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Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0001-8796-3237
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2016 (English)In: Frontiers in Neuroanatomy, ISSN 1662-5129, E-ISSN 1662-5129, Vol. 10, 37Article in journal (Refereed) Published
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Text
Abstract [en]

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 x 10(4) neurons and 5.1 x 10(7) plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45x more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

Place, publisher, year, edition, pages
2016. Vol. 10, 37
Keyword [en]
SpiNNaker, learning, plasticity, digital neuromorphic hardware, Bayesian confidence propagation neural network (BCPNN), event-driven simulation, fixed-point accuracy
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-185975DOI: 10.3389/fnana.2016.00037ISI: 000373595100002Scopus ID: 2-s2.0-84966267433OAI: oai:DiVA.org:kth-185975DiVA: diva2:926698
Note

QC 20160509

Available from: 2016-05-09 Created: 2016-04-29 Last updated: 2017-04-19Bibliographically approved
In thesis
1. Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
Open this publication in new window or tab >>Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns

do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations. 

 

In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels. 

 

The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. 89 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:11
Keyword
Bayes' rule, synaptic plasticity and memory modeling, intrinsic excitability, naïve Bayes classifier, spiking neural networks, Hebbian learning, neuromorphic engineering, reinforcement learning, temporal sequence learning, attractor network
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-205568 (URN)978-91-7729-351-4 (ISBN)
Public defence
2017-05-09, F3, Lindstedtsvägen 26, Stockholm, 13:00 (English)
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Supervisors
Note

QC 20170421

Available from: 2017-04-21 Created: 2017-04-19 Last updated: 2017-04-21Bibliographically approved

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