Integración y competencia sináptica en la sustancia negra pars reticulata: un análisis experimental e in silico

The substantia nigra pars reticulata (SNr) is a primary output for basal ganglia signaling. It plays an important role in the control of movement, integrating inputs from upstream structures in the basal ganglia, before sending organized projections to a range of targets in the midbrain, brainstem, and thalamus. Here, we present a detailed in silico model of the mouse SNr, including its major afferent inputs. The electrophysiological and morphological properties of SNr neurons are characterized in acute brain slices via whole cell patch-clamp recordings and morphological reconstruction. Using reconstructed morphologies, multicompartmental models of single neurons are instantiated within the NEURON simulation environment and populated with relevant modeled ion channels. Model parameters are optimized via an evolutionary algorithm, such that simulated neurons faithfully reproduce recorded electrophysiological behavior. Using the simulation infrastructure software Snudda, single neuron models are incorporated into a circuit-level model, where the sparse connectivity within the SNr is recreated. We simulate the mouse SNr at scale, featuring realistic volumes and neuronal density. The unique synaptic properties and activity patterns of different afferent sources are captured in silico. Born out of ex vivo data, our model reproduces in vivo firing patterns. Our simulations suggest that paradoxical activity increases in response to experimental inhibition can be explained by lateral connectivity. In addition, our model predicts the functional implications of characteristic short-term synaptic plasticity in the indirect pathway of the basal ganglia. The model can be extended to include additional inputs and be connected with existing models of upstream basal ganglia nuclei to further explore circuit dynamics.