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Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI

T. Yu, T. J. Sejnowski, G. Cauwenberghs
2011 IEEE Transactions on Biomedical Circuits and Systems  
Index Terms-Analog VLSI, biophysical neural dynamics, neuromorphic engineering, programmable channel kinetics, silicon neuron interfaces, spiking neuron models.  ...  The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane  ...  Chi for help with the experimental setup, and the MOSIS Educational Program for fabricating the chip.  ... 
doi:10.1109/tbcas.2011.2169794 pmid:22227949 pmcid:PMC3251010 fatcat:fcjjxsuxwzcorocxj3t6ysjl6m

Controlling articulated robots in task-space with spiking silicon neurons

Samir Menon, Sam Fok, Alex Neckar, Oussama Khatib, Kwabena Boahen
2014 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics  
Factorizing the controller reduced the neural regression's complexity to cubic in the dynamic range of the robot's state and desired forces.  ...  The controller is compliant and can draw shapes with a pen on a dynamically perturbed surface while remaining stable.  ...  TASK-SPACE CONTROL WITH SPIKING NEURONS We mapped our task-space force controller on to Neurogrid using the Neural Engineering Framework [10] , which computes functions with pools of spiking neurons by  ... 
doi:10.1109/biorob.2014.6913773 dblp:conf/biorob/MenonFNKB14 fatcat:3axwjujjzbeord3ieorhprzuvi

Breaking the virtual barrier: real-time interactions with spiking neural models

Corey M Thibeault, Frederick C Harris, Philip H Goodman
2010 BMC Neuroscience  
Presented here is a toolkit, dubbed NCSTools, used for real-time interactions with large-scale neural simulations run on the NeoCortical Simulator (NCS).  ...  The model information flow can be altered dynamically by NCSTools, as can model parameters  ...  These programs can be used for control of defined NCSTools actions, monitoring specific neural information, or synchronization with the simulation.  ... 
doi:10.1186/1471-2202-11-s1-p73 pmcid:PMC3090963 fatcat:cmae5lh7mbax3dblcrncttrkpm

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

Florian Walter, Florian Röhrbein, Alois Knoll
2015 Neural Networks  
Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language.  ...  The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks  ...  The actual computation of neural dynamics is implemented in the Processing Core.  ... 
doi:10.1016/j.neunet.2015.07.004 pmid:26422422 fatcat:qbpqali565blfphyprdvy6o3lm

Neural dynamics in reconfigurable silicon

Arindam Basu, Shubha Ramakrishnan, Paul Hasler
2010 Proceedings of 2010 IEEE International Symposium on Circuits and Systems  
Emphasis is placed on replicating the detailed dynamics of computational neural models.  ...  The programmability is achieved using floating gate transistors with on-chip programming control.  ...  CONCLUSION We presented a reconfigurable integrated circuit for accurately describing neural dynamics and computations.  ... 
doi:10.1109/iscas.2010.5536960 dblp:conf/iscas/BasuRH10 fatcat:2sp2qconqfdanetrpim4bp2fgy

Neural Dynamics in Reconfigurable Silicon

Arindam Basu, Shubha Ramakrishnan, Csaba Petre, Scott Koziol, Stephen Brink, Paul E. Hasler
2010 IEEE Transactions on Biomedical Circuits and Systems  
Emphasis is placed on replicating the detailed dynamics of computational neural models.  ...  The programmability is achieved using floating gate transistors with on-chip programming control.  ...  CONCLUSION We presented a reconfigurable integrated circuit for accurately describing neural dynamics and computations.  ... 
doi:10.1109/tbcas.2010.2055157 pmid:23853376 fatcat:aazltzdporffle6brlldnpllu4

Biologically Sound Neural Networks for Embedded Systems Using OpenCL [chapter]

István Fehérvári, Anita Sobe, Wilfried Elmenreich
2013 Lecture Notes in Computer Science  
In this paper, we present an OpenCL implementation of a biologically sound spiking neural network with two goals in mind: First, applied neural dynamics should be accurate enough for bio-inspired training  ...  We show that an optimized GPU kernel code can perform sufficiently fast to be used for future embedded neural processing.  ...  Our results also compare to similar implementations of spiking neural networks with different neural dynamics [10] .  ... 
doi:10.1007/978-3-642-40148-0_18 fatcat:p4vdazof35cn7jnsq5qgzk7qyi

Data and Power Efficient Intelligence with Neuromorphic Learning Machines

Emre O. Neftci
2018 iScience  
The success of deep networks and recent industry involvement in brain-inspired computing is igniting a widespread interest in neuromorphic hardware that emulates the biological processes of the brain on  ...  We find that (1) recent work in binary deep networks and approximate gradient descent learning are strikingly compatible with a neuromorphic substrate; (2) where real-time adaptability and autonomy are  ...  Matching Neural and Synaptic Dynamics Several computational models of the brain argue that computational optimality hinges on plasticity dynamics and neural dynamics being matched (Lengyel et al., 2005  ... 
doi:10.1016/j.isci.2018.06.010 pmid:30240646 pmcid:PMC6123858 fatcat:zo4dvtgo75c7pn6n7tal24fkly

Deep learning incorporating biologically-inspired neural dynamics [article]

Stanisław Woźniak, Angeliki Pantazi, Thomas Bohnstingl, Evangelos Eleftheriou
2019 arXiv   pre-print
Simultaneously, Spiking Neural Networks (SNNs) incorporating biologically-feasible spiking neurons have held great promise because of their rich temporal dynamics and high-power efficiency.  ...  Here we show an alternative perspective on the spiking neuron that casts it as a particular ANN construct called Spiking Neural Unit (SNU), and a soft SNU (sSNU) variant that generalizes its dynamics to  ...  The neuron N2 with a step activation function reproduces the spiking behaviour, whereas the neuron N2 with a sigmoid activation function generalizes the neural dynamics beyond the spiking case. c, SNUs  ... 
arXiv:1812.07040v2 fatcat:bbvknvrgzrcfzkge34nsapq5vy

Dynamic parallelism for synaptic updating in GPU-accelerated spiking neural network simulations

Bahadir Kasap, A. John van Opstal
2018 Neurocomputing  
Theoretical neuroscientists have exploited the use of general purpose computing on GPUs, in neural field model computations and spiking neural network simulations [2] .  ...  The same problem exists in spiking neural network simulations on other parallel computing architectures [10].  ...  In terms of spiking neural network simulations, dynamic parallelism substantially accelerates the massive neural computations, by implementing the spike-triggered calculations at each synaptic updating  ... 
doi:10.1016/j.neucom.2018.04.007 pmid:30245550 pmcid:PMC6147227 fatcat:knhi4bij75b35m3ocnekjxtn4i

Modular Deconstruction Reveals the Dynamical and Physical Building Blocks of a Locomotion Motor Program

Angela M. Bruno, William N. Frost, Mark D. Humphries
2015 Neuron  
systems with richer, faster dynamics.  ...  We computed an auto-correlogram A with 1 s bins up to a maximum lag of 20 s; the bin-size was chosen to be smaller than the characteristic timescale of the motor program in every recording ( Figure S1  ... 
doi:10.1016/j.neuron.2015.03.005 pmid:25819612 pmcid:PMC6016739 fatcat:yfv53s2gqzcb3ntk74bv4io5oa

Programmable neuromorphic circuits for spike-based neural dynamics

Mostafa Rahimi Azghadi, Saber Moradi, Giacomo Indiveri
2013 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS)  
Hardware implementations of spiking neural networks offer promising solutions for a wide set of tasks, ranging from autonomous robotics to brain machine interfaces.  ...  We propose a set of programmable hybrid analog/digital neuromorphic circuits than can be used to build compact low-power neural processing systems.  ...  The chip implements a spiking neural network of 32 adaptive exponential Integrate-and-Fire (I&F) neuron circuits [16] with dynamic synapse circuits.  ... 
doi:10.1109/newcas.2013.6573600 dblp:conf/newcas/AzghadiMI13 fatcat:yh6dxsid2vfufe5kii445olehq

Recent Advances and New Frontiers in Spiking Neural Networks [article]

Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Qingyu Wang, Bo Xu
2022 arXiv   pre-print
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven  ...  With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot.  ...  Acknowledgments This work was supported by the National Key R&D Program of China (2020AAA0104305), the Shanghai Municipal Science and Technology Major Project, and the Strategic Priority Research Program  ... 
arXiv:2204.07050v4 fatcat:zagxemwbtvdhtevycfdbie2r5q

Low-Power Neuromorphic Hardware for Signal Processing Applications [article]

Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa,, Evangelos Eleftheriou
2019 arXiv   pre-print
Inspired by the time-encoding mechanism used by the brain, third generation spiking neural networks (SNNs) are being studied for building a new class of information processing engines.  ...  Hence, novel computational architectures that address the von Neumann bottleneck are necessary in order to build systems that can implement SNNs with low energy budgets.  ...  The chip has 128 neural cores, with each core having 1024 spiking neurons and 2 Mb SRAM to store the connectivity, configuration, and dynamic state of all neurons within the core.  ... 
arXiv:1901.03690v3 fatcat:34eavryprvdaxcvuteujwizeia

Large-scale spiking circuit simulation of spatio-temporal dynamics in superior colliculus

Richard E Veale, Tadashi Isa, Masatoshi Yoshida
2014 BMC Neuroscience  
We present computational simulations of the intrinsic circuitry of the superior colliculus using large-scale spiking neural circuit models.  ...  This contrasts with the motor-related intermediate layers ( Figure 1B) , whose response to stimulation was best explained by both spatial and temporal symmetry between inhibitory and excitatory neural  ...  Acknowledgements R.V. is a GRF and IGERT; this research was supported by NSF GROW/JSPS Strategic Program.  ... 
doi:10.1186/1471-2202-15-s1-p4 pmcid:PMC4126475 fatcat:qt5ydrm73bhhfem3cahtt6ckse
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