Vertical System (VS) tangential cells network model (Trousdale et al. 2014)

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Accession:155727
Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength.
Reference:
1 . Trousdale J, Carroll SR, Gabbiani F, Josic K (2014) Near-optimal decoding of transient stimuli from coupled neuronal subpopulations. J Neurosci 34:12206-22 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon; Synapse; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Fly vertical system tangential cell;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s): Nicotinic; GabaA;
Gene(s):
Transmitter(s): Acetylcholine; Gaba;
Simulation Environment: Python;
Model Concept(s): Activity Patterns; Spatio-temporal Activity Patterns; Simplified Models; Invertebrate; Connectivity matrix;
Implementer(s): Gabbiani, F; Trousdale, James [jamest212 at gmail.com];
Search NeuronDB for information about:  Nicotinic; GabaA; Acetylcholine; Gaba;
'''
Function:     my_2d_hist

Arguments:    x - Length N list of horizontal coordinates of points
              y - Length M list of vertical coordinates of points
              x_edges - Length H+1 list of horizontal bin edges
              y_edges - Length V+1 list of vertical bin edges
           
Output:       A size VxH binary array, with a 1 in an entry indicating that a point in the list of ordered pairs (x,y)
              fell into the corresponding pair of horizontal and vertical bins.
           
Description:  This program is specialized to not count the number of points which lie in each bin, but instead just
              detects whether at least one point lies in each bin. Used when generating bar images.

Authors:      James Trousdale - jamest212@gmail.com
'''

__all__ = ["my_2d_hist",]

import numpy as np

def my_2d_hist(x,y,x_edges,y_edges):

    out = np.zeros((len(y_edges)-1,len(x_edges)-1))

    x_inds = np.mod(np.int64(np.floor((x-x_edges[0])/(x_edges[1]-x_edges[0]))),360)
    y_inds = np.mod(np.int64(np.floor((y-y_edges[0])/(y_edges[1]-y_edges[0]))),180)


    
    out[y_inds,x_inds] = 1
        
    return out



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