- Poster presentation
- Open Access
A maximum likelihood estimator of neural network synaptic weights
© Taouali and Cessac; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Maximum Likelihood Estimator
- Reverse Engineering
- Synaptic Weight
- Gibbs Distribution
- Collective Dynamic
The statistics of spikes in a neuronal network is constrained on one hand by the stimulus and shared noise, and on the other hand by neuron interactions and collective dynamics. The join spike statistics and its spatio-temporal correlations can be explicitly computed in conductance-based Integrate-and-Fire models [1, 2]. The probability distribution of spike is a Gibbs distribution (in its most general definition allowing to consider non-stationarity) which encompasses existing statistical models such as Maximum Entropy models or Generalized-Linear Models.
Moreover, the dependence of spike statistics in network parameters such as synaptic weights and stimulus is explicit.
This estimator is based on a plausible probabilistic model of spiking activity, and not a Poisson likelihood processing. So, it offers a flexible framework that should allow better statistical analysis of real data.
This work was supported by INRIA, ERC-NERVI number 227747, KEOPS ANR-CONICYT and European Union Project # FP7-269921 (BrainScales), Renvision grant agreement N 600847 and Mathemacs FP7-ICT_2011.9.7.
- Cessac B: Statistics of spike trains in conductance-based neural networks: Rigorous results. The journal of Mathematical Neuroscience. 2011, 1: 8-10.1186/2190-8567-1-8.View ArticlePubMedGoogle Scholar
- Cofré R, Cessac B: Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses, to appear in. Chaos, Solitons and Fractals. 2013Google Scholar
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