Neural Computation最新文献_第3页

Neural Code Translation With LIF Neuron Microcircuits 用LIF神经元微电路进行神经代码翻译。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01754

Ville Karlsson;Joni Kämäräinen

{"title":"Neural Code Translation With LIF Neuron Microcircuits","authors":"Ville Karlsson;Joni Kämäräinen","doi":"10.1162/neco_a_01754","DOIUrl":"10.1162/neco_a_01754","url":null,"abstract":"Spiking neural networks (SNNs) provide an energy-efficient alternative to traditional artificial neural networks, leveraging diverse neural encoding schemes such as rate, time-to-first-spike (TTFS), and population-based binary codes. Each encoding method offers distinct advantages: TTFS enables rapid and precise transmission with minimal energy use, rate encoding provides robust signal representation, and binary population encoding aligns well with digital hardware implementations. This letter introduces a set of neural microcircuits based on leaky integrate-and-fire neurons that enable translation between these encoding schemes. We propose two applications showcasing the utility of these microcircuits. First, we demonstrate a number comparison operation that significantly reduces spike transmission by switching from rate to TTFS encoding. Second, we present a high-bandwidth neural transmitter capable of encoding and transmitting binary population-encoded data through a single axon and reconstructing it at the target site. Additionally, we conduct a detailed analysis of these microcircuits, providing quantitative metrics to assess their efficiency in terms of neuron count, synaptic complexity, spike overhead, and runtime. Our findings highlight the potential of LIF neuron microcircuits in computational neuroscience and neuromorphic computing, offering a pathway to more interpretable and efficient SNN designs.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 6","pages":"1124-1153"},"PeriodicalIF":2.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046411","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

Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks 自适应指数型积分网络平均场模型的动力学和分岔结构。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01758

Lionel Kusch;Damien Depannemaecker;Alain Destexhe;Viktor Jirsa

{"title":"Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks","authors":"Lionel Kusch;Damien Depannemaecker;Alain Destexhe;Viktor Jirsa","doi":"10.1162/neco_a_01758","DOIUrl":"10.1162/neco_a_01758","url":null,"abstract":"The study of brain activity spans diverse scales and levels of description and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking networks presents challenges for understanding their dynamics. To tackle this, a mean-field formulation offers a potential approach for dimensionality reduction while retaining essential elements. Here, we focus on a previously developed mean-field model of adaptive exponential integrate and fire (AdEx) networks used in various research work. We observe qualitative similarities in the bifurcation structure but quantitative differences in mean firing rates between the mean-field model and AdEx spiking network simulations. Even if the mean-field model does not accurately predict phase shift during transients and oscillatory input, it generally captures the qualitative dynamics of the spiking network’s response to both constant and varying inputs. Finally, we offer an overview of the dynamical properties of the AdExMF to assist future users in interpreting their results of simulations.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 6","pages":"1102-1123"},"PeriodicalIF":2.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045166","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

Memory States From Almost Nothing: Representing and Computing in a Nonassociative Algebra 记忆状态：非结合代数的表示与计算。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01755

Stefan Reimann

{"title":"Memory States From Almost Nothing: Representing and Computing in a Nonassociative Algebra","authors":"Stefan Reimann","doi":"10.1162/neco_a_01755","DOIUrl":"10.1162/neco_a_01755","url":null,"abstract":"This letter presents a nonassociative algebraic framework for the representation and computation of information items in high-dimensional space. This framework is consistent with the principles of spatial computing and with the empirical findings in cognitive science about memory. Computations are performed through a process of multiplication-like binding and nonassociative interference-like bundling. Models that rely on associative bundling typically lose order information, which necessitates the use of auxiliary order structures, such as position markers, to represent sequential information that is important for cognitive tasks. In contrast, the nonassociative bundling proposed allows the construction of sparse representations of arbitrarily long sequences that maintain their temporal structure across arbitrary lengths. In this operation, noise is a constituent element of the representation of order information rather than a means of obscuring it. The nonassociative nature of the proposed framework results in the representation of a single sequence by two distinct states. The L-state, generated through left-associative bundling, continuously updates and emphasizes a recency effect, while the R-state, formed through right-associative bundling, encodes finite sequences or chunks, capturing a primacy effect. The construction of these states may be associated with activity in the prefrontal cortex in relation to short-term memory and hippocampal encoding in long-term memory, respectively. The accuracy of retrieval is contingent on a decision-making process that is based on the mutual information between the memory states and the cue. The model is able to replicate the serial position curve, which reflects the empirical recency and primacy effects observed in cognitive experiments.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 6","pages":"1154-1170"},"PeriodicalIF":2.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057372","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

Low-Rank, High-Order Tensor Completion via t- Product-Induced Tucker (tTucker) Decomposition 通过t积诱导塔克（tTucker）分解的低秩、高阶张量补全。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-14 DOI: 10.1162/neco_a_01756

Yaodong Li;Jun Tan;Peilin Yang;Guoxu Zhou;Qibin Zhao

引用次数: 0

Replay as a Basis for Backpropagation Through Time in the Brain 回放作为大脑中穿越时间反向传播的基础。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-02-14 DOI: 10.1162/neco_a_01735

Huzi Cheng;Joshua W. Brown

{"title":"Replay as a Basis for Backpropagation Through Time in the Brain","authors":"Huzi Cheng;Joshua W. Brown","doi":"10.1162/neco_a_01735","DOIUrl":"10.1162/neco_a_01735","url":null,"abstract":"How episodic memories are formed in the brain is a continuing puzzle for the neuroscience community. The brain areas that are critical for episodic learning (e.g., the hippocampus) are characterized by recurrent connectivity and generate frequent offline replay events. The function of the replay events is a subject of active debate. Recurrent connectivity, computational simulations show, enables sequence learning when combined with a suitable learning algorithm such as backpropagation through time (BPTT). BPTT, however, is not biologically plausible. We describe here, for the first time, a biologically plausible variant of BPTT in a reversible recurrent neural network, R2N2, that critically leverages offline replay to support episodic learning. The model uses forward and backward offline replay to transfer information between two recurrent neural networks, a cache and a consolidator, that perform rapid one-shot learning and statistical learning, respectively. Unlike replay in standard BPTT, this architecture requires no artificial external memory store. This approach outperforms existing solutions like random feedback local online learning and reservoir network. It also accounts for the functional significance of hippocampal replay events. We demonstrate the R2N2 network properties using benchmark tests from computer science and simulate the rodent delayed alternation T-maze task.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 3","pages":"403-436"},"PeriodicalIF":2.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958829","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

Gradual Domain Adaptation via Normalizing Flows 通过规范化流程逐步适应领域。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-02-14 DOI: 10.1162/neco_a_01734

Shogo Sagawa;Hideitsu Hino

引用次数: 0

Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm 用数据驱动SINDy算法揭示随机决策模型的动力学方程。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-02-14 DOI: 10.1162/neco_a_01736

Brendan Lenfesty;Saugat Bhattacharyya;KongFatt Wong-Lin

{"title":"Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm","authors":"Brendan Lenfesty;Saugat Bhattacharyya;KongFatt Wong-Lin","doi":"10.1162/neco_a_01736","DOIUrl":"10.1162/neco_a_01736","url":null,"abstract":"Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioral data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-driven approach, to elucidate the deterministic linear and nonlinear components of often-used stochastic decision models within reaction time task paradigms. Based on the simulated decision variable activities of the models and assuming the noise coefficient term is known beforehand, SINDy, enhanced with approaches using multiple trials, could readily estimate the deterministic terms in the dynamical equations, choice accuracy, and decision time of the models across a range of signal-to-noise ratio values. In particular, SINDy performed the best using the more memory-intensive multi-trial approach while trial-averaging of parameters performed more moderately. The single-trial approach, although expectedly not performing as well, may be useful for real-time modeling. Taken together, our work offers alternative approaches for SINDy to uncover the dynamics in perceptual decision making and, more generally, for first-passage time problems.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 3","pages":"569-587"},"PeriodicalIF":2.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958974","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

Toward a Free-Response Paradigm of Decision Making in Spiking Neural Networks 基于脉冲神经网络的自由反应决策模式研究。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-02-14 DOI: 10.1162/neco_a_01733

Zhichao Zhu;Yang Qi;Wenlian Lu;Zhigang Wang;Lu Cao;Jianfeng Feng

{"title":"Toward a Free-Response Paradigm of Decision Making in Spiking Neural Networks","authors":"Zhichao Zhu;Yang Qi;Wenlian Lu;Zhigang Wang;Lu Cao;Jianfeng Feng","doi":"10.1162/neco_a_01733","DOIUrl":"10.1162/neco_a_01733","url":null,"abstract":"Spiking neural networks (SNNs) have attracted significant interest in the development of brain-inspired computing systems due to their energy efficiency and similarities to biological information processing. In contrast to continuous-valued artificial neural networks, which produce results in a single step, SNNs require multiple steps during inference to achieve a desired accuracy level, resulting in a burden in real-time response and energy efficiency. Inspired by the tradeoff between speed and accuracy in human and animal decision-making processes, which exhibit correlations among reaction times, task complexity, and decision confidence, an inquiry emerges regarding how an SNN model can benefit by implementing these attributes. Here, we introduce a theory of decision making in SNNs by untangling the interplay between signal and noise. Under this theory, we introduce a new learning objective that trains an SNN not only to make the correct decisions but also to shape its confidence. Numerical experiments demonstrate that SNNs trained in this way exhibit improved confidence expression, reduced trial-to-trial variability, and shorter latency to reach the desired accuracy. We then introduce a stopping policy that can stop inference in a way that further enhances the time efficiency of SNNs. The stopping time can serve as an indicator to whether a decision is correct, akin to the reaction time in animal behavior experiments. By integrating stochasticity into decision making, this study opens up new possibilities to explore the capabilities of SNNs and advance SNNs and their applications in complex decision-making scenarios where model performance is limited.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 3","pages":"481-521"},"PeriodicalIF":2.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958972","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

Improving Recall in Sparse Associative Memories That Use Neurogenesis 利用神经发生提高稀疏联想记忆的回忆准确性。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-02-14 DOI: 10.1162/neco_a_01732

Katy Warr;Jonathon Hare;David Thomas

{"title":"Improving Recall in Sparse Associative Memories That Use Neurogenesis","authors":"Katy Warr;Jonathon Hare;David Thomas","doi":"10.1162/neco_a_01732","DOIUrl":"10.1162/neco_a_01732","url":null,"abstract":"The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their noisy variants. Solutions to this problem may be required to memorize vast numbers of patterns based on limited training data and subsequently recall the patterns in the presence of noise. To solve this problem, previous work has explored sparse associative memory (SAM)—associative memory neural models that exploit the principle of sparse neural coding observed in the brain. Research into a subcategory of SAM has been inspired by the biological process of adult neurogenesis, whereby new neurons are generated to facilitate adaptive and effective lifelong learning. Although these neurogenesis models have been demonstrated in previous research, they have limitations in terms of recall memory capacity and robustness to noise. In this article, we provide a unifying framework for characterizing a type of SAM network that has been pretrained using a learning strategy that incorporated a simple neurogenesis model. Using this characterization, we formally define network topology and threshold optimization methods to empirically demonstrate greater than 104 times improvement in memory capacity compared to previous work. We show that these optimizations can facilitate the development of networks that have reduced interneuron connectivity while maintaining high recall efficacy. This paves the way for ongoing research into fast, effective, low-power realizations of associative memory on neuromorphic platforms.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 3","pages":"437-480"},"PeriodicalIF":2.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958803","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 多臂强盗实值组合纯探索的快速算法。

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-01-21 DOI: 10.1162/neco_a_01728

Shintaro Nakamura;Masashi Sugiyama

引用次数: 0