successful functional models of the brain, but they neglect obvious biological details such as spikes and Dale's law. Here we argue that these details are crucial in order to understand how real neural circuits operate. Towards this aim, we put forth a new framework for spike-based computation in low-rank excitatory-inhibitory spiking networks. By considering populations with rank-1 connectivity, we cast each neuron's spiking threshold as a boundary in a low-dimensional input-output space. We then show how the combined thresholds of a population of inhibitory neurons form a stable boundary in this space, and those of a population of excitatory neurons form an unstable boundary. Combining the two boundaries results in a rank-2 excitatory-inhibitory (EI) network with inhibition-stabilized dynamics at the intersection of the two boundaries. The computation of the resulting networks can be understood as the difference of two convex functions and is thereby capable of approximating arbitrary non-linear input-output mappings. We demonstrate several properties of these networks, including noise suppression and amplification, irregular activity and synaptic balance, as well as how they relate to rate network dynamics in the limit that the boundary becomes soft. Finally, while our work focuses on small networks (5-50 neurons), we discuss potential avenues for scaling up to much larger networks. Overall, our work proposes a new perspective on spiking networks that may serve as a starting point for a mechanistic understanding of biological spike-based computation.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"36 5","pages":"803-857"},"PeriodicalIF":2.9,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805834","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