The provided code models aspects of computational neuroscience related to synaptic connectivity and neural activity patterns within the cerebellar microcircuitry. The focus is on granule cells (GCs) and their interactions with mossy fibers (MFs). This model is particularly concerned with understanding how input correlations and synaptic connections affect neuronal activity and learning, concepts critical to cerebellar function.
Granule Cells and Mossy Fibers:
Synaptic Connectivity:
N_syn
represents the number of synaptic inputs granule cells receive from mossy fibers, simulating variations in synaptic connectivity. The range reflects the known variability in synaptic connections providing inputs to granule cells.Input Correlation (sigma
):
sigma
, which signifies the spatial scale or radius of correlated input activity, a critical aspect since neurons often receive input with varying degrees of correlation, impacting neural coding and network behavior.Activity Patterns and Variability:
part_shuffle
functions). It looks at how the input from mossy fibers can lead to different activity outcomes in granule cells, simulating biological variability in neural response.Population Statistics:
Learning and Adaptation:
The model, therefore, provides insights into the functional implications of synaptic connectivity and input correlation on cerebellar processing, capturing elements of the cerebellum's role in sensorimotor integration and learning.