Biological Cybernetics最新文献_第4页

Divisive normalization processors in the early visual system of the Drosophila brain. 果蝇大脑早期视觉系统中的分裂正常化处理器

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-12-01 Epub Date: 2023-09-13 DOI: 10.1007/s00422-023-00972-x

Aurel A Lazar, Yiyin Zhou

{"title":"Divisive normalization processors in the early visual system of the Drosophila brain.","authors":"Aurel A Lazar, Yiyin Zhou","doi":"10.1007/s00422-023-00972-x","DOIUrl":"10.1007/s00422-023-00972-x","url":null,"abstract":"Divisive normalization is a model of canonical computation of brain circuits. We demonstrate that two cascaded divisive normalization processors (DNPs), carrying out intensity/contrast gain control and elementary motion detection, respectively, can model the robust motion detection realized by the early visual system of the fruit fly. We first introduce a model of elementary motion detection and rewrite its underlying phase-based motion detection algorithm as a feedforward divisive normalization processor. We then cascade the DNP modeling the photoreceptor/amacrine cell layer with the motion detection DNP. We extensively evaluate the DNP for motion detection in dynamic environments where light intensity varies by orders of magnitude. The results are compared to other bio-inspired motion detectors as well as state-of-the-art optic flow algorithms under natural conditions. Our results demonstrate the potential of DNPs as canonical building blocks modeling the analog processing of early visual systems. The model highlights analog processing for accurately detecting visual motion, in both vertebrates and invertebrates. The results presented here shed new light on employing DNP-based algorithms in computer vision.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"411-431"},"PeriodicalIF":1.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10752861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10224341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning heterogeneous delays in a layer of spiking neurons for fast motion detection. 快速运动检测中尖峰神经元层的异构延迟学习。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-09-11 DOI: 10.1007/s00422-023-00975-8

Antoine Grimaldi, Laurent U Perrinet

{"title":"Learning heterogeneous delays in a layer of spiking neurons for fast motion detection.","authors":"Antoine Grimaldi, Laurent U Perrinet","doi":"10.1007/s00422-023-00975-8","DOIUrl":"10.1007/s00422-023-00975-8","url":null,"abstract":"The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiology, especially for the performance of neuromorphic hardware, such as event-based cameras. Nonetheless, many artificial neural models disregard this critical temporal dimension of neural activity. In this study, we present a model designed to efficiently detect temporal spiking motifs using a layer of spiking neurons equipped with heterogeneous synaptic delays. Our model capitalizes on the diverse synaptic delays present on the dendritic tree, enabling specific arrangements of temporally precise synaptic inputs to synchronize upon reaching the basal dendritic tree. We formalize this process as a time-invariant logistic regression, which can be trained using labeled data. To demonstrate its practical efficacy, we apply the model to naturalistic videos transformed into event streams, simulating the output of the biological retina or event-based cameras. To evaluate the robustness of the model in detecting visual motion, we conduct experiments by selectively pruning weights and demonstrate that the model remains efficient even under significantly reduced workloads. In conclusion, by providing a comprehensive, event-driven computational building block, the incorporation of heterogeneous delays has the potential to greatly improve the performance of future spiking neural network algorithms, particularly in the context of neuromorphic chips.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"373-387"},"PeriodicalIF":1.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10571295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What can computer vision learn from visual neuroscience? Introduction to the special issue. 计算机视觉可以从视觉神经科学中学到什么？特刊简介。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 DOI: 10.1007/s00422-023-00977-6

Kexin Chen, Hirak J Kashyap, Jeffrey L Krichmar, Xiumin Li

引用次数: 2

A Fundamental Inequality Governing the Rate Coding Response of Sensory Neurons. 控制感觉神经元速率编码反应的一个基本不等式。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-08-19 DOI: 10.1007/s00422-023-00971-y

Willy Wong

引用次数: 0

Canonical circuit computations for computer vision. 计算机视觉规范电路计算。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-06-12 DOI: 10.1007/s00422-023-00966-9

Daniel Schmid, Christian Jarvers, Heiko Neumann

{"title":"Canonical circuit computations for computer vision.","authors":"Daniel Schmid, Christian Jarvers, Heiko Neumann","doi":"10.1007/s00422-023-00966-9","DOIUrl":"10.1007/s00422-023-00966-9","url":null,"abstract":"Advanced computer vision mechanisms have been inspired by neuroscientific findings. However, with the focus on improving benchmark achievements, technical solutions have been shaped by application and engineering constraints. This includes the training of neural networks which led to the development of feature detectors optimally suited to the application domain. However, the limitations of such approaches motivate the need to identify computational principles, or motifs, in biological vision that can enable further foundational advances in machine vision. We propose to utilize structural and functional principles of neural systems that have been largely overlooked. They potentially provide new inspirations for computer vision mechanisms and models. Recurrent feedforward, lateral, and feedback interactions characterize general principles underlying processing in mammals. We derive a formal specification of core computational motifs that utilize these principles. These are combined to define model mechanisms for visual shape and motion processing. We demonstrate how such a framework can be adopted to run on neuromorphic brain-inspired hardware platforms and can be extended to automatically adapt to environment statistics. We argue that the identified principles and their formalization inspires sophisticated computational mechanisms with improved explanatory scope. These and other elaborated, biologically inspired models can be employed to design computer vision solutions for different tasks and they can be used to advance neural network architectures of learning.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"299-329"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9613848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bio-inspired, task-free continual learning through activity regularization. 通过活动规格化进行生物启发的、无任务的持续学习。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-08-17 DOI: 10.1007/s00422-023-00973-w

Francesco Lässig, Pau Vilimelis Aceituno, Martino Sorbaro, Benjamin F Grewe

{"title":"Bio-inspired, task-free continual learning through activity regularization.","authors":"Francesco Lässig, Pau Vilimelis Aceituno, Martino Sorbaro, Benjamin F Grewe","doi":"10.1007/s00422-023-00973-w","DOIUrl":"10.1007/s00422-023-00973-w","url":null,"abstract":"The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"345-361"},"PeriodicalIF":1.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10014353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Stakes of neuromorphic foveation: a promising future for embedded event cameras. 神经形态中央凹的利害关系：嵌入式事件摄像机的前景广阔。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-09-21 DOI: 10.1007/s00422-023-00974-9

Amélie Gruel, Dalia Hareb, Antoine Grimaldi, Jean Martinet, Laurent Perrinet, Bernabé Linares-Barranco, Teresa Serrano-Gotarredona

引用次数: 3

Periodic solutions in next generation neural field models. 新一代神经场模型的周期解。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-08-03 DOI: 10.1007/s00422-023-00969-6

Carlo R Laing, Oleh E Omel'chenko

引用次数: 0

The Bcm rule allows a spinal cord model to learn rhythmic movements. Bcm规则允许脊髓模型学习有节奏的运动。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-08-18 DOI: 10.1007/s00422-023-00970-z

Matthias Kohler, Florian Röhrbein, Alois Knoll, Alin Albu-Schäffer, Henrik Jörntell

{"title":"The Bcm rule allows a spinal cord model to learn rhythmic movements.","authors":"Matthias Kohler, Florian Röhrbein, Alois Knoll, Alin Albu-Schäffer, Henrik Jörntell","doi":"10.1007/s00422-023-00970-z","DOIUrl":"10.1007/s00422-023-00970-z","url":null,"abstract":"Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock-Cooper-Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"275-284"},"PeriodicalIF":1.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10600281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10078177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extreme image transformations affect humans and machines differently. 极端的图像变换对人类和机器的影响是不同的。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-10-01 Epub Date: 2023-06-13 DOI: 10.1007/s00422-023-00968-7

Girik Malik, Dakarai Crowder, Ennio Mingolla

引用次数: 2