Convergence regulates synchronization-dependent AP transfer in feedforward NNs (Sailamul et al 2017)

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We study how synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. We implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.
1 . Sailamul P, Jang J, Paik SB (2017) Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks. J Comput Neurosci 43:189-202 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Synapse;
Brain Region(s)/Organism:
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s): I Sodium; I Potassium; I T low threshold; I Cl, leak;
Gap Junctions:
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Synchronization; Oscillations; Action Potentials; Activity Patterns; Information transfer; Synaptic Convergence;
Implementer(s): Sailamul, Pachaya [pachaya_sailamul at]; Jang, Jaeson ; Paik, Se-Bum ;
Search NeuronDB for information about:  I T low threshold; I Sodium; I Potassium; I Cl, leak;
//////////////////* Parameters Setting *////////////////// 

/////////// General /////////// 
RESOLUTION = 1 // ( Plotted points per ms ) specified in runtime
NtwrkSIZE = 1500 // V1 size = 1500um by 1500um ---> may read from input
V1SIZE = NtwrkSIZE  
/////////// Cells Template /////////// 
// These parameters cannot put outside the template. Therefore, the values here are for reference only.
// Check the template file for the exact value
/* SOMA_SIZE = 25 //[um], for L and diam
//For Hodgkin-Huxley model
GNABAR_HH = 0.12 //gnabar_hh, [mho/cm2](mho = ohm^-1)  Maximum specific sodium channel conductance
GKBAR_HH = 0.036 //gkbar_hh, [mho/cm2]   Maximum potassium channel conductance
GL_HH = 50e-06 //gl_hh, [mho/cm2]  Leakage conductance
EL_HH = -70 //el_hh , [mV]       Leakage reversal potential
//Membrane potential Threshold = -55
NETCON_THRESHOLD = 0 //nc.threshold --> When the source variable passes threshold in the positive direction at time t-delay, the target will receive an event at time t along with weight information. 
EPSP_TAU1 = 1 //[ms                                                      ]
EPSP_TAU2 = 3 //[ms]
IPSP_TAU1 = 1 //[ms]
IPSP_TAU2 = 7 //[ms]
IPSP_I_REVERSE = -80 //[mV]
/////////// Network specification interface /////////// 
PROP_SPEED_I = 1000 //[um/ms] speed of axonal propagation < 1 m/s >
PROP_SPEED_E = 100  //[um/ms] speed of axonal propagation < 0.1 m/s >

/////////// Declare Position /////////// 

/////////// Network Simulation and Recorded /////////// 

///////////  Network Generation /////////// 

/////////// Local Connection /////////// 

RANGE_E = 200 //[um] Estimate coupling distance (Ref. McLaughlin, 2000)
RANGE_I = 100 //[um] Estimate coupling distance (Ref. McLaughlin, 2000)
// Connection Probability
PMAX_E = 0.85  //Reid & Alonso 1995
PMAX_I = 0.85  //Reid & Alonso 1995

printf("Done Setting Parameters")

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