Degenerate neural circuits perform the same function despite being structurally different. However, it is unclear whether neural circuits with interacting neuromodulator sources can themselves be degenerate while maintaining the same neuromodulatory function. Here, we address this by computationally modelling the neural circuits of neuromodulators serotonin and dopamine, local glutamatergic and GABAergic interneurons, and their possible interactions, under reward/punishment-based conditioning tasks. The neural modelling is constrained by relevant experimental studies of the VTA or DRN system using e.g. electrophysiology, optogenetics, and voltammetry. We first show that a single parsimonious, sparsely connected neural circuit model can recapitulate several separate experimental findings that indicated diverse, heterogeneous, distributed and mixed DRN-VTA neuronal signalling in reward and punishment tasks. The inability for this model to recapitulate all observed neuronal signalling suggests potentially multiple circuits acting in parallel. Then using computational simulations and dynamical systems analysis, we demonstrate that several different stable circuit architectures can produce the same observed network activity profile, hence demonstrating degeneracy. Due to the extensive D2-mediated connections in the investigated circuits, we simulate D2 receptor agonist by increasing the connection strengths emanating from the VTA DA neurons. We found that the simulated D2 agonist can distinguish among sub-groups of the degenerate neural circuits based on substantial deviations in specific neural populations’ activities in reward and punishment conditions. This forms a testable model prediction using pharmacological means. Overall, this theoretical work suggests the plausibility of degeneracy within neuromodulator circuitry and has important implication for the stable and robust maintenance of neuromodulatory functions.
|Journal||Frontiers in Computational Neurosience|
|Publication status||Accepted/In press - 30 Dec 2022|
- computational modelling
- reward and punishment