- Poster presentation
- Open Access
Does noise shift or delete spikes?
© Voronenko and Lindner; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Poisson Process
- Spike Train
- Neuron Model
- Single Neuron
- Extensive Simulation
Stochastic leaky integrate-and-fire neurons are widely used to study properties of neural networks (e.g. ) as well as the spontaneous activity  and signal transmission  of single neurons. They have also been employed in studies of the 'common-noise' problem, i.e. the question of how correlated input to two cells causes output spike train correlations of these neurons .
In the cortex, input correlations seem to be weak  (in terms of the above sketch, c<<1), however, in the sensory periphery with a strong time-dependent stimulus the situation can be completely different (e.g.). The two neurons receive the same strong stimulus (often modeled as a random signal) and each neuron is subject to a small amount of intrinsic noise (1-c<<1). Without the intrinsic noise two identical neurons would fire in complete synchrony. How does the weak noise change the spikes?
This work was funded by the BMBF (FKZ: 01GQ1001A).
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