|
|
|
| e-Pub |
Previous | 
Home
Section: New Results
Spike Train statistics
Statistical models for spike trains analysis in the retina.
Participant : Bruno Cessac.
Recent advances in multi-electrodes array acquisition have made it possible to record the activity of up to several hundreds of neurons at the same time and to register their collective activity (spike trains). For the retina, this opens up new perspectives in understanding how retinal structure and ganglion cells encode information about a visual scene and what is transmitted to the brain. Especially, two paradigms can be confronted: in the first one, ganglion cells encode information independently of each others; in the second one non linear dynamics and connectivity contribute to produce a population coding where spatio-temporal correlations, although weak, play a significant role in spike coding. Confronting these two paradigms can be done at an experimental and at a theoretical level. On experimental grounds, new methods to analyse the role of weak correlations in spike train statistics are required. On theoretical grounds, mathematical results have been established, in neuronal models, showing how non linear dynamics and connectivity contribute to produce a correlated spike response to stimuli. In the context of the ANR KEOPS project, we have been working on these two aspects and we present our main results.
This work is available as [33] .
Spectral dimension reduction on parametric models for spike train statistics
Participants : Cesar Ravello, Ruben Herzog, Bruno Cessac, Maria-Jose Escobar, Adrian Palacios.
It has been shown that the neurons of visual system present correlated activity in response to different stimuli. The role of these correlations is an unresolved subject. These correlations vary according to the stimulus, specially with natural images. To uncover the role of these correlation and characterize the population code, it is necessary to measure the simultaneous activity of large neural populations. This has been achieved thanks to the advent of Multi-Electrode Array technology, opening up a way to better characterize how the brain encodes information in the concerted activity of neurons. In parallel, powerful statistical tools have been developed to accurately characterize spatio-temporal correlations between neurons. Methods based on Maximum Entropy Principle, where statistical entropy is maximized under a set of constraints corresponding to specific assumptions on the relevant statistical quantities, have been proved successfully, specially when they consider spatiotemporal correlations. They are although limited by (i) the assumption of stationarity, (ii) the many possible choice of constraints, and (iii) the huge number of free parameters. We present our results on these two aspects obtained in the context of ANR KEOPS .
This work is available as [54] .