Proceedings of the ... Conference on Cognitive Computational Neuroscience最新文献

Invariant correlation: a general and straightforward method to quantify invariance in time-varying neural signals. 不变性相关：一种通用和直接的方法来量化时变神经信号的不变性。

Proceedings of the ... Conference on Cognitive Computational Neuroscience Pub Date : 2026-01-01 DOI: 10.32470/srbsjd0

Jarrod M Hicks, Dana Boebinger, Kirill V Nourski, Matthew A Howard, Christopher M Garcia, Thomas Wychowski, Webster Pilcher, Sam V Norman-Haignere

{"title":"Invariant correlation: a general and straightforward method to quantify invariance in time-varying neural signals.","authors":"Jarrod M Hicks, Dana Boebinger, Kirill V Nourski, Matthew A Howard, Christopher M Garcia, Thomas Wychowski, Webster Pilcher, Sam V Norman-Haignere","doi":"10.32470/srbsjd0","DOIUrl":"10.32470/srbsjd0","url":null,"abstract":"Recognizing information in natural stimuli is challenging because sensory inputs for different instances of the same feature often vary substantially. Successful recognition thus requires neural systems to generate representations that are invariant to such variation, posing a fundamental computational challenge for sensory coding. Much remains unknown about how sensory systems code invariant information in complex, temporally dynamic stimuli such as speech, in part due to methodological challenges in measuring and modeling invariance from noisy, time-varying neural responses. Here, we introduce the \"invariant correlation\", a straightforward and general method for directly quantifying the strength and temporal dynamics of invariance from time-varying neural signals and computational models. We demonstrate its utility by quantifying invariance to acoustic variation in neural representations of phonemes using spatiotemporally precise intracranial recordings from human auditory cortex. Our method successfully revealed substantial diversity in the strength and dynamics of invariance across the human auditory cortex and across different phonetic features within individual electrodes. We illustrate how many of these empirically measured patterns can be explained by applying the same analysis to the predictions from a simple computational model based on spectrotemporal tuning in a cochleagram representation of sound. Although we focus here on speech, the invariant correlation is a broadly applicable tool for characterizing the organization and computational mechanisms underlying invariant representations of dynamic stimuli across sensory modalities.","PeriodicalId":524209,"journal":{"name":"Proceedings of the ... Conference on Cognitive Computational Neuroscience","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13685575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149248480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implications of hierarchical Markov models of behavior: on irreversibility, predictability, and dimensionality. 行为的层次马尔可夫模型的含义：关于不可逆性、可预测性和维度。

Proceedings of the ... Conference on Cognitive Computational Neuroscience Pub Date : 2026-01-01 DOI: 10.32470/06d965e7

John J Vastola, Kanaka Rajan

{"title":"Implications of hierarchical Markov models of behavior: on irreversibility, predictability, and dimensionality.","authors":"John J Vastola, Kanaka Rajan","doi":"10.32470/06d965e7","DOIUrl":"https://doi.org/10.32470/06d965e7","url":null,"abstract":"The maturation of quantitative tools for studying the high-level structure of animal behavior, and especially tools which represent spontaneous behavior as a sequence of stereotyped and neurally well-defined 'syllables', demands that the field revisit a fundamental theoretical question: if the coarse structure of behavior can be accurately described by Markov models, what do these models really tell us about behavior? In this work, we explore the theoretical implications of these models and discuss how they allow us to quantitatively formulate questions about the sequence-like nature and effective dimensionality of behavior. One important insight is that the eigenvalues and eigenvectors of various model-associated matrices furnish interpretable time scales and modifications of behavior that occur on those time scales. We illustrate our points using both toy examples and Markov models fit to real data. By analyzing the consequences of Markov representations, we clarify the theoretical meaning of progress in quantifying behavior.","PeriodicalId":524209,"journal":{"name":"Proceedings of the ... Conference on Cognitive Computational Neuroscience","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents. 使用深度强化学习训练的RNN代理解剖斑马鱼幼虫狩猎。

Proceedings of the ... Conference on Cognitive Computational Neuroscience Pub Date : 2026-01-01 DOI: 10.32470/h4pp9b0

Raaghav Malik, Satpreet H Singh, Sonja Johnson-Yu, Nathan Wu, Roy Harpaz, Florian Engert, Kanaka Rajan

{"title":"Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents.","authors":"Raaghav Malik, Satpreet H Singh, Sonja Johnson-Yu, Nathan Wu, Roy Harpaz, Florian Engert, Kanaka Rajan","doi":"10.32470/h4pp9b0","DOIUrl":"https://doi.org/10.32470/h4pp9b0","url":null,"abstract":"Larval zebrafish hunting provides a tractable setting to study how ecological and energetic constraints shape adaptive behavior in both biological brains and artificial agents. Here we develop a minimal agent-based model, training recurrent policies with deep reinforcement learning in a bout-based zebrafish simulator. Despite its simplicity, the model reproduces hallmark hunting behaviors-including eye vergence-linked pursuit, speed modulation, and stereotyped approach trajectories-that closely match real larval zebrafish. Quantitative trajectory analyses show that pursuit bouts systematically reduce prey angle by roughly half before strike, consistent with measurements. Virtual experiments and parameter sweeps vary ecological and energetic constraints, bout kinematics, and environmental factors such as food density, food speed, and vergence limits. These manipulations reveal how constraints and environments shape pursuit dynamics, strike success, and abort rates, yielding falsifiable predictions for neuroscience experiments. These sweeps identify a compact set of constraints-binocular sensing, the coupling of forward speed and turning in bout kinematics, and modest energetic costs on locomotion and vergence-that are sufficient for zebrafish-like hunting to emerge. Strikingly, these behaviors arise in minimal agents without detailed biomechanics, fluid dynamics, circuit realism, or imitation learning from real zebrafish data. Taken together, this work provides a normative account of zebrafish hunting as the optimal balance between energetic cost and sensory benefit, highlighting the trade-offs that structure vergence and trajectory dynamics. We establish a virtual lab that narrows the experimental search space and generates falsifiable predictions about behavior and neural coding.","PeriodicalId":524209,"journal":{"name":"Proceedings of the ... Conference on Cognitive Computational Neuroscience","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-supervised transformers predict dynamics of object-based attention in humans. 自监督变压器预测人类基于物体的注意力动态。

Proceedings of the ... Conference on Cognitive Computational Neuroscience Pub Date : 2023-08-01

Hossein Adeli, Seoyoung Ahn, Nikolaus Kriegeskorte, Gregory J Zelinsky

引用次数: 0

Using Generated Object Reconstructions to Study Object-based Attention. 使用生成对象重构研究基于对象的注意力。

Proceedings of the ... Conference on Cognitive Computational Neuroscience Pub Date : 2023-08-01

Seoyoung Ahn, Hossein Adeli, Gregory J Zelinsky

{"title":"Using Generated Object Reconstructions to Study Object-based Attention.","authors":"Seoyoung Ahn, Hossein Adeli, Gregory J Zelinsky","doi":"","DOIUrl":"","url":null,"abstract":"Object-based attention operates by selecting complete object representations as a fundamental unit, but existing theories tend to focus on bottom-up cues, such as Gestalt principles, and have given relatively little attention to the influence of top-down signals on this process, as well as how bottom-up and top-down factors may interact. Here we propose that Object Reconstruction-guided Attention (ORA) may provide a useful framework to study the interplay between bottom-up and top-down factors in object-based attention. The ORA model encodes object-based representations to reconstruct object location and appearance, and utilizes this reconstructed information to further bias the bottom-up signal in the feedforward pass. The objective of the model is to best explain the input by selecting and reconstructing target objects with the lowest reconstruction error, creating an object-selection bias. Our results demonstrate that this simple reconstruction-based selection principle can support various visual tasks, providing new insights into the brain mechanisms underlying robust object-based attention and visual perception. Future work will extend this work to more naturalistic images and examine the model's correspondence with human behavior.","PeriodicalId":524209,"journal":{"name":"Proceedings of the ... Conference on Cognitive Computational Neuroscience","volume":"2023 ","pages":"752-754"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0