Models of Corticostriatal Synaptic Plasticity and Plateau Potentials in Striatal Projection Neurons

QC 20240904

Abstract

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 out-come of an action. The synapses from cortical neurons on to 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 ofthe 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 corticostriatalsynapses 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, canuse 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 relieson 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.

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