Neural Computation最新文献

Bounded Rational Decision Networks With Belief Propagation 带信念传播的有界理性决策网络。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-12 DOI: 10.1162/neco_a_01719

Gerrit Schmid;Sebastian Gottwald;Daniel A. Braun

{"title":"Bounded Rational Decision Networks With Belief Propagation","authors":"Gerrit Schmid;Sebastian Gottwald;Daniel A. Braun","doi":"10.1162/neco_a_01719","DOIUrl":"10.1162/neco_a_01719","url":null,"abstract":"Complex information processing systems that are capable of a wide variety of tasks, such as the human brain, are composed of specialized units that collaborate and communicate with each other. An important property of such information processing networks is locality: there is no single global unit controlling the modules, but information is exchanged locally. Here, we consider a decision-theoretic approach to study networks of bounded rational decision makers that are allowed to specialize and communicate with each other. In contrast to previous work that has focused on feedforward communication between decision-making agents, we consider cyclical information processing paths allowing for back-and-forth communication. We adapt message-passing algorithms to suit this purpose, essentially allowing for local information flow between units and thus enabling circular dependency structures. We provide examples that show how repeated communication can increase performance given that each unit’s information processing capability is limited and that decision-making systems with too few or too many connections and feedback loops achieve suboptimal utility.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 1","pages":"76-127"},"PeriodicalIF":2.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395372","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

Computation With Sequences of Assemblies in a Model of the Brain 用大脑模型中的集合序列进行计算

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-12 DOI: 10.1162/neco_a_01720

Max Dabagia;Christos H. Papadimitriou;Santosh S. Vempala

{"title":"Computation With Sequences of Assemblies in a Model of the Brain","authors":"Max Dabagia;Christos H. Papadimitriou;Santosh S. Vempala","doi":"10.1162/neco_a_01720","DOIUrl":"10.1162/neco_a_01720","url":null,"abstract":"Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain’s learning capabilities remain unmatched. How cognition arises from neural activity is the central open question in neuroscience, inextricable from the study of intelligence itself. A simple formal model of neural activity was proposed in Papadimitriou et al. (2020) and has been subsequently shown, through both mathematical proofs and simulations, to be capable of implementing certain simple cognitive operations via the creation and manipulation of assemblies of neurons. However, many intelligent behaviors rely on the ability to recognize, store, and manipulate temporal sequences of stimuli (planning, language, navigation, to list a few). Here we show that in the same model, sequential precedence can be captured naturally through synaptic weights and plasticity, and, as a result, a range of computations on sequences of assemblies can be carried out. In particular, repeated presentation of a sequence of stimuli leads to the memorization of the sequence through corresponding neural assemblies: upon future presentation of any stimulus in the sequence, the corresponding assembly and its subsequent ones will be activated, one after the other, until the end of the sequence. If the stimulus sequence is presented to two brain areas simultaneously, a scaffolded representation is created, resulting in more efficient memorization and recall, in agreement with cognitive experiments. Finally, we show that any finite state machine can be learned in a similar way, through the presentation of appropriate patterns of sequences. Through an extension of this mechanism, the model can be shown to be capable of universal computation. Taken together, these results provide a concrete hypothesis for the basis of the brain’s remarkable abilities to compute and learn, with sequences playing a vital role.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 1","pages":"193-233"},"PeriodicalIF":2.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395373","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

Computing With Residue Numbers in High-Dimensional Representation 用高维表示法计算残差数

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-12 DOI: 10.1162/neco_a_01723

Christopher J. Kymn;Denis Kleyko;E. Paxon Frady;Connor Bybee;Pentti Kanerva;Friedrich T. Sommer;Bruno A. Olshausen

引用次数: 0

Selective Inference for Change Point Detection by Recurrent Neural Network 利用递归神经网络进行变化点检测的选择性推理

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-12 DOI: 10.1162/neco_a_01724

Tomohiro Shiraishi;Daiki Miwa;Vo Nguyen Le Duy;Ichiro Takeuchi

引用次数: 0

Relating Human Error–Based Learning to Modern Deep RL Algorithms 将基于人类错误的学习与现代深度 RL 算法联系起来。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-12 DOI: 10.1162/neco_a_01721

Michele Garibbo;Casimir J. H. Ludwig;Nathan F. Lepora;Laurence Aitchison

引用次数: 0

Realizing Synthetic Active Inference Agents, Part II: Variational Message Updates 实现合成主动推理代理，第二部分：变异信息更新。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-12 DOI: 10.1162/neco_a_01713

Thijs van de Laar;Magnus Koudahl;Bert de Vries

{"title":"Realizing Synthetic Active Inference Agents, Part II: Variational Message Updates","authors":"Thijs van de Laar;Magnus Koudahl;Bert de Vries","doi":"10.1162/neco_a_01713","DOIUrl":"10.1162/neco_a_01713","url":null,"abstract":"The free energy principle (FEP) describes (biological) agents as minimizing a variational free energy (FE) with respect to a generative model of their environment. Active inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimizing an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF by message passing on free-form Forney-style factor graphs (FFGs). A companion paper (part I of this article; Koudahl et al., 2023) introduces a constrained FFG (CFFG) notation that visually represents (generalized) FE objectives for AIF. This article (part II) derives message-passing algorithms that minimize (generalized) FE objectives on a CFFG by variational calculus. A comparison between simulated Bethe and generalized FE agents illustrates how the message-passing approach to synthetic AIF induces epistemic behavior on a T-maze navigation task. Extension of the T-maze simulation to learning goal statistics and a multiagent bargaining setting illustrate how this approach encourages reuse of nodes and updates in alternative settings. With a full message-passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models and move closer to industrial applications of synthetic AIF.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 1","pages":"38-75"},"PeriodicalIF":2.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309093","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 Fast Algorithm for the Real-Valued Combinatorial Pure Exploration of the Multi-Armed Bandit.

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-02 DOI: 10.1162/neco_a_01728

Shintaro Nakamura, Masashi Sugiyama

引用次数: 0

On the Compressive Power of Autoencoders With Linear and ReLU Activation Functions.

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-02 DOI: 10.1162/neco_a_01729

Liangjie Sun, Chenyao Wu, Wai-Ki Ching, Tatsuya Akutsu

{"title":"On the Compressive Power of Autoencoders With Linear and ReLU Activation Functions.","authors":"Liangjie Sun, Chenyao Wu, Wai-Ki Ching, Tatsuya Akutsu","doi":"10.1162/neco_a_01729","DOIUrl":"https://doi.org/10.1162/neco_a_01729","url":null,"abstract":"In this letter, we mainly study the depth and width of autoencoders consisting of rectified linear unit (ReLU) activation functions. An autoencoder is a layered neural network consisting of an encoder, which compresses an input vector to a lower-dimensional vector, and a decoder, which transforms the low-dimensional vector back to the original input vector exactly (or approximately). In a previous study, Melkman et al. (2023) studied the depth and width of autoencoders using linear threshold activation functions with binary input and output vectors. We show that similar theoretical results hold if autoencoders using ReLU activation functions with real input and output vectors are used. Furthermore, we show that it is possible to compress input vectors to one-dimensional vectors using ReLU activation functions, although the size of compressed vectors is trivially Ω(log n) for autoencoders with linear threshold activation functions, where n is the number of input vectors. We also study the cases of linear activation functions. The results suggest that the compressive power of autoencoders using linear activation functions is considerably limited compared with those using ReLU activation functions.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-25"},"PeriodicalIF":2.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774849","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 in Associative Networks Through Pavlovian Dynamics.

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-12-02 DOI: 10.1162/neco_a_01730

Daniele Lotito, Miriam Aquaro, Chiara Marullo

{"title":"Learning in Associative Networks Through Pavlovian Dynamics.","authors":"Daniele Lotito, Miriam Aquaro, Chiara Marullo","doi":"10.1162/neco_a_01730","DOIUrl":"https://doi.org/10.1162/neco_a_01730","url":null,"abstract":"Hebbian learning theory is rooted in Pavlov's classical conditioning While mathematical models of the former have been proposed and studied in the past decades, especially in spin glass theory, only recently has it been numerically shown that it is possible to write neural and synaptic dynamics that mirror Pavlov conditioning mechanisms and also give rise to synaptic weights that correspond to the Hebbian learning rule. In this letter we show that the same dynamics can be derived with equilibrium statistical mechanics tools and basic and motivated modeling assumptions. Then we show how to study the resulting system of coupled stochastic differential equations assuming the reasonable separation of neural and synaptic timescale. In particular, we analytically demonstrate that this synaptic evolution converges to the Hebbian learning rule in various settings and compute the variance of the stochastic process. Finally, drawing from evidence on pure memory reinforcement during sleep stages, we show how the proposed model can simulate neural networks that undergo sleep-associated memory consolidation processes, thereby proving the compatibility of Pavlovian learning with dreaming mechanisms.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":" ","pages":"1-33"},"PeriodicalIF":2.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774845","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 Fast Algorithm for All-Pairs-Shortest-Paths Suitable for Neural Networks 适合神经网络的全对最短路径快速算法

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2024-11-19 DOI: 10.1162/neco_a_01716

Zeyu Jing;Markus Meister

引用次数: 0