Gap junction plasticity as a mechanism to regulate network-wide oscillations (Pernelle et al 2018)

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"Oscillations of neural activity emerge when many neurons repeatedly activate together and are observed in many brain regions, particularly during sleep and attention. Their functional role is still debated, but could be associated with normal cognitive processes such as memory formation or with pathologies such as schizophrenia and autism. Powerful oscillations are also a hallmark of epileptic seizures. Therefore, we wondered what mechanism could regulate oscillations. A type of neuronal coupling, called gap junctions, has been shown to promote synchronization between inhibitory neurons. Computational models show that when gap junctions are strong, neurons synchronize together. Moreover recent investigations show that the gap junction coupling strength is not static but plastic and dependent on the firing properties of the neurons. Thus, we developed a model of gap junction plasticity in a network of inhibitory and excitatory neurons. We show that gap junction plasticity can maintain the right amount of oscillations to prevent pathologies from emerging. Finally, we show that gap junction plasticity serves an additional functional role and allows for efficient and robust information transfer."
1 . Pernelle G, Nicola W, Clopath C (2018) Gap junction plasticity as a mechanism to regulate network-wide oscillations. PLoS Comput Biol 14:e1006025 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract Izhikevich neuron; Abstract integrate-and-fire leaky neuron;
Gap Junctions: Gap junctions;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: Python;
Model Concept(s): Gamma oscillations;
Implementer(s): Pernelle, Guillaume [g.pernelle14 at];
Search NeuronDB for information about:  Gaba; Glutamate;
# coding: utf-8

# In[1]:

get_ipython().magic('matplotlib inline')
from fns import *
from fns.functionsTF import *

# In[2]:

params = []
res = []
config = load_config()
glist = [1,5]
for g in glist:
    # number of iterations to run (dt = 0.1ms, T=2000 -> d=200 ms)
    T = 2000
    # initialize the model
    gpu = TfConnEvolveNet(config=config, T=T)
    # number of excitatory neurons
    # number of inhibitory neurons
    # mean external drive = 120
    # choose on which hardware to run the simulation
    gpu.device = '/cpu:0' #'/gpu:0'

    # mean gap junction coupling
    gpu.g1 = g
    # when to start plasticity 
    gpu.stabTime = np.inf # static network
    # when to stop plasticity
    gpu.stopTime = np.inf
    # save the spikes
    gpu.spikeMonitor = True
    # save the individual voltages, currents, etc.
    gpu.monitor_single = True

    # run the simulation
    # release memory
    del gpu  

# ## Raster Plots

# In[3]:

fig = plt.figure(figsize=(5,4))

glist = [1,5]
for i in range(2):
    ax = fig.add_subplot(2, 1, i+1)
    raster = res[i].raster
    x,y = np.where(raster[-1000:,700:800]!=0)
    ax.scatter(x+100,y, marker='.', s=10, color='r')
    x,y = np.where(raster[-1000:,800:900]!=0)
    ax.scatter(x+100,y+110, marker='.', s=10, color='b')
    ax.text(20, 150, 'I', fontweight='bold', fontsize=20 )
    ax.text(20, 50, 'E', fontweight='bold', fontsize=20 )
plt.xlabel('Time [100 ms]')

# ## Voltage Traces

# In[4]:

Icolor = '#3366cc'
Ecolor = '#FF6868'
fig = plt.figure(figsize=(5,4))
for i in range(2):
    gpu = res[i]
    ax2 = fig.add_subplot(111)
    # get voltage from the 40th inhibitory neuron
    ax2.plot(gpu.vAll[840][-1000:] - i*200, c=Icolor)
    # get the mean voltage
    ax2.plot(gpu.vmI1[-1000:]/200 - 40 - i*200, c=Icolor, alpha=0.5)
plt.yticks([-20,-220], [r'$\gamma = $%d' % glist[0], r'$\gamma = $%d' % glist[1]])
plt.xlabel('Time [100 ms]')

# In[5]:


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