ANNarchy 4.8.2
  • ANNarchy
  • Installation
  • Tutorial
  • Manual
  • Notebooks
  • Reference

Gap Junctions

  • List of notebooks
  • Rate-coded networks
    • Echo-state networks
    • Neural field
    • Bar Learning
    • Miconi network
    • Structural plasticity
  • Spiking networks
    • AdEx
    • PyNN/Brian
    • Izhikevich
    • Synaptic transmission
    • Gap junctions
    • Hodgkin-Huxley
    • COBA/CUBA
    • STP
    • STDP I
    • STDP II
    • Homeostatic STDP - Ramp
    • Homeostatic STDP - SORF
  • Advanced features
    • Hybrid networks
    • Parallel run
    • Bayesian optimization
  • Extensions
    • Image
    • Tensorboard
    • BOLD monitor I
    • BOLD monitor II
    • ANN to SNN I
    • ANN to SNN II

Gap Junctions

Download JupyterNotebook Download JupyterNotebook

#!pip install ANNarchy

A simple network with gap junctions.

This is a reimplementation of the Brian example:

http://brian2.readthedocs.org/en/2.0b3/examples/synapses.gapjunctions.html

import numpy as np
import ANNarchy as ann

ann.clear()
ann.setup(dt=0.1)

neuron = ann.Neuron(
    parameters = "v0 = 1.05: population; tau = 10.0: population",
    equations = "tau*dv/dt = v0 - v + g_gap",
    spike = "v >  1.",
    reset = "v = 0."
)

gap_junction = ann.Synapse(
    psp = "w * (pre.v - post.v)"
)

pop = ann.Population(10, neuron)
pop.v = np.linspace(0., 1., 10)

proj = ann.Projection(pop, pop, 'gap', gap_junction)
proj.connect_all_to_all(0.02)

trace = ann.Monitor(pop[0] + pop[5], 'v')

ann.compile()

ann.simulate(500.)

data = trace.get('v')

import matplotlib.pyplot as plt

plt.figure(figsize=(15, 10))
plt.plot(data[:, 0])
plt.plot(data[:, 1])
plt.xlabel('Time (ms)')
plt.ylabel('v')
plt.show()
ANNarchy 4.8 (4.8.2) on darwin (posix).
Compiling ...  OK 

Synaptic transmission
Hodgkin-Huxley
 

Copyright Julien Vitay, Helge Ülo Dinkelbach, Fred Hamker