Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
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AUTOMATION AND ROBOTICS (116 journals)                     

Showing 1 - 113 of 113 Journals sorted alphabetically
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 10)
ACM Transactions on Human-Robot Interaction     Open Access   (Followers: 3)
Advanced Robotics     Hybrid Journal   (Followers: 28)
Advances in Computed Tomography     Open Access   (Followers: 2)
Advances in Image and Video Processing     Open Access   (Followers: 25)
Advances in Robotics & Automation     Open Access   (Followers: 11)
American Journal of Robotic Surgery     Full-text available via subscription   (Followers: 7)
Annual Review of Control, Robotics, and Autonomous Systems     Full-text available via subscription   (Followers: 12)
Artificial Life and Robotics     Hybrid Journal   (Followers: 17)
Augmented Human Research     Hybrid Journal  
Automated Software Engineering     Hybrid Journal   (Followers: 9)
Automatic Control and Information Sciences     Open Access   (Followers: 4)
Automation and Remote Control     Hybrid Journal   (Followers: 5)
Autonomous Agents and Multi-Agent Systems     Hybrid Journal   (Followers: 9)
Autonomous Robots     Hybrid Journal   (Followers: 11)
Biocybernetics and Biological Engineering     Full-text available via subscription   (Followers: 4)
Biological Cybernetics     Hybrid Journal   (Followers: 10)
Biomimetic Intelligence and Robotics     Open Access  
Cognitive Robotics     Open Access   (Followers: 6)
Computational Intelligence and Neuroscience     Open Access   (Followers: 18)
Computer-Aided Design     Hybrid Journal   (Followers: 9)
Construction Robotics     Hybrid Journal   (Followers: 4)
Current Robotics Reports     Hybrid Journal   (Followers: 4)
Cybernetics & Human Knowing     Full-text available via subscription   (Followers: 3)
Cybernetics and Systems Analysis     Hybrid Journal  
Cybernetics and Systems: An International Journal     Hybrid Journal   (Followers: 1)
Design Automation for Embedded Systems     Hybrid Journal   (Followers: 7)
Digital Zone : Jurnal Teknologi Informasi Dan Komunikasi     Open Access  
Drone Systems and Applications     Open Access   (Followers: 1)
Electrical Engineering and Automation     Open Access   (Followers: 9)
Facta Universitatis, Series : Automatic Control and Robotics     Open Access   (Followers: 1)
Foundations and Trends® in Robotics     Full-text available via subscription   (Followers: 5)
Frontiers in Neurorobotics     Open Access   (Followers: 6)
Frontiers in Robotics and AI     Open Access   (Followers: 8)
GIScience & Remote Sensing     Open Access   (Followers: 59)
IAES International Journal of Robotics and Automation     Open Access   (Followers: 5)
IEEE Robotics & Automation Magazine     Full-text available via subscription   (Followers: 70)
IEEE Robotics and Automation Letters     Hybrid Journal   (Followers: 9)
IEEE Transactions on Affective Computing     Hybrid Journal   (Followers: 23)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 17)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 71)
IEEE Transactions on Cybernetics     Hybrid Journal   (Followers: 16)
IEEE Transactions on Intelligent Vehicles     Hybrid Journal   (Followers: 2)
IEEE Transactions on Medical Robotics and Bionics     Hybrid Journal   (Followers: 5)
IEEE Transactions on Neural Networks and Learning Systems     Hybrid Journal   (Followers: 54)
IEEE Transactions on Robotics     Hybrid Journal   (Followers: 71)
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews     Hybrid Journal   (Followers: 16)
IET Cyber-systems and Robotics     Open Access   (Followers: 2)
IET Systems Biology     Open Access   (Followers: 1)
Industrial Robot An International Journal     Hybrid Journal   (Followers: 2)
Intelligent Control and Automation     Open Access   (Followers: 6)
Intelligent Service Robotics     Hybrid Journal   (Followers: 2)
International Journal of Adaptive, Resilient and Autonomic Systems     Full-text available via subscription   (Followers: 3)
International Journal of Advanced Pervasive and Ubiquitous Computing     Full-text available via subscription   (Followers: 4)
International Journal of Advanced Robotic Systems     Full-text available via subscription   (Followers: 1)
International Journal of Agent Technologies and Systems     Full-text available via subscription   (Followers: 4)
International Journal of Ambient Computing and Intelligence     Full-text available via subscription   (Followers: 3)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
International Journal of Applied Evolutionary Computation     Full-text available via subscription   (Followers: 3)
International Journal of Artificial Life Research     Full-text available via subscription  
International Journal of Automation and Control     Hybrid Journal   (Followers: 11)
International Journal of Automation and Control Engineering     Open Access   (Followers: 5)
International Journal of Automation and Logistics     Hybrid Journal   (Followers: 3)
International Journal of Automation and Smart Technology     Open Access   (Followers: 3)
International Journal of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 15)
International Journal of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 2)
International Journal of Cyber Behavior, Psychology and Learning     Full-text available via subscription   (Followers: 7)
International Journal of Humanoid Robotics     Hybrid Journal   (Followers: 6)
International Journal of Imaging & Robotics     Full-text available via subscription   (Followers: 3)
International Journal of Intelligent Information Technologies     Full-text available via subscription   (Followers: 2)
International Journal of Intelligent Machines and Robotics     Hybrid Journal   (Followers: 3)
International Journal of Intelligent Mechatronics and Robotics     Full-text available via subscription   (Followers: 5)
International Journal of Intelligent Robotics and Applications     Hybrid Journal  
International Journal of Intelligent Systems Design and Computing     Hybrid Journal   (Followers: 1)
International Journal of Intelligent Unmanned Systems     Hybrid Journal   (Followers: 3)
International Journal of Machine Consciousness     Hybrid Journal   (Followers: 6)
International Journal of Machine Learning and Cybernetics     Hybrid Journal   (Followers: 38)
International Journal of Machine Learning and Networked Collaborative Engineering     Open Access   (Followers: 16)
International Journal of Mechanisms and Robotic Systems     Hybrid Journal   (Followers: 2)
International Journal of Mechatronics and Automation     Hybrid Journal   (Followers: 5)
International Journal of Robotics and Automation     Full-text available via subscription   (Followers: 8)
International Journal of Robotics and Control     Open Access   (Followers: 3)
International Journal of Robotics Applications and Technologies     Full-text available via subscription   (Followers: 1)
International Journal of Robotics Research     Hybrid Journal   (Followers: 15)
International Journal of Space-Based and Situated Computing     Hybrid Journal   (Followers: 2)
International Journal of Synthetic Emotions     Full-text available via subscription  
International Journal of Tomography & Simulation     Full-text available via subscription   (Followers: 1)
Journal of Automation and Control     Open Access   (Followers: 9)
Journal of Biomechanical Engineering     Full-text available via subscription   (Followers: 12)
Journal of Computer Assisted Tomography     Hybrid Journal   (Followers: 2)
Journal of Control & Instrumentation     Full-text available via subscription   (Followers: 19)
Journal of Control, Automation and Electrical Systems     Hybrid Journal   (Followers: 13)
Journal of Intelligent and Robotic Systems     Hybrid Journal   (Followers: 6)
Journal of Intelligent Learning Systems and Applications     Open Access   (Followers: 4)
Journal of Physical Agents     Open Access   (Followers: 1)
Journal of Robotic Surgery     Hybrid Journal   (Followers: 3)
Journal of Robotics     Open Access   (Followers: 6)
Jurnal Otomasi Kontrol dan Instrumentasi     Open Access  
Machine Translation     Hybrid Journal   (Followers: 13)
Proceedings of the ACM on Human-Computer Interaction     Hybrid Journal   (Followers: 3)
Results in Control and Optimization     Open Access   (Followers: 3)
Revista Iberoamericana de Automática e Informática Industrial RIAI     Open Access  
ROBOMECH Journal     Open Access   (Followers: 1)
Robotic Surgery : Research and Reviews     Open Access   (Followers: 1)
Robotica     Hybrid Journal   (Followers: 5)
Robotics and Autonomous Systems     Hybrid Journal   (Followers: 19)
Robotics and Biomimetics     Open Access   (Followers: 1)
Robotics and Computer-Integrated Manufacturing     Hybrid Journal   (Followers: 7)
Science Robotics     Full-text available via subscription   (Followers: 11)
Soft Robotics     Hybrid Journal   (Followers: 5)
Universal Journal of Control and Automation     Open Access   (Followers: 2)
Unmanned Systems     Hybrid Journal   (Followers: 4)
Wearable Technologies     Open Access   (Followers: 3)

           

Similar Journals
Journal Cover
Biological Cybernetics
Journal Prestige (SJR): 0.667
Citation Impact (citeScore): 2
Number of Followers: 10  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1432-0770 - ISSN (Online) 0340-1200
Published by Springer-Verlag Homepage  [2468 journals]
  • Divisive normalization processors in the early visual system of the
           Drosophila brain

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      Abstract: 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.
      PubDate: 2023-09-13
       
  • Learning heterogeneous delays in a layer of spiking neurons for fast
           motion detection

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      Abstract: 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.
      PubDate: 2023-09-11
       
  • A Fundamental Inequality Governing the Rate Coding Response of Sensory
           Neurons

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      Abstract: Abstract A fundamental inequality governing the spike activity of peripheral neurons is derived and tested against auditory data. This inequality states that the steady-state firing rate must lie between the arithmetic and geometric means of the spontaneous and peak activities during adaptation. Implications towards the development of auditory mechanistic models are explored.
      PubDate: 2023-08-19
       
  • The Bcm rule allows a spinal cord model to learn rhythmic movements

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      Abstract: 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.
      PubDate: 2023-08-18
       
  • Bio-inspired, task-free continual learning through activity regularization

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      Abstract: 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.
      PubDate: 2023-08-17
       
  • Periodic solutions in next generation neural field models

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      Abstract: Abstract We consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is demonstrated through its application to several other systems involving delays, two-population architecture and networks of Winfree oscillators.
      PubDate: 2023-08-03
       
  • Toward metacognition: subject-aware contrastive deep fusion representation
           learning for EEG analysis

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      Abstract: Abstract We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects’ confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time–frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity within a multi-subject electroencephalogram dataset to boost representation learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying confidence levels by achieving a classification accuracy of 95.7% while also identifying influential brain regions.
      PubDate: 2023-07-04
      DOI: 10.1007/s00422-023-00967-8
       
  • Extreme image transformations affect humans and machines differently

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      Abstract: Some recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that humans do not. As a result, out-of-distribution or adversarial input is often challenging for ANNs. Humans instead learn abstract patterns and are mostly unaffected by many extreme image distortions. We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and ANNs on an object recognition task. We show that machines perform better than humans for certain transforms and struggle to perform at par with humans on others that are easy for humans. We quantify the differences in accuracy for humans and machines and find a ranking of difficulty for our transforms for human data. We also suggest how certain characteristics of human visual processing can be adapted to improve the performance of ANNs for our difficult-for-machines transforms.
      PubDate: 2023-06-13
      DOI: 10.1007/s00422-023-00968-7
       
  • Canonical circuit computations for computer vision

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      Abstract: 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.
      PubDate: 2023-06-12
      DOI: 10.1007/s00422-023-00966-9
       
  • How aggressive interactions with biomimetic agents optimize reproductive
           performances in mass-reared males of the Mediterranean fruit fly

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      Abstract: Abstract Mass-rearing procedures of insect species, often used in biological control and Sterile Insect Technique, can reduce the insects competitiveness in foraging, dispersal, and mating. The evocation of certain behaviours responsible to induce specific neuroendocrine products may restore or improve the competitiveness of mass-reared individuals. Herein, we used a mass-reared strain of Ceratitis capitata as model organism. C. capitata is a polyphagous pest exhibiting territorial displays that are closely related to its reproductive performance. We tested if the behaviour of C. capitata males could be altered by hybrid aggressive interactions with a conspecific-mimicking robotic fly, leading to more competitive individuals in subsequent mating events. Aggressive interactions with the robotic fly had a notable effect on subsequent courtship and mating sequences of males that performed longer courtship displays compared to naïve individuals. Furthermore, previous interactions with the robotic fly produced a higher mating success of males. Reproductive performances of C. capitata males may be improved by specific octopaminergic neurones activated during previous aggressive interactions with the robotic fly. This study adds fundamental knowledge on the potential role of specific neuro-behavioural processes in the ecology of tephritid species and paves the way to innovative biotechnological control methods based on robotics and bionics.
      PubDate: 2023-05-31
      DOI: 10.1007/s00422-023-00965-w
       
  • Mixed $$\mu $$ -synthesis tracking control and disturbance rejection in a
           robotic digit of an impaired human hand for anthropomorphic coordination

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      Abstract: Abstract In a partially impaired anthropomorphic hand, maintaining the movement coordination of the robotic digits with the central nervous system (CNS) and natural digits is crucial for robust performance. A challenge in the control perspective of movement coordination of a human hand is finding methods robust to the disturbances in a well-posed control problem of a biomechanical model. We use visco-elastic dynamics in the human palm frame of reference to explore the biomechanics of movement coordination to solve this control problem. Our biomechanical model incorporates the time delay due to actuation force, parametric uncertainty, exogenous disturbances, and sensory noise to constitute a 21-degree of freedom model. A mixed \(\mu \) -synthesis controller, considering the real parametric uncertainty, represents the CNS in the control paradigm. We consider the robotic finger’s flexion movement when perturbed from the initial equilibrium. The controller provides feedback force at the joints to regulate the robotic finger movement. The index finger follows a reference trajectory of the joint angular position profile and stabilizes at a flexion angle of 1 rad/s at a time of 1 s. The main control objective is to keep the angular displacement of the finger joint constant when a disturbance force acts. We simulate the modeling scheme in MATLAB/ Simulink. The results demonstrate that our controller scheme is robust against the worst-case disturbance and achieves the desired performance value. Developing a biologically inspired neurophysiological controller with robust performance has many applications, including assistive rehabilitation devices, hand movement disorder diagnosis, and robotic manipulators.
      PubDate: 2023-05-24
      DOI: 10.1007/s00422-023-00964-x
       
  • Self-organizing maps on “what-where” codes towards fully
           unsupervised classification

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      Abstract: Abstract Interest in unsupervised learning architectures has been rising. Besides being biologically unnatural, it is costly to depend on large labeled data sets to get a well-performing classification system. Therefore, both the deep learning community and the more biologically-inspired models community have focused on proposing unsupervised techniques that can produce adequate hidden representations which can then be fed to a simpler supervised classifier. Despite great success with this approach, an ultimate dependence on a supervised model remains, which forces the number of classes to be known beforehand, and makes the system depend on labels to extract concepts. To overcome this limitation, recent work has been proposed that shows how a self-organizing map (SOM) can be used as a completely unsupervised classifier. However, to achieve success it required deep learning techniques to generate high quality embeddings. The purpose of this work is to show that we can use our previously proposed What-Where encoder in tandem with the SOM to get an end-to-end unsupervised system that is Hebbian. Such system, requires no labels to train nor does it require knowledge of which classes exist beforehand. It can be trained online and adapt to new classes that may emerge. As in the original work, we use the MNIST data set to run an experimental analysis and verify that the system achieves similar accuracies to the best ones reported thus far. Furthermore, we extend the analysis to the more difficult Fashion-MNIST problem and conclude that the system still performs.
      PubDate: 2023-05-15
      DOI: 10.1007/s00422-023-00963-y
       
  • Low-dimensional models of single neurons: a review

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      Abstract: Abstract The classical Hodgkin–Huxley (HH) point-neuron model of action potential generation is four-dimensional. It consists of four ordinary differential equations describing the dynamics of the membrane potential and three gating variables associated to a transient sodium and a delayed-rectifier potassium ionic currents. Conductance-based models of HH type are higher-dimensional extensions of the classical HH model. They include a number of supplementary state variables associated with other ionic current types, and are able to describe additional phenomena such as subthreshold oscillations, mixed-mode oscillations (subthreshold oscillations interspersed with spikes), clustering and bursting. In this manuscript we discuss biophysically plausible and phenomenological reduced models that preserve the biophysical and/or dynamic description of models of HH type and the ability to produce complex phenomena, but the number of effective dimensions (state variables) is lower. We describe several representative models. We also describe systematic and heuristic methods of deriving reduced models from models of HH type.
      PubDate: 2023-04-15
      DOI: 10.1007/s00422-023-00960-1
       
  • Approaching object acceleration differentially affects the predictions of
           neuronal collision avoidance models

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      Abstract: Abstract The processing of visual information for collision avoidance has been investigated at the biophysical level in several model systems. In grasshoppers, the (so-called) \(\eta \) model captures reasonably well the visual processing performed by an identified neuron called the lobular giant movement detector as it tracks approaching objects. Similar phenomenological models have been used to describe either the firing rate or the membrane potential of neurons responsible for visually guided collision avoidance in other animals. Specifically, in goldfish, the \(\kappa \) model has been proposed to describe the Mauthner cell, an identified neuron involved in startle escape responses. In the vinegar fly, a third model was developed for the giant fiber neuron, which triggers last resort escapes immediately before an impending collision. One key property of these models is their prediction that peak neuronal responses occur at a fixed delay after the simulated approaching object reaches a threshold angular size on the retina. This prediction is valid for simulated objects approaching at a constant speed. We tested whether it remains valid when approaching objects accelerate. After characterizing and comparing the models’ responses to accelerating and constant speed stimuli, we find that the prediction holds true for the \(\kappa \) and the giant fiber model, but not for the \(\eta \) model. These results suggest that acceleration in the approach trajectory of an object may help distinguish and further constrain the neuronal computations required for collision avoidance in grasshoppers, fish and vinegar flies.
      PubDate: 2023-04-08
      DOI: 10.1007/s00422-023-00961-0
       
  • Structure and dynamics that specialize neurons for high-frequency
           coincidence detection in the barn owl nucleus laminaris

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      Abstract: Abstract A principal cue for sound source localization is the difference in arrival times of sounds at an animal’s two ears (interaural time difference, ITD). Neurons that process ITDs are specialized to compare the timing of inputs with submillisecond precision. In the barn owl, ITD processing begins in the nucleus laminaris (NL) region of the auditory brain stem. Remarkably, NL neurons are sensitive to ITDs in high-frequency sounds (kilohertz-range). This contrasts with ITD-based sound localization in analogous regions in mammals where ITD sensitivity is typically restricted to lower-frequency sounds. Guided by previous experiments and modeling studies of tone-evoked responses of NL neurons, we propose NL neurons achieve high-frequency ITD sensitivity if they respond selectively to the small-amplitude, high-frequency oscillations in their inputs, and remain relatively non-responsive to mean input level. We use a biophysically based model to study the effects of soma–axon coupling on dynamics and function in NL neurons. First, we show that electrical separation of the soma from the axon region in the neuron enhances high-frequency ITD sensitivity. This soma–axon coupling configuration promotes linear subthreshold dynamics and rapid spike initiation, making the model more responsive to input oscillations, rather than mean input level. Second, we provide new evidence for the essential role of phasic dynamics for high-frequency neural coincidence detection. Transforming our model to the phasic firing mode further tunes the model to respond selectively to the oscillating inputs that carry ITD information. Similar structural and dynamical mechanisms specialize mammalian auditory brain stem neurons for ITD sensitivity, and thus, our work identifies common principles of ITD processing and neural coincidence detection across species and for sounds at widely different frequencies.
      PubDate: 2023-04-01
      DOI: 10.1007/s00422-023-00962-z
       
  • Efficient multi-scale representation of visual objects using a
           biologically plausible spike-latency code and winner-take-all inhibition

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      Abstract: Abstract Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.
      PubDate: 2023-04-01
      DOI: 10.1007/s00422-023-00956-x
       
  • Validating models of sensory conflict and perception for motion sickness
           prediction

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      Abstract: Abstract The human motion perception system has long been linked to motion sickness through state estimation conflict terms. However, to date, the extent to which available perception models are able to predict motion sickness, or which of the employed perceptual mechanisms are of most relevance to sickness prediction, has not been studied. In this study, the subjective vertical model, the multi-sensory observer model and the probabilistic particle filter model were all validated for their ability to predict motion perception and sickness, across a large set of motion paradigms of varying complexity from literature. It was found that even though the models provided a good match for the perception paradigms studied, they could not be made to capture the full range of motion sickness observations. The resolution of the gravito-inertial ambiguity has been identified to require further attention, as key model parameters selected to match perception data did not optimally match motion sickness data. Two additional mechanisms that may enable better future predictive models of sickness have, however, been identified. Firstly, active estimation of the magnitude of gravity appears to be instrumental for predicting motion sickness induced by vertical accelerations. Secondly, the model analysis showed that the influence of the semicircular canals on the somatogravic effect may explain the differences in the dynamics observed for motion sickness induced by vertical and horizontal plane accelerations.
      PubDate: 2023-03-27
      DOI: 10.1007/s00422-023-00959-8
       
  • Editorial board of biological cybernetics: advances in computational
           neuroscience

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      PubDate: 2023-03-22
      DOI: 10.1007/s00422-023-00958-9
       
  • A time-causal and time-recursive scale-covariant scale-space
           representation of temporal signals and past time

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      Abstract: Abstract This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original signal) and (ii) the temporal smoothing process is both time-causal and time-recursive, in the sense that it does not require access to future information and can be performed with no other temporal memory buffer of the past than the resulting smoothed temporal scale-space representations themselves. For specific subsets of parameter settings for the classes of linear and shift-invariant temporal smoothing operators that obey this property, it is shown how temporal scale covariance can be additionally obtained, guaranteeing that if the temporal input signal is rescaled by a uniform temporal scaling factor, then also the resulting temporal scale-space representations of the rescaled temporal signal will constitute mere rescalings of the temporal scale-space representations of the original input signal, complemented by a shift along the temporal scale dimension. The resulting time-causal limit kernel that obeys this property constitutes a canonical temporal kernel for processing temporal signals in real-time scenarios when the regular Gaussian kernel cannot be used, because of its non-causal access to information from the future, and we cannot additionally require the temporal smoothing process to comprise a complementary memory of the past beyond the information contained in the temporal smoothing process itself, which in this way also serves as a multi-scale temporal memory of the past. We describe how the time-causal limit kernel relates to previously used temporal models, such as Koenderink’s scale-time kernels and the ex-Gaussian kernel. We do also give an overview of how the time-causal limit kernel can be used for modelling the temporal processing in models for spatio-temporal and spectro-temporal receptive fields, and how it more generally has a high potential for modelling neural temporal response functions in a purely time-causal and time-recursive way, that can also handle phenomena at multiple temporal scales in a theoretically well-founded manner. We detail how this theory can be efficiently implemented for discrete data, in terms of a set of recursive filters coupled in cascade. Hence, the theory is generally applicable for both: (i) modelling continuous temporal phenomena over multiple temporal scales and (ii) digital processing of measured temporal signals in real time. We conclude by stating implications of the theory for modelling temporal phenomena in biological, perceptual, neural and memory processes by mathematical models, as well as implications regarding the philosophy of time and perceptual agents. Specifically, we propose that for A-type theories of time, as well as for perceptual agents, the notion of a non-infinitesimal inner temporal scale of the temporal receptive fields has to be included in representations of the present, where the inherent nonzero temporal delay of such time-causal receptive fields implies a need for incorporating predictions from the actual time-delayed present in the layers of a perceptual hierarchy, to make it possible for a representation of the perceptual present to constitute a representation of the environment with timing properties closer to the actual present.
      PubDate: 2023-01-23
      DOI: 10.1007/s00422-022-00953-6
       
  • Comparison between an exact and a heuristic neural mass model with
           second-order synapses

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      Abstract: Abstract Neural mass models (NMMs) are designed to reproduce the collective dynamics of neuronal populations. A common framework for NMMs assumes heuristically that the output firing rate of a neural population can be described by a static nonlinear transfer function (NMM1). However, a recent exact mean-field theory for quadratic integrate-and-fire (QIF) neurons challenges this view by showing that the mean firing rate is not a static function of the neuronal state but follows two coupled nonlinear differential equations (NMM2). Here we analyze and compare these two descriptions in the presence of second-order synaptic dynamics. First, we derive the mathematical equivalence between the two models in the infinitely slow synapse limit, i.e., we show that NMM1 is an approximation of NMM2 in this regime. Next, we evaluate the applicability of this limit in the context of realistic physiological parameter values by analyzing the dynamics of models with inhibitory or excitatory synapses. We show that NMM1 fails to reproduce important dynamical features of the exact model, such as the self-sustained oscillations of an inhibitory interneuron QIF network. Furthermore, in the exact model but not in the limit one, stimulation of a pyramidal cell population induces resonant oscillatory activity whose peak frequency and amplitude increase with the self-coupling gain and the external excitatory input. This may play a role in the enhanced response of densely connected networks to weak uniform inputs, such as the electric fields produced by noninvasive brain stimulation.
      PubDate: 2022-12-01
      DOI: 10.1007/s00422-022-00952-7
       
 
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