Hennig, Matthias

Kotaleski, Jeanette Hellgren

Trpevski, Daniel

European Commission: Erasmus Mundus PhD programme: EuroSPIN, the European Study Programme in Neuroinformatics

2025-02-21T16:31:31Z

2025-02-21T16:31:31Z

2025-02-25

In this thesis we studied synaptic plasticity and neuronal computation in single striatal projection neurons (SPNs), which have a major role in goal-directed learning. Goal-directed or reward learning means to learn, based on sensory information from the body and the environment, to select actions out of all the behavioral repertoire that lead to obtaining a goal or reward (such as food or water). In mammals, all the behavioral motor repertoire is under constant, tonic inhibition, and the direct-pathway SPNs (dSPNs) select (disinhibit) goal-obtaining actions. The learning process is guided by the neuromodulator dopamine which signals the positive or negative outcome of an action. The synapses from cortical neurons onto the dSPNs, called corticostriatal synapses, are responsive to dopamine signals, and can strengthen and weaken based on the (positive or negative) action outcome. This promotes or discourages future actions in the same or similar sensory context. Within a collaborative computational modeling effort, we studied the biochemical circuitry in the corticostriatal synapses with multiscale modeling and simulations. This circuitry in the corticostriatal synapses responds to neuromodulatory signals and controls the expression of synaptic plasticity. Multiscale modeling and simulations enable studying a system at multiple temporal and spatial scales, and integrating the results across the different scales. Based on molecular dynamics simulations of the enzyme which transduces extracellular neuromodulatory signals into an intracellular second messenger molecule, and Brownian dynamics simulations of regulator molecules binding to the enzyme, we constructed a kinetic model of the enzyme-based signal transduction network. The kinetic model showed that two co-occuring neuromodulatory signals, a dopamine peak and an acetylcholine pause, are required to produce the second messenger and thus enable strengthening of corticostriatal synapses onto dSPNs, and that only the dopamine signal is not enough. Next, we developed a local, calcium- and reward-dependent learning rule based on what is known about the biochemical circuitry of corticostriatal synapses onto dSPNs. We show that with this biologically-based learning rule, single SPNs can learn to solve the nonlinear feature binding problem (NFBP), a computationally hard problem representing the class of linearly nonseparable tasks. This result suggests that different, unrelated or partially related stimuli that require executing the same action to obtain a goal, can use the same SPNs responsible for selecting that action, and that a single SPN can reliably distinguish between similar stimuli. The solution of the NFBP with the aforementioned learning rule relies on supralinear dendritic voltage elevations called plateau potentials. Experimentally, plateau potentials are all-or-none events, a property crucial for performing nonlinear computations required to solve the NFBP. However, computational models of plateau potentials often produce graded voltage elevations. We analyzed and compared existing plateau potential models, and found that long-lasting glutamate spillover in the extrasynaptic space robustly produces all-or-none plateau potentials by activating extrasynaptic N-methyl-D-aspartate (NMDA) glutamate receptors. This suggests that glutamate spillover may be a mechanism for generating all-or-none plateau potentials in vivo, as well. In summary, the findings presented in this thesis advance our understanding of the role of single dSPNs in goal-directed learning, the biophysical mechanisms involved in performing their nonlinear computations, and the neuromodulatory signals necessary to produce synaptic strengthening and thus implement goal-directed learning.

https://hdl.handle.net/1842/43136

http://dx.doi.org/10.7488/era/5678

en

The University of Edinburgh

Bruce, N. J.*, Narzi, D.*, Trpevski, D.*, van Keulen, S. C.*, Nair, A. G., Röthlisberger, U., Wade, R. C., Carloni, P., and Hellgren Kotaleski, J. (2019). “Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals”. PLOS Computational Biology 15(10):e1007382. doi: 10.1371/journal.pcbi.1007382

Khodadadi, Z., Trpevski, D., Lindroos, R., Hellgren Kotaleski, J. (2024) “Local, calcium- and reward-based synaptic learning rule that enhances dendritic nonlinearities can solve the nonlinear feature binding problem”. eLife 13:RP97274; doi: 10.7554/eLife.97274.1

Trpevski, D., Khodadadi, Z., Carannante, I., and Hellgren Kotaleski, J. (2023). “Glutamate spillover drives robust allor- none dendritic plateau potentials—an in silico investigation using models of striatal projection neurons”. Front. Cell. Neurosci. 17, 1196182. doi: 10.3389/fncel.2023.1196182

Santos, J. P. G., Pajo, K., Trpevski, D., Stepaniuk, A., Eriksson, O., Nair, A. G., Keller, D., Hellgren Kotaleski, J., Kramer, A. (2022). “A modular workflow for model building, analysis, and parameter estimation in systems biology and neuroscience”. Neuroinformatics 20: 241–259. doi: 10.1007/s12021-021-09546-3

van Keulen, S. C., Martin, J., Colizzi, F., Frezza, E., Trpevski, D., Cirauqui Diaz, N., Vidossich, P., Röthlisberger, U., Hellgren Kotaleski, J., Wade, R. C., Carloni, P. (2023). “Multiscale molecular simulations to investigate adenylyl cyclase-based signaling in the brain”. WIREs Computational Science 13(1): e1623. doi: 10.1002/wcms.1623

Trpevski, D.*, Khodadadi, Z.*, Hellgren Kotaleski, J. (2024). “An inhibitory plasticity rule for control of neuronal firing rate and dendritic compartmentalization”. Manuscript

synaptic plasticity

multicompartment neuron models

signal transduction

signaling networks

kinetic modeling

adenylyl cyclase

metaplasticity

reward learning

dopamine

feature binding problem

plateau potentials

glutamate spillover

dendritic nonlinearities

supralinear integration

synaptic clustering

learning rules

extrasynaptic NMDA receptors

Models of corticostriatal synaptic plasticity and plateau potentials in striatal projection neurons

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy