Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses

Frontiers in Neural Circuits

卷:8

Stefano eCavallari¹  Stefano ePanzeri²  Alberto eMazzoni³ 

[1] Fondazione Istituto Italiano di Tecnologia;

[2] Max Planck Institute for Biological Cybernetics;

[3] Scuola Superiore Sant'Anna;

关键词: recurrent neural network;    Local Field Potentials;    Correlation analysis;    information encoding;    conductance based neuron models;    integrate-and-fire neurons;   

DOI  :  10.3389/fncir.2014.00012

来源: DOAJ

【 摘 要 】

Models of networks of Leaky Integrate-and-Fire neurons (LIF) are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single-neuron and neural population dynamics of conductance-based networks (COBN) and current-based networks (CUBN) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-sensitive in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, COBN showed stronger neuronal population synchronization in the gamma band, and their spectral information about the network input was higher and spread over a broader range of frequencies. These results suggest that second order properties of network dynamics depend strongly on the choice of synaptic model.

【 授权许可】

Unknown