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:     ang_dist

Arguments:    th1 - scalar angle, or vector of angles (in degrees)
              th2 - scalar angle, or vector of angles (in degrees)
           
Output:       * If th1, th2 are both scalars, returns the angular distance between them
              * If one of th1 or th2 is a vector, and the other a scalar, returns a vector with the distances between
                each entry of the vector of angles and the scalar angle.
              * If th1, th2 are both vectors of equal length, then returns the vector of distances between each
                pair of angles th1[i], th2[i]
           
Description: Computes angular distances in degrees

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

import numpy as np

def ang_dist(th1,th2):
    return np.min(np.mod([np.array(th1)-np.array(th2),np.array(th2)-np.array(th1)],360),0)

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