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Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits

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Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits PHILIP J. TULLY Doctoral Thesis Stockholm, Sweden 2017 TRITA-CSC-A-2017:11 ISSN 1653-5723 KTH School of Computer Science and Communication ISRN-KTH/CSC/A-17/11-SE SE-100 44 Stockholm ISBN 978-91-7729-351-4 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framläg- ges till offentlig granskning för avläggande av teknologie doktorsexamen i datalogi tisdagen den 9 maj 2017 klockan 13.00 i F3, Lindstedtsvägen 26, Kungl Tekniska högskolan, Valhallavägen 79, Stockholm. © Philip J. Tully, May 2017 Tryck: Universitetsservice US AB iii Abstract 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 osten- sibly propel them towards runaway excitation or quiescence. What dynamical phe- nomena 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. Inspiring some of the most seminal neuroscience exper- iments, theoretical models provide insights that demystify experimental measure- ments and even inform new experiments. 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, neuromod- ulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, post- synaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels. In terms of large-scale networks, the model can account for a diversity of functions, processes and timescales exhibited by the cerebral cortex and basal ganglia. Imbued with spike-based BCPNN, a modular cortical memory network can learn meta-stable sequential or nonsequential transitions. The trajectory of attractor states traversed by the network is governed by externally presented stimuli and internal network parameters. A spike-based BCPNN cortical model can perform basic perceptual grouping operations, and a spike-based BCPNN basal ganglia model can learn to dis-inhibit reward-seeking actions in a multiple-choice learning task with transiently changing reward schedules. Numerical simulations are carried out on both a Cray XC-30 supercomputer and on SpiNNaker: the digital, neuromorphic architecture designed for simulating large-scale spiking neuronal networks using general-purpose ARM processors. Spiking cortical networks are scaled up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses. At the time of publishing, this represents the largest plastic neural network ever simulated on neuromorphic hardware. 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. It sheds light upon many cognitive and motor functions, including some high-level phenomena associated with Parkinson’s disease. The thesis will argue that spiking neural networks can represent information in the form of probability distributions, and that this biophysical realization of Bayesian computation can help reconcile disparate experimental observations. iv Sammanfattning Cortikala och sub-cortikala nätverk I hjärnan som förändras under hela ens liv. De fungerar trots destabiliserande synaptiska och icke-synaptiska mekanismer som borde driva nätverken till okontrollerad aktivitetsökning eller tystnande existerar. Vilka dynamiska fenomen finns som möjliggör en balansering av nätverken? Hur representerad fenomenen i form av aktivitetsmönster och hur relaterar de till våra sinnesupplevelser? Var möjliggör att inlärning och minnesoperationer kan fungera trots den neurala omorganisation som hela tiden pågår i hjärnan? Ett steg mot att besvara dessa frågor kan göras genom storskalig simulering av neurala nätverk. Matematisk modellering har gett betydande bidrag till några av de mest innovativa experiment inom neurovetenskapen. I denna avhandling intro- duceras en Hebbiansk inlärningsregel för neuroner med actionspotentialer framtagen med inspiration från statistisk inferens. Den spikbaserade versionen av Bayesian Confidence Propagation Neural Network (BCPNN) inlärningsregel involverar för- ändringar i både av synapsstyrka samt strömstyrka inne i nervcellerna. Modellen bygger på idén att utfallet av molekylära reaktionen i synapserna och nervcellerna beskrivas genom biologiska mekansimer så som hebbiansk inlärning, homeostatisk plasticitet och nervcellers aktiveringsbarhet. Tillfällig integration mellan minnesre- laterade aktivitet möjliggör tidsberoenden mellan aktionspotentialer och en stabil inlärningsregim som förblir funktionell med avseende på postsynaptisk aktivitets- reglering, actionpotentialsberoendebaserad inlärning samt förändring av nervcellers aktivitetsgrad. Med avseende på storskaliga nätverk kan modellen förklara funktionell mångfald vil- ket inkluderar processer och tidsskalor som kan återfinnas i cerebrala cortex (hjärn- barken) och basala ganglierna. Ett modulärt cortikalt minnesnätverk som använder sig har actionspotentialsbaserad BCPNN-inlärning kan lära sig metastabila sekventi- ella och icke-sekventiella övergångar. Sekvensen av aktivitetsmönster som nätverket genomgår styrs både av externstimuli och av modellparametrar. En cortical BCPNN modell klarar av att genomföra enkla sinnliga grupperingsoperationer samt en mo- dell av basal ganglierna med BCPNN kan lära sig att selektera handlingar i en flervalsuppgift med belöningsbaserad inlärning som har ett skiftande belöningssche- man. Simuleringarna I denna avhandling gjordes på både en Cray XC-30 superdator samt på SpiNNaker - en digital arkitektur inspirerad av hjärnan byggd på ARM pro- cessorer. Nätverksstorleken har skalats upp till 2.0 × 104 nervceller samt 5.1 × 107 plastiska synapser. I skrivandets stund representerar detta det största nätverk med plastiska synapser som någonsin har simulerats på en SpiNNaker arkitektur. Denna avhandling ämnar visa hur flera olika plasticitetsmekansimer kan intera- gera och åstadkomma belöningsbaserad, auto- och hetero associativ inlärning och inkluderar flera högnivåfenomen som associerats med Parkinsons sjukdom. Denna avhandling kommer visa hur neurala nätverk med aktionspotentialer kan represen- tera information som sannolikhetsdistributioner och hur en biofysikalisk realisering av Bayesianska beräkningar kan sammanföra spretiga experimentella observationer. v Acknowledgements My deepest gratitude is to my first supervisor, Professor Anders Lansner. It has been an amazing experience working with a scholar who is so dedicated to ensur- ing my growth both as a student and a person. Your tireless efforts to meet with me to offer advice have helped me to excel in a comfortable atmosphere that was conducive to the free exchange of ideas. Even outside of my doctoral endeavor, you’ve provided the motivation to help me to achieve recognition both at KTH and beyond. Your contributions to this work and to shaping my success cannot merely be expressed by the words on this page. You’ve been a huge part of my life for the past five years, and most of all, I am thankful for your friendship. I would like to thank my second supervisor, Dr. Matthias Hennig, for the count- less hours you’ve spent helping me explore new possibilities and academic avenues. Your different perspectives and healthy skepticism and have inspired me to achieve a deep and broad set of knowledge that has not only benefitted my PhD thesis work, but also my scientific approach as a whole. I’ll take these invaluable lessons with me as I go forward in the next steps of my career. To Professor Örjan Ekeberg, for nudging me to pursue the opportunity to do my PhD abroad in the first place, and dragging me away from that small liberal arts school in upstate New York. The world is much bigger and better than I could have ever imagined back then, so thanks for helping break me out of my bubble. To my two office roommates - Dr. Pierre Berthet and Dr. Bernhard Kaplan - thank you for all the moments of wit and levity, putting up with my groans and keyboard smashes, and for helping drown the PhD stress away with after-work pub sessions. I would also like to thank my fellow lab members and collaborators over all the years - Dr. Pawel Herman, Dr. Mikael Lindahl, Dr. Henrik Lindén, Pradeep Krishnamurthy, Dr. Jamie Knight, Prof. Steve Furber, Cristina Meli, Dr. Mar- cus Petersson, Prof. Jeanette Hellgren-Kotaleski, Dr. Mikael Lundqvist, Prof. Erik Fransén, Florian Fiebig, Peter Åhlström, Ekaterina Brocke, Prof. Tony Lin- deberg, David Silverstein, Dr. Omar Arenas, Jovana Belic, Ramón Mayorquin, Jan Pieczkowski, Anu Nair, Miha Pelko, Dr. Paolo Puggioni, Jyotika Bahuguna, Nathalie Dupuy, Renato Duarte, Dr. Yann Sweeney, Dinesh Natesan, Daniel
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