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Hardware Implementation of a Bio-plausible Neuron Model for Evolution and Growth of Spiking Neural Networks on FPGA

Hooman Shayani, Peter J. Bentley, Andy M. Tyrrell
2008 2008 NASA/ESA Conference on Adaptive Hardware and Systems  
We propose a digital neuron model suitable for evolving and growing heterogeneous spiking neural networks on FPGAs by introducing a novel flexible dendrite architecture and the new PLAQIF (Piecewise-Linear  ...  A network of 161 neurons and 1610 synapses was simulated, implemented, and verified on a Virtex-5 chip with 4210 times real-time speed with 1 ms resolution.  ...  FPGA-based POE Spiking Neural Networks The evolution of directly mapped recurrent spiking neural networks on FPGAs has been tackled by a few researchers (e.g.  ... 
doi:10.1109/ahs.2008.13 dblp:conf/ahs/ShayaniBT08 fatcat:zb33pcttgzgmfjklxpxexirzfi

A Multi-cellular Developmental Representation for Evolution of Adaptive Spiking Neural Microcircuits in an FPGA

Hooman Shayani, Peter J. Bentley, Andy M. Tyrrell
2009 2009 NASA/ESA Conference on Adaptive Hardware and Systems  
Here, a bio-inspired developmental genotype-phenotype mapping for evolution of spiking neural microcircuits in an FPGA is introduced, based on a digital neuron model and cortex structure suggested and  ...  It has been shown that evolutionary and developmental processes can be used for emergence of scalability, robustness and fault-tolerance in hardware.  ...  A bio-inspired multi-cellular developmental process for growing spiking neural microcircuits in an FPGA, based on a flexible and bio-plausible digital neuron model and cortex structure was introduced.  ... 
doi:10.1109/ahs.2009.39 dblp:conf/ahs/ShayaniBT09 fatcat:hy7bw7i6avevjjadkwtpyamgba

POEtic Tissue: An Integrated Architecture for Bio-inspired Hardware [chapter]

Andy M. Tyrrell, Eduardo Sanchez, Dario Floreano, Gianluca Tempesti, Daniel Mange, Juan-Manuel Moreno, Jay Rosenberg, Alessandro E. P. Villa
2003 Lecture Notes in Computer Science  
The reasons for this are many, but one of the main problems has always been the lack of a universal platform, and of a proper methodology for the implementation of such systems.  ...  The implementation of bio-inspired systems in hardware has however been limited, and more often than not been more a matter of artistry than engineering.  ...  The Community is not responsible for any use that might be made of data appearing in this publication.  ... 
doi:10.1007/3-540-36553-2_12 fatcat:27zisuni7ne4zhr4pow5k75kwi

Brain-Inspired Hardware Solutions for Inference in Bayesian Networks

Leila Bagheriye, Johan Kwisthout
2021 Frontiers in Neuroscience  
This comprehensive review paper discusses different hardware implementations of Bayesian networks considering different devices, circuits, and architectures, as well as a more futuristic overview to solve  ...  The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due  ...  FIGURE 14 14 FIGURE 14 | (A) A generic structure of spiking neural networks. (B) Neuron 3 receives spikes from Neuron 1 and Neuron 2.  ... 
doi:10.3389/fnins.2021.728086 pmid:34924925 pmcid:PMC8677599 fatcat:tihogzl6tfbpjdybwpggllwd5u

An Overview of Neuromorphic Computing for Artificial Intelligence Enabled Hardware-based Hopfield Neural Network

Zheqi Yu, Amir M. Abdulghani, Adnan Zahid, Hadi Heidari, Muhammad A. Imran, Qammer H. Abbasi.
2020 IEEE Access  
Inspired by biology, this novel system has implemented the theory of human brain modeling by connecting feigned neurons and synapses to reveal the new neuroscience concepts.  ...  Towards the end, we conclude with a broad discussion and a viable plan for the latest application prospects to facilitate developers with a better understanding of the aforementioned model in accordance  ...  ACKNOWLEDGMENT Authors would like to thank Sultan Qaboos University (Government of the Sultanate of Oman) for supporting Dr. A. M. Abdulghani.  ... 
doi:10.1109/access.2020.2985839 fatcat:mclixaatyzbk7kn4lvshxw7aie

Evolutionary morphogenesis for multi-cellular systems

Daniel Roggen, Diego Federici, Dario Floreano
2006 Genetic Programming and Evolvable Machines  
It outperforms a direct genetic encoding when evolving spiking neural networks for  ...  The morphogenetic system is inspired by gene expression and cellular differentiation. It focuses on low computational requirements which allows fast execution and a compact hardware implementation.  ...  The information provided is the sole responsibility of the authors and does not reflect the Community's opinion.  ... 
doi:10.1007/s10710-006-9019-1 fatcat:ov5m62flgjek7g7jea7sschmzm

Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons

Umang Garg, Kezhou Yang, Abhronil Sengupta
2021 Frontiers in Neuroscience  
We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced  ...  Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain.  ...  The work presented in this article is guided by the observation that current neuromorphic computing architectures have mainly focused on emulation of bio-plausible computational models for neuron and synapse-but  ... 
doi:10.3389/fnins.2021.699632 pmid:34712110 pmcid:PMC8546188 fatcat:jkxjxk5npjbvrmkzkbqwxcujau

Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons [article]

Umang Garg, Kezhou Yang, Abhronil Sengupta
2021 arXiv   pre-print
We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced  ...  Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain.  ...  ACKNOWLEDGMENTS The work was supported in part by the National Science Foundation grants BCS #2031632, ECCS #2028213 and CCF #1955815.  ... 
arXiv:2007.00776v6 fatcat:dya5ocu6jre2feabjxu3r7oj3q

Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain [article]

Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs, Giacomo Indiveri, Kundan Kumar, Ning Qiao, Johannes Schemmel, Runchun Wang, Elisabetta Chicca, Jennifer Olson Hasler, Jae-sun Seo, Shimeng Yu, Yu Cao, André van Schaik, Ralph Etienne-Cummings
2018 arXiv   pre-print
NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal  ...  Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain.  ...  The paper presented a systematic method for configuring reliable Finite State Machine (FSM) computation on spiking neural arrays implemented on neuromorphic hardware.  ... 
arXiv:1805.08932v1 fatcat:xqtzbpp5ubhfrpfhj6sjffi6ii

Towards making a cyborg: A closed-loop reservoir-neuro system

Peter Aaser, Martinius Knudsen, Ola Huse Ramstad, Rosanne van de Wijdeven, Stefano Nichele, Ioanna Sandvig, Gunnar Tufte, Ulrich Stefan Bauer, Øyvind Halaas, Sverre Hendseth, Axel Sandvig, Vibeke Valderhaug
2017 Proceedings of the 14th European Conference on Artificial Life ECAL 2017  
Furthermore, we describe the bio-and nano-technological procedures utilized for the culture of such dissociated neural networks and the interface software and hardware framework used for creating a closed-loop  ...  In this paper we describe how living cultures of neurons (biological neural networks) are successfully grown in-vitro over Micro-Electrode Arrays (MEAs), which allow them to be interfaced to a robotic  ...  Acknowledgements The research leading to these results has received funding from the Norwegian Research Council's IKTPLUSS programme (project SOCRATES, n. 270961), from NTNU, and from HiOA.  ... 
doi:10.7551/ecal_a_072 dblp:conf/ecal/AaserKRWNSTBHHS17 fatcat:l34cr2clpvgfflu5ezrxyz62qi

2022 Roadmap on Neuromorphic Computing and Engineering [article]

Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano (+47 others)
2022 arXiv   pre-print
The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic  ...  Modern computation based on the von Neumann architecture is today a mature cutting-edge science.  ...  Acknowledgements The author thanks the CNRS for support. Acknowledgements The author would like to thank E. Donati for fun and insightful discussions and brainstorming on the topic.  ... 
arXiv:2105.05956v3 fatcat:pqir5infojfpvdzdwgmwdhsdi4

Neuromorphic Photonics, Principles of [chapter]

Bhavin J. Shastri, Alexander N. Tait, Thomas Ferreira de Lima, Mitchell A. Nahmias, Hsuan-Tung Peng, Paul R. Prucnal
2018 Encyclopedia of Complexity and Systems Science  
We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection  ...  The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of neuromorphic photonics.  ...  For example, one of the simplest models of a spiking neuron is called leaky integrate-and-fire (LIF), described in Eq. 1.  ... 
doi:10.1007/978-3-642-27737-5_702-1 fatcat:h3qtsskafzgq3ipmtxa2xlfksi

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera, Maurizio Martina, Muhammad Shafique
2020 IEEE Access  
This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance  ...  In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking  ...  BACKGROUND ON DEEP NEURAL NETWORKS (DNNS) The constituent element of a neural network is the neuron, also called perceptron, a computational block that attempts to model the behavior of a biological neuron  ... 
doi:10.1109/access.2020.3039858 fatcat:nticzqgrznftrcji4krhyjxudu

Spatial-Temporal Entangled Sparse Distributed Storage (STE-SDS) and Sparse Distributed Code (SDC) in the Systolic Hebb Agnostic Resonance Perceptron (SHARP) Proposed as Hypothetical Model Linking Mini and Macro-Column Scale Functionality in the Cerebral Cortex [chapter]

Luca Marchese
2016 Smart Innovation, Systems and Technologies  
This paper describes a bio-inspired spiking neural network that is proposed as a model of a cortical area network and is tailored to be the brick of a modular framework for building self-organizing neurocognitive  ...  The neuron models are biologically inspired but not biologically plausible.  ...  The growth of memory has been 1000 times the growth of clock frequency Figure 34 . 34 The hardware implementation with stages composed of a CPLD and a flash memory.  ... 
doi:10.1007/978-3-319-33747-0_15 fatcat:fephvqswmjdilcwh4vfvsnfcde

Memristive and CMOS Devices for Neuromorphic Computing

Valerio Milo, Gerardo Malavena, Christian Monzio Compagnoni, Daniele Ielmini
2020 Materials  
Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures.  ...  First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed.  ...  Acknowledgments This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 648635).  ... 
doi:10.3390/ma13010166 pmid:31906325 pmcid:PMC6981548 fatcat:mqi7putgvvc2ddlm7i2qqt6zh4
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