Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-03-01
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| Series: | Frontiers in Neural Circuits |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncir.2014.00012/full |
| _version_ | 1818055637921169408 |
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| author | Stefano eCavallari Stefano ePanzeri Stefano ePanzeri Alberto eMazzoni Alberto eMazzoni |
| author_facet | Stefano eCavallari Stefano ePanzeri Stefano ePanzeri Alberto eMazzoni Alberto eMazzoni |
| author_sort | Stefano eCavallari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T12:16:07Z |
| format | Article |
| id | doaj.art-3d2c25dd184f4a7689b755689a7b388b |
| institution | Directory Open Access Journal |
| issn | 1662-5110 |
| language | English |
| last_indexed | 2024-12-10T12:16:07Z |
| publishDate | 2014-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neural Circuits |
| spelling | doaj.art-3d2c25dd184f4a7689b755689a7b388b2022-12-22T01:49:13ZengFrontiers Media S.A.Frontiers in Neural Circuits1662-51102014-03-01810.3389/fncir.2014.0001274866Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapsesStefano eCavallari0Stefano ePanzeri1Stefano ePanzeri2Alberto eMazzoni3Alberto eMazzoni4Fondazione Istituto Italiano di TecnologiaFondazione Istituto Italiano di TecnologiaMax Planck Institute for Biological CyberneticsFondazione Istituto Italiano di TecnologiaScuola Superiore Sant'AnnaModels 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.http://journal.frontiersin.org/Journal/10.3389/fncir.2014.00012/fullrecurrent neural networkLocal Field PotentialsCorrelation analysisinformation encodingconductance based neuron modelsintegrate-and-fire neurons |
| spellingShingle | Stefano eCavallari Stefano ePanzeri Stefano ePanzeri Alberto eMazzoni Alberto eMazzoni Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses Frontiers in Neural Circuits recurrent neural network Local Field Potentials Correlation analysis information encoding conductance based neuron models integrate-and-fire neurons |
| title | Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses |
| title_full | Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses |
| title_fullStr | Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses |
| title_full_unstemmed | Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses |
| title_short | Comparison of the dynamics of neural interactions in integrate-and-fire networks with current-based and conductance-based synapses |
| title_sort | comparison of the dynamics of neural interactions in integrate and fire networks with current based and conductance based synapses |
| topic | recurrent neural network Local Field Potentials Correlation analysis information encoding conductance based neuron models integrate-and-fire neurons |
| url | http://journal.frontiersin.org/Journal/10.3389/fncir.2014.00012/full |
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