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Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking 1 Model with Reward Dependent Plasticity
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). Karolinska Institute, Stockholm, Sweden.
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). Karolinska Institute, Stockholm, Sweden.
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). Karolinska Institute, Stockholm, Sweden.ORCID-id: 0000-0002-0550-0739
Vise andre og tillknytning
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-185159OAI: oai:DiVA.org:kth-185159DiVA, id: diva2:918740
Merknad

QS 2016

Tilgjengelig fra: 2016-04-11 Laget: 2016-04-11 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Inngår i avhandling
1. Computational Dissection of the Basal Ganglia: functions and dynamics in health and disease
Åpne denne publikasjonen i ny fane eller vindu >>Computational Dissection of the Basal Ganglia: functions and dynamics in health and disease
2016 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The basal ganglia (BG), a group of nuclei in the forebrain of all vertebrates, are important for behavioral selection. BG receive contextual input from most cortical areas as well as from parts of the thalamus, and provide output to brain systems that are involved in the generation of behavior, i.e. the thalamus and the brain stem. Many neurological disorders such as Parkinson’s disease and Huntington’s disease, and several neuropsychiatric disorders, are related to BG. Studying BG enhances the understanding as to how behaviors are learned and modified. These insights can be used to improve treatments for several BG disorders, and to develop brain-inspired algorithms for solving special information-processing tasks.

 

In this thesis modeling and simulations have been used to investigate function and dynamics of BG. In the first project a model was developed to explore a new hypothesis about how conflicts between competing actions are resolved in BG. It was proposed that a subsystem named the arbitration system, composed of the subthalamic nucleus (STN), pedunculopontine nucleus (PPN), the brain stem, central medial nucleus of thalamus (CM), globus pallidus interna (GPi) and globus pallidus externa (GPe), resolve basic conflicts between alternative motor programs. On top of the arbitration system there is a second subsystem named the extension systems, which involves the direct and indirect pathway of the striatum. This system can modify the output of the arbitration system to bias action selection towards outcomes dependent on contextual information.

 

In the second project a model framework was developed in two steps, with the aim to gain a deeper understanding of how synapse dynamics, connectivity and neural excitability in the BG relate to function and dynamics in health and disease. First a spiking model of STN, GPe and substantia nigra pars reticulata (SNr), with emulated inputs from striatal medium spiny neurons (MSNs) and the cortex, was built and used to study how synaptic short-term plasticity affected action selection signaling in the direct-, hyperdirect- and indirect pathways. It was found that the functional consequences of facilitatory synapses onto SNr neurons are substantial, and only a few presynaptic MSNs can suppress postsynaptic SNr neurons. The model also predicted that STN signaling in SNr is mainly effective in a transient manner. The model was later extended with a striatal network, containing MSNs and fast spiking interneurons (FSNs), and modified to represent GPe with two types of neurons: type I, which projects downstream in BG, and type A, which have a back-projection to striatum. Furthermore, dopamine depletion dependent modification of connectivity and neuron excitability were added to the model. Using this extended BG model, it was found that FSNs and GPe type A neurons controlled excitability of striatal neurons during low cortical drive, whereas MSN collaterals have a greater impact at higher cortical drive. The indirect pathway increased the dynamical range over which two possible action commands were competing, while removing intrastriatal inhibition deteriorated action selection capabilities. Dopamine-depletion induced effects on spike synchronization and oscillations in the BG were also investigated here.

 

For the final project, an abstract spiking BG model which included a hypothesized control of the reward signaling dopamine system was developed. This model incorporated dopamine-dependent synaptic plasticity, and used a plasticity rule based on probabilistic inference called Bayesian Confidence Propagation Neural Network (BCPNN). In this paradigm synaptic connections were controlled by gathering statistics about neural input and output activity. Synaptic weights were inferred using Bayes’ rule to estimate the confidence of future observations from the input. The model exhibits successful performance, measured as a moving average of correct selected actions, in a multiple-choice learning task with a changeable reward schedule. Furthermore, the model predicts a decreased performance upon dopamine lesioning, and suggests that removing the indirect pathway may disrupt learning in profound ways.

sted, utgiver, år, opplag, sider
Stockholm: Kungliga Tekniska högskolan, 2016. s. 47
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 1653-5723
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-185160 (URN)978-91-7595-925-2 (ISBN)
Disputas
2016-05-04, FS, Lindstedtsvägen 26, KTH Campus, Stockholm, 13:00
Opponent
Veileder
Merknad

QC 20160412

Tilgjengelig fra: 2016-04-12 Laget: 2016-04-11 Sist oppdatert: 2018-01-10bibliografisk kontrollert
2. Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
Åpne denne publikasjonen i ny fane eller vindu >>Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2017. s. 89
Serie
TRITA-CSC-A, ISSN 1653-5723 ; 2017:11
Emneord
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
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
urn:nbn:se:kth:diva-205568 (URN)978-91-7729-351-4 (ISBN)
Disputas
2017-05-09, F3, Lindstedtsvägen 26, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Merknad

QC 20170421

Tilgjengelig fra: 2017-04-21 Laget: 2017-04-19 Sist oppdatert: 2017-04-21bibliografisk kontrollert

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