An information-theoretic study of neuronal spike correlations in the mammalian cerebral cortex (PhD Thesis in Computational Neuroscience)

FERNANDO MONTANI

Imperial College London, UK

Lugar: London; Año: 2008 p. 209

We have used information theory to examine whether stimulus-dependent correlation could contribute to the neural coding of orientation and contrast by pairs of V1 cells. To this end, we have used a modified version of the method of information components. This analysis revealed that although synchrony is prevalent and informative, the additional information it provides is frequently offset by the redundancy arising from the similar tuning properties of the two cells. Thus, coding is roughly independent with weak synergy or redundancy arising depending on the similarity in tuning and the temporal precision of the analysis. Our findings suggest that this would allow cortical circuits to enjoy the stability provided by having similarly tuned neurons without suffering the penalty of redundancy as the associated information transmission deficit is compensated by stimulus dependent synchrony.  The underlying origins of synchronized firing between cortical neurons are still under discussion. GABAergic inhibitory neurons may be involved in the generation of oscillatory activity in the cortex and its synchronization. Specifically, reduction of GABAergic inhibition may favour cortical plasticity producing functional recovery following focal brain lesions. We present a computational and analytical model of a topographically mapped population code which includes a focal lesion as well as a process for receptive field enlargement. Our finding shows that by tuning the receptive field plasticity to a certain value, the information transfer through the cortex after stroke can be optimized. A widespread distribution of neuronal activity can generate higher-order stochastic interactions. In this case, pair-wise correlations do not uniquely determine synchronizing spiking in a population of neurons, and higher order interactions across neurons cannot be disregarded. We present a new statistical approach, using the information geometry framework, for analyzing the probability distribution function (PDF) of spike firing patterns by considering higher order correlations in a neuronal pool. We have studied the limit of a large population of neurons and associated a deformation parameter to the higher order correlations in the PDF. We have also performed an analytical estimation of the Fisher information in order to evaluate the implications of higher order correlations between spikes on information transmission. This leads to a new procedure to study higher order stochastic interactions. The overall findings of this thesis warn about making any extensive statement about the role of neuronal spike correlations without considering the case of higher order correlations.