arXiv: Neurons and Cognition最新文献

Phase-amplitude coupling in neuronal oscillator networks 神经振荡器网络的相幅耦合

arXiv: Neurons and Cognition Pub Date : 2020-12-08 DOI: 10.1103/PhysRevResearch.3.023218

Yuzhen Qin, Tommaso Menara, D. Bassett, F. Pasqualetti

引用次数: 6

Quality of internal representation shapes learning performance in feedback neural networks 内部表征的质量决定了反馈神经网络的学习性能

arXiv: Neurons and Cognition Pub Date : 2020-11-11 DOI: 10.1103/PHYSREVRESEARCH.3.013176

Lee Susman, F. Mastrogiuseppe, N. Brenner, O. Barak

{"title":"Quality of internal representation shapes learning performance in feedback neural networks","authors":"Lee Susman, F. Mastrogiuseppe, N. Brenner, O. Barak","doi":"10.1103/PHYSREVRESEARCH.3.013176","DOIUrl":"https://doi.org/10.1103/PHYSREVRESEARCH.3.013176","url":null,"abstract":"A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Despite the simplicity of this learning scheme, the factors contributing to or hindering the success of training in reservoir networks are in general not well understood. In this work, we study non-linear feedback networks trained to generate a sinusoidal signal, and analyze how learning performance is shaped by the interplay between internal network dynamics and target properties. By performing exact mathematical analysis of linearized networks, we predict that learning performance is maximized when the target is characterized by an optimal, intermediate frequency which monotonically decreases with the strength of the internal reservoir connectivity. At the optimal frequency, the reservoir representation of the target signal is high-dimensional, de-synchronized, and thus maximally robust to noise. We show that our predictions successfully capture the qualitative behaviour of performance in non-linear networks. Moreover, we find that the relationship between internal representations and performance can be further exploited in trained non-linear networks to explain behaviours which do not have a linear counterpart. Our results indicate that a major determinant of learning success is the quality of the internal representation of the target, which in turn is shaped by an interplay between parameters controlling the internal network and those defining the task.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128372168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Generalisation of neuronal excitability allows for the identification of an excitability change parameter that links to an experimentally measurable value 神经元兴奋性的普遍化允许识别与实验可测量值相关的兴奋性变化参数

arXiv: Neurons and Cognition Pub Date : 2020-10-31 DOI: 10.5281/zenodo.4159691

J. Broek, Guillaume Drion

{"title":"Generalisation of neuronal excitability allows for the identification of an excitability change parameter that links to an experimentally measurable value","authors":"J. Broek, Guillaume Drion","doi":"10.5281/zenodo.4159691","DOIUrl":"https://doi.org/10.5281/zenodo.4159691","url":null,"abstract":"Neuronal excitability is the phenomena that describes action potential generation due to a stimulus input. Commonly, neuronal excitability is divided into two classes: Type I and Type II, both having different properties that affect information processing, such as thresholding and gain scaling. These properties can be mathematically studied using generalised phenomenological models, such as the Fitzhugh-Nagumo model and the mirrored FHN. The FHN model shows that each excitability type corresponds to one specific type of bifurcation in the phase plane: Type I underlies a saddle-node on invariant cycle bifurcation, and Type II a Hopf bifurcation. The difficulty of modelling Type I excitability is that it is not only represented by its underlying bifurcation, but also should be able to generate frequency while maintaining a small depolarising current. Using the mFHN model, we show that this situation is possible without modifying the phase portrait, due to the incorporation of a slow regenerative variable. We show that in the singular limit of the mFHN model, the time-scale separation can be chosen such that there is a configuration of a classical phase portrait that allows for SNIC bifurcation, zero-frequency onset and a depolarising current, such as observed in Type I excitability. Using the definition of slow conductance, g_s, we show that these mathematical findings for excitability change are translatable to reduced conductance based models and also relates to an experimentally measurable quantity. This not only allows for a measure of excitability change, but also relates the mathematical parameters that indicate a physiological Type I excitability to parameters that can be tuned during experiments.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127711076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short term memory by transient oscillatory dynamics in recurrent neural networks 递归神经网络的瞬态振荡动态短时记忆

arXiv: Neurons and Cognition Pub Date : 2020-10-29 DOI: 10.1103/PhysRevResearch.3.033193

K. Ichikawa, K. Kaneko

引用次数: 5

Predicting brain evoked response to external stimuli from temporal correlations of spontaneous activity 从自发活动的时间相关性预测大脑对外部刺激的诱发反应

arXiv: Neurons and Cognition Pub Date : 2020-09-02 DOI: 10.1103/PhysRevResearch.2.033355

Alessandro Sarracino, O. Arviv, O. Shriki, L. Arcangelis

引用次数: 13

Memory systems of the brain 大脑的记忆系统

arXiv: Neurons and Cognition Pub Date : 2020-09-01 DOI: 10.31219/OSF.IO/W6KN9

Alvaro Pastor

引用次数: 0

Feedback Gains modulate with Motor Memory Uncertainty 反馈增益与运动记忆不确定性调制

arXiv: Neurons and Cognition Pub Date : 2020-08-17 DOI: 10.51628/001C.22336

Sae Franklin, D. W. Franklin

{"title":"Feedback Gains modulate with Motor Memory Uncertainty","authors":"Sae Franklin, D. W. Franklin","doi":"10.51628/001C.22336","DOIUrl":"https://doi.org/10.51628/001C.22336","url":null,"abstract":"A sudden change in dynamics produces large errors leading to increases in muscle co-contraction and feedback gains during early adaptation. We previously proposed that internal model uncertainty drives these changes, whereby the sensorimotor system reacts to the change in dynamics by upregulating stiffness and feedback gains to reduce the effect of model errors. However, these feedback gain increases have also been suggested to represent part of the adaptation mechanism. Here, we investigate this by examining changes in visuomotor feedback gains during gradual or abrupt force field adaptation. Participants grasped a robotic manipulandum and reached while a curl force field was introduced gradually or abruptly. Abrupt introduction of dynamics elicited large initial increases in kinematic error, muscle co-contraction and visuomotor feedback gains, while gradual introduction showed little initial change in these measures despite evidence of adaptation. After adaptation had plateaued, there was a change in the co-contraction and visuomotor feedback gains relative to null field movements, but no differences (apart from the final muscle activation pattern) between the abrupt and gradual introduction of dynamics. This suggests that the initial increase in feedback gains is not part of the adaptation process, but instead an automatic reactive response to internal model uncertainty. In contrast, the final level of feedback gains is a predictive tuning of the feedback gains to the external dynamics as part of the internal model adaptation. Together, the reactive and predictive feedback gains explain the wide variety of previous experimental results of feedback changes during adaptation.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115752676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Simple RGC: ImageJ Plugins for Counting Retinal Ganglion Cells and Determining the Transduction Efficiency of Viral Vectors in Retinal Wholemounts 简单的RGC: ImageJ插件用于计数视网膜神经节细胞和确定病毒载体在视网膜整体的转导效率

arXiv: Neurons and Cognition Pub Date : 2020-08-14 DOI: 10.5334/jors.342

Tiger Cross, Rasika Navarange, Joon-ho Son, William Burr, Arjun Singh, Kelvin Zhang, M. Rusu, Konstantinos Gkoutzis, A. Osborne, Bart Nieuwenhuis Department of Computing, I. -. London, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, U. Cambridge, L. Systems, Netherlands Institute for Neuroscience, R. Arts, Sciences

{"title":"Simple RGC: ImageJ Plugins for Counting Retinal Ganglion Cells and Determining the Transduction Efficiency of Viral Vectors in Retinal Wholemounts","authors":"Tiger Cross, Rasika Navarange, Joon-ho Son, William Burr, Arjun Singh, Kelvin Zhang, M. Rusu, Konstantinos Gkoutzis, A. Osborne, Bart Nieuwenhuis Department of Computing, I. -. London, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, U. Cambridge, L. Systems, Netherlands Institute for Neuroscience, R. Arts, Sciences","doi":"10.5334/jors.342","DOIUrl":"https://doi.org/10.5334/jors.342","url":null,"abstract":"Simple RGC consists of a collection of ImageJ plugins to assist researchers investigating retinal ganglion cell (RGC) injury models in addition to helping assess the effectiveness of treatments. The first plugin named RGC Counter accurately calculates the total number of RGCs from retinal wholemount images. The second plugin named RGC Transduction measures the co-localisation between two channels making it possible to determine the transduction efficiencies of viral vectors and transgene expression levels. The third plugin named RGC Batch is a batch image processor to deliver fast analysis of large groups of microscope images. These ImageJ plugins make analysis of RGCs in retinal wholemounts easy, quick, consistent, and less prone to unconscious bias by the investigator. The plugins are freely available from the ImageJ update site this https URL.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129912021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Poincaré Return Maps in Neural Dynamics: Three Examples 神经动力学中的poincarcars返回图:三个例子

arXiv: Neurons and Cognition Pub Date : 2020-07-08 DOI: 10.1007/978-3-030-60107-2_3

M. Kolomiets, A. Shilnikov

引用次数: 0

Synchronization malleability in neural networks under a distance-dependent coupling 距离依赖耦合下神经网络的同步延展性

arXiv: Neurons and Cognition Pub Date : 2020-06-05 DOI: 10.1103/physrevresearch.2.043309

R. Budzinski, K. L. Rossi, B. Boaretto, T. L. Prado, S. R. Lopes

引用次数: 4