QUANTIFYING SENSORY INFORMATION IN CONTINUOUS BRAIN SIGNALS

Abstract

How is information processed in the brain? This is one of the main and most challengingquestions in Neuroscience. The established hypothesis is that information isencoded in the temporal dynamics of spikes. However, there is growing evidence thatcontinuous signals such as Local Field Potentials (LFP) can play an important rolein coding neural information. Recently, Montemurro et al. [2008] reported that thephase-of-firing code, a mechanism previously observed in the hippocampus, is used inthe sensory cortices for information encoding. In the phase-of-firing code, the neuronscommunicate spikes with respect to the phase of continuous signals producedby population activity, such as the LFP. Using information-theoretic measures, it wasshown that when the timing of spikes was measured with respect to the phase ofthe LFP, an extra amount of sensory information was revealed in the responses thatwas not available from the spike codes alone. On the one hand, it still remains tobe established how widespread this novel coding mechanism is. So far it has beenverified in a few sensory modalities and it is not clear whether it is a universal codingmechanism. On the other, the estimation of information from continuous signalsposes serious challenges from a technical point of view. The main reason is thataccurate estimations of information measures require unrealistic amount of experimentaldata, mostly due to the presence of correlated activity. When these measuresare applied to assess the information content in continuous responses, they lead tosevere biases in the results, which can affect the conclusions regarding the validity ofspecific neural codes. The main goal of this Thesis is to explore the universality of thephase-of-firing code by studying it in novel systems, establish the origin of this code,and to develop more effcient numerical methods to accurately quantify information encoded in continuous brain signals. In particular, in this Thesis we investigate therole of continuous signals in sensory modalities where it has not been explored sofar. We verified the presence of a phase-of-firing code in both the somatosensorycortex of the rat, and the visual thalamus of mice, thus giving support to the possibleuniversality of this coding mechanism. While the phase-of-firing code found inthese systems shares common features with those found in previous studies, we alsocharacterised important differences. In the rat whisker system it was found that highfrequency bands of the LFP play a more prominent role than what observed in thevisual and auditory cortices of monkeys. This is compatible with the behavioural andmechanical constraints of this system, which require a high discrimination of finelystructured temporal information in the stimulus. In the case of the visual thalamus ofmice, we found that the phase-of-firing code contributes significantly to the encodingof irradiance information conveyed by melanopsin photoreceptors in the retina. Wealso investigated the source of the phase-of-firing codes in cortex by modelling therelationship between population spikes and LFP. In particular, we studied the interplaybetween the effective spatial integration of information resulting from populationactivity and the temporal memory imprinted in the LFP as a consequence of filteringmechanisms in the neural tissue. We found that most of the information in theLFP comes from a neural neighbourhood of a radius of about 150-350 Um, and atemporal history of 200-300 msec. Finally, we developed novel practical methods forquantifying the information content of continuous signals in the brain, which yieldaccurate results under realistic experimental conditions. These methods are based onthe projection of the statistics of the response space into a lower dimensional manifold.In particular, we modelled continuous neural responses as a hierarchy of Markovmodels of increasing order, and found that the structure of temporal dependencies ofreal LFP can be captured b

Date of Award	1 Aug 2014
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Enrico Bracci (Supervisor) & Marcelo Montemurro (Supervisor)