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A Model of Neural Inspiration for Local Accumulative Computation
[chapter]
2003
Lecture Notes in Computer Science
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This paper explores the computational capacity of a novel local computational model that expands the conventional analogical and logical dynamic neural models, based on the charge and discharge of a capacity ...
This model is denominated as accumulative computation and is inspired in biological short-term memory mechanisms. ...
of a novel local computational model that expands the conventional analogical and logical dynamic neural models, based on the charge and discharge of a capacity or in the use of a D flip-flop. ...
doi:10.1007/978-3-540-45210-2_39
fatcat:r5tc7rnywrgunb3mfivia7pury
Preface
2018
Natural Computing
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These papers present contributions of bio-inspired techniques as neural computation, swarm optimization, evolutionary algorithms or local search to some complex problems in artificial vision, protein folding ...
In this paper, the authors consider lateral interaction in accumulative computation (LIAC), the implementation for computer vision of two biologically-inspired methods denominated algorithmic lateral interaction ...
doi:10.1007/s11047-018-9717-7
fatcat:5ouk5tjycrf33dcytdn4f7yqey
Lateral interaction in accumulative computation: a model for motion detection
2003
Neurocomputing
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This model is based on a series of neuronal models in one layer, namely the local accumulative computation model, the double time scale model and the recurrent lateral interaction model. ...
In this paper we present a novel model based on neural networks denominated lateral interaction in accumulative computation (LIAC). ...
Acknowledgements The authors would like to thank the anonymous referees for their very helpful comments that signiÿcantly improved the manuscript. ...
doi:10.1016/s0925-2312(02)00571-4
fatcat:vhvbi54sc5hppnx4nigkdl3sfe
A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †
2018
Sensors
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cases of accumulated charge in a local memory. ...
With the aim of attaining the necessary real-time performance, we used a model of the neutrally-inspired accumulative computation (AC) method, a simplified version of the ALI method in which the more time-consuming ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s18051420
pmid:29751584
pmcid:PMC5982089
fatcat:kkwbnfrlhbg45fdvpjuvplj7ku
The Evolution of the Evolving Neuro-Fuzzy Systems: From Expert Systems to Spiking-, Neurogenetic-, and Quantum Inspired
[chapter]
2013
Studies in Fuzziness and Soft Computing
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This chapter follows the development of a class of intelligent information systems called evolving neuro-fuzzy systems (ENFS). ...
The review includes fuzzy expert systems, fuzzy neuronal networks, evolving connectionist systems, spiking neural networks, neurogenetic systems, and quantum inspired systems, all discussed from the point ...
I would like to thank Diana Kassabova for helping me with the manuscript and the editors of the volume for their tremendous effort to put together a memorable collection of chapters representing both the ...
doi:10.1007/978-3-642-35641-4_41
fatcat:aghg3dtw3feyll2rzhhjljfanq
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition
[article]
2022
arXiv
pre-print
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Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical ...
However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server ...
Then, the vehicle computes the gradient change of the local model (Equations ( 8 ) and ( 11 )) based on the loss to update the model weights. 3) The RSU collects the model parameters uploaded by the ...
arXiv:2205.14315v1
fatcat:e26a3yhlp5h47ih56mgfaghae4
Real-Time Accumulative Computation Motion Detectors
2009
Sensors
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The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. ...
Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. ...
in the state of accumulated charge in a local memory [11] . ...
doi:10.3390/s91210044
pmid:22303161
pmcid:PMC3267209
fatcat:s7duri2aevd5xocqow3pjzl7wu
Brain-Inspired Learning on Neuromorphic Substrates
[article]
2020
arXiv
pre-print
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This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates. ...
Specifically, we show a direct connection between Real-Time Recurrent Learning (RTRL), an online algorithm for computing gradients in conventional Recurrent Neural Networks (RNNs), and biologically plausible ...
emulation of brain-inspired neural networks. ...
arXiv:2010.11931v1
fatcat:e7bwgrmynvgmfkuordiqb3zusq
Using haptics to extract object shape from rotational manipulations
2014
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
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In the present paper, we thus explore whether a biologically inspired model based on dynamic neural fields can offer a route towards a practical algorithm for tactile SLAM. ...
We demonstrate that our model can accumulate shape information from reasonably short interaction sequences and autonomously build a representation despite significant ambiguity of the tactile data due ...
This comparison mechanism has been previously used in a neurally-inspired model for coordinate frame transformations [29] . ...
doi:10.1109/iros.2014.6942856
dblp:conf/iros/StrubWRS14
fatcat:yxa5wqtqdffbzac76cvsypugse
Brain-Inspired Learning on Neuromorphic Substrates
2021
Proceedings of the IEEE
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This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates. ...
Specifically, we show a direct connection between real-time recurrent learning (RTRL), an online algorithm for computing gradients in conventional recurrent neural networks (RNNs), and biologically plausible ...
of brain-inspired neural networks. ...
doi:10.1109/jproc.2020.3045625
fatcat:pelkbpbg5jg7pjyvkvtpgrt2su
Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM
2018
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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In this paper, we investigate the use of ultra low-power, mixed signal analog/digital neuromorphic hardware for implementation of biologically inspired neuronal path integration and map formation for a ...
for a mobile robot. ...
For instance, inspired by the navigation system of rats, a competitive biologically-inspired neural SLAM system -RatSLAM -was proposed [11] , [6] . ...
doi:10.1109/iros.2018.8594228
dblp:conf/iros/KreiserRSP18
fatcat:tj33u5m4sbh5fd4mo5dlsjm4qi
Simulating mirror-neuron responses using a neural model for visual action recognition
2008
BMC Neuroscience
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Transfected and endogenous Kv2.1 is now demonstrated to preferentially accumulate within the axon initial segment (AIS) over other neurite processes; 87% of 14 DIV hippocampal neurons show endogenous channel ...
Photobleach studies indicated individual channels within the cluster perimeter were highly mobile (FRAP tau=10.4+/-4.8 sec), supporting our model that Kv2.1 clusters are formed by the retention of mobile ...
We present a neurophysiologically inspired model for the visual recognition of hand movements. ...
doi:10.1186/1471-2202-9-s1-p112
pmid:19014551
pmcid:PMC2592246
fatcat:p6vju3ritzfhrdxqe7eammlrbu
Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects
[article]
2021
arXiv
pre-print
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Through this survey, it will provide a comprehensive reference for research in this direction. ...
Then, some points of view about the prospects of the visual perception computational models are presented. ...
[43] proposed a local-global memory neural network (LGMNN) model ,where the local memory helps learn the individual patterns of a patient and the global memory can learn the group information of disease ...
arXiv:2109.03391v1
fatcat:xtgda2x6azd2laun45tqfj77gi
Neuromorphic Electronic Systems for Reservoir Computing
[article]
2020
arXiv
pre-print
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This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. ...
Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been ...
Introduction The term "neuromorphic computing" refers to a variety of brain-inspired computers, architectures, devices, and models that are used in the endeavor to mimic biological neural networks [1] ...
arXiv:1908.09572v2
fatcat:cimkbnvyrjc3lhixlyufgmqy3i
A neural path integration mechanism for adaptive vector navigation in autonomous agents
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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Input signals from an allothetic compass and odometry are sustained through leaky neural integrator circuits, which are then used to compute the home vector by local excitation-global inhibition interactions ...
The emergent behavior of the controlled agent does not only show a robust solution for the problem of autonomous agent navigation, but it also reproduces various aspects of animal navigation. ...
ACKNOWLEDGMENT This research was supported by the Emmy Noether Program (DFG, MA4464/3-1), and the Federal Ministry of Education and Research (BMBF) by a grant to the Bernstein Center for Computational ...
doi:10.1109/ijcnn.2015.7280400
dblp:conf/ijcnn/GoldschmidtDWM15
fatcat:7xrpkxtr45g6bkdnxeen2pc74a
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