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]:

config = load_config()

res = []
T = 5000

# run two simulations, first without shared gap junctions, then with 40 shared gap junctions
for sG in [0,40]:
    gpu = TfConnEvolveNet(config=config, T=T)

    # number of cross-network gap junctions
    gpu.sG = sG

    NE = 800
    NI = 200
    ## input network
    # number of excitatory neurons
    # number of inhibitory neurons

    ## gamma network
    # number of excitatory neurons
    # number of inhibitory neurons

    ### input to the input-network
    seed = 0
    # input amplitude
    A = 400
    x = generateInput2(seed, T, tau=100)[np.newaxis, :]
    w0 = np.random.rand(1, 2*(NE+NI)) * 2
    w0 = w0 * A
    w0 = w0 * np.concatenate([np.ones((1, NE+NI)), np.zeros((1, NE+NI))], axis=1)
    INP = w0.T @ x + 200

    ### small constant drive to the output-network
    # k = np.ones((1, T)) * 200
    # w1 = np.concatenate([np.zeros((1, NE+NI)), np.ones((1, NE+NI))], axis=1)
    # INP2 = w1.T @ k

    # feed input to network
    gpu.input = INP 

    # choose hardware
    gpu.device = '/gpu:0'

    # mean initial gap junction coupling
    gpu.g1 = 5.5
    gpu.g2 = 5.5

    # do not save the spikes
    gpu.spikeMonitor = False
    # do not save the individual voltages, currents, etc.
    gpu.monitor_single = False

    # iteration 
    gpu.stabTime = np.inf

    # rule: g0 = 0 for no bound rule, g0 = 10 for softbound rule
    gpu.g0 = 10


    del gpu

# ## Reconstruction input

# In[3]:

s = 0
e = T

for i in range(2):
    # input signal to input-network
    inp = res[i].input[0,s:e]/np.max(res[i].input[0,s:e])
    # population activity of inhibitory neurons of the output-network
    ifr = res[i].vvmI2[s:e] 

    spikes, xdec, ydec, corr_predict = decode(inp, ifr )
    # rescale signals for plotting
    inp -= np.min(inp)
    inp /= np.max(inp)
    ydec -= np.min(ydec)
    ydec /= np.max(ydec)
    # plot input
    plt.plot(xdec, inp, color='r', label='input')
    # plot decoded input
    plt.plot(xdec,ydec, color=snCol, label='decoded input')
    plt.legend(fontsize=18, loc='best', handlelength=1)
    plt.xlabel('Time [%d ms]'%int((e-s)*res[i].dt))
    plt.title('Input vs Decoded Input')
    if i==0:
        plt.suptitle(r'\textbf{No cross-network GJs}', y=1.05, fontsize=22)
        plt.suptitle(r'\textbf{40 cross-network GJs}', y=1.05, fontsize=22)


# In[ ]: