A multiscale computational approach to replicate the full-scale mouse striatum model

The present work aims to reproduce and replicate the results of a prominent study in the computational neuroscience field. The objectives of this reproduction are verifying the original claims and results and assessing the research's degree of replicability. In the original study, the authors presented a detailed neural network model of the striatum, a nucleus in the subcortical basal ganglia of the forebrain. The methodology employed for the replication focuses on two scopes: the influence of network size and model complexity on the observed dynamics in the neural circuit. Different combinations of scale and complexity reduction have been performed and compared. The results show similar qualitative behavior, considering the limitations and simplifications adopted. Reducing the size primarily impacted the overall network mean activity and firing rates while simplifying the single-neuron models affected the network responses to cortico-thalamic inputs. Lastly, some recommendations are made for future works: improvements that can be implemented on the replicated models and evaluation and feedback on the documentation and accessibility of the original research.