BioNet: A Python interface to NEURON for modeling large-scale networks
Fig 5
Application example: Model of the layer 4 in mouse V1.
(A) The in silico study [9] mimicked in vivo visual physiology experiments (bottom), where a mouse watches visual stimuli such as, e.g., drifting gratings, while the activity of neurons in its cortex are recorded. (Center) The top view of the cortical surface, with boundaries of cortical areas delineated (VISp is V1). The inner boundary encloses part of the tissue that was modeled using biophysically detailed cells, whereas the tissue between the inner and outer circles was modeled using the simplified LIF cells. (Top) The 3D visualization of the layer 4 model (only 10% of cells are shown for clarity). (B) Example of synaptic innervation of the biophysically detailed cell models of each type. Synapses (depicted as spheres) are color coded according to their source cell type. (C) Rastergrams of the external inputs: (Top) “background” input (BKG, khaki) that switches between “ON” to “OFF” states, loosely representing different brain states; (Bottom) LGN input (green) corresponding to the visual response to 0.5 second gray screen (gray line) followed by 2.5 second drifting grating (black line). (D) The connection matrix showing the peak conductance strength for connections between each pair of cell types. (E) Simulation output: (Top) spike raster in the biophysical “core”. The node_ids are ordered such that cells with similar ids have similar preferred orientation angle. In this example, cells preferring ~0, ~180, and ~360 degrees are responding strongly to a horizontal drifting grating. (Bottom) somatic voltage traces and the corresponding calcium traces for example excitatory (red) and inhibitory (blue) cells.