Neural Computation最新文献_第2页

Context-Sensitive Processing in a Model Neocortical Pyramidal Cell With Two Sites of Input Integration

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-18 DOI: 10.1162/neco_a_01739

Bruce P. Graham;Jim W. Kay;William A. Phillips

{"title":"Context-Sensitive Processing in a Model Neocortical Pyramidal Cell With Two Sites of Input Integration","authors":"Bruce P. Graham;Jim W. Kay;William A. Phillips","doi":"10.1162/neco_a_01739","DOIUrl":"10.1162/neco_a_01739","url":null,"abstract":"Neocortical layer 5 thick-tufted pyramidal cells are prone to exhibiting burst firing on receipt of coincident basal and apical dendritic inputs. These inputs carry different information, with basal inputs coming from feedforward sensory pathways and apical inputs coming from diverse sources that provide context in the cortical hierarchy. We explore the information processing possibilities of this burst firing using computer simulations of a noisy compartmental cell model. Simulated data on stochastic burst firing due to brief, simultaneously injected basal and apical currents allow estimation of burst firing probability for different stimulus current amplitudes. Information-theory-based partial information decomposition (PID) is used to quantify the contributions of the apical and basal input streams to the information in the cell output bursting probability. Four different operating regimes are apparent, depending on the relative strengths of the input streams, with output burst probability carrying more or less information that is uniquely contributed by either the basal or apical input, or shared and synergistic information due to the combined streams. We derive and fit transfer functions for these different regimes that describe burst probability over the different ranges of basal and apical input amplitudes. The operating regimes can be classified into distinct modes of information processing, depending on the contribution of apical input to output bursting: apical cooperation, in which both basal and apical inputs are required to generate a burst; apical amplification, in which basal input alone can generate a burst but the burst probability is modulated by apical input; apical drive, in which apical input alone can produce a burst; and apical integration, in which strong apical or basal inputs alone, as well as their combination, can generate bursting. In particular, PID and the transfer function clarify that the apical amplification mode has the features required for contextually modulated information processing.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 4","pages":"588-634"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607147","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

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-18 DOI: 10.1162/neco_a_01743

Hanna Pankka;Jaakko Lehtinen;Risto J. Ilmoniemi;Timo Roine

{"title":"Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach","authors":"Hanna Pankka;Jaakko Lehtinen;Risto J. Ilmoniemi;Timo Roine","doi":"10.1162/neco_a_01743","DOIUrl":"10.1162/neco_a_01743","url":null,"abstract":"Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain–computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task; however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4–7.5 Hz) and alpha-frequency (8–13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies 150 ms ahead, with mean absolute errors of 1.0 ± 1.1 µV (theta) and 0.9 ± 1.1 µV (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be used to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain–computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 4","pages":"793-814"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607149","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

Spiking Neuron-Astrocyte Networks for Image Recognition

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-18 DOI: 10.1162/neco_a_01740

Jhunlyn Lorenzo;Juan-Antonio Rico-Gallego;Stéphane Binczak;Sabir Jacquir

{"title":"Spiking Neuron-Astrocyte Networks for Image Recognition","authors":"Jhunlyn Lorenzo;Juan-Antonio Rico-Gallego;Stéphane Binczak;Sabir Jacquir","doi":"10.1162/neco_a_01740","DOIUrl":"10.1162/neco_a_01740","url":null,"abstract":"From biological and artificial network perspectives, researchers have started acknowledging astrocytes as computational units mediating neural processes. Here, we propose a novel biologically inspired neuron-astrocyte network model for image recognition, one of the first attempts at implementing astrocytes in spiking neuron networks (SNNs) using a standard data set. The architecture for image recognition has three primary units: the preprocessing unit for converting the image pixels into spiking patterns, the neuron-astrocyte network forming bipartite (neural connections) and tripartite synapses (neural and astrocytic connections), and the classifier unit. In the astrocyte-mediated SNNs, an astrocyte integrates neural signals following the simplified Postnov model. It then modulates the integrate-and-fire (IF) neurons via gliotransmission, thereby strengthening the synaptic connections of the neurons within the astrocytic territory. We develop an architecture derived from a baseline SNN model for unsupervised digit classification. The spiking neuron-astrocyte networks (SNANs) display better network performance with an optimal variance-bias trade-off than SNN alone. We demonstrate that astrocytes promote faster learning, support memory formation and recognition, and provide a simplified network architecture. Our proposed SNAN can serve as a benchmark for future researchers on astrocyte implementation in artificial networks, particularly in neuromorphic systems, for its simplified design.","PeriodicalId":54731,"journal":{"name":"Neural Computation","volume":"37 4","pages":"635-665"},"PeriodicalIF":2.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607154","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 Generalized Time Rescaling Theorem for Temporal Point Processes.

IF 2.7 4区计算机科学

Neural Computation Pub Date : 2025-03-03 DOI: 10.1162/neco_a_01745

Xi Zhang, Akshay Aravamudan, Georgios C Anagnostopoulos

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