Our sensory experiences are encoded in the patterns of activity of the neurons in our brain. While we know we are capable of sensing and responding to a constantly changing sensory environment, we often study neural activity by repeatedly presenting the same stimulus and analyzing the average neural response. It is not understood how the average neural response represents the dynamic neural activity that produces our perceptions. In this work, we use functional imaging of the rodent primary somatosensory cortex, specifically the whisker representations, and apply classic signal-detection methods to test the predictive power of the average neural response. Stimulus features such as intensity are thought to be perceptually separable from the average representation; however, we show that stimulus intensity cannot be reliably decoded from neural activity from only a single experience. Instead, stimulus intensity was encoded only across many experiences. We observed this probabilistic neural code in multiple classic sensory paradigms including complex temporal stimuli (pairs of whisker deflections) and multi-whisker stimuli. These data suggest a novel framework for the encoding of stimulus features in the presence of high-neural variability. Specifically we suggest that our brains can compensate for unreliability by encoding information redundantly across cortical space. This thesis predicts that a somatosensory stimulus is not encoded identically each time it is experienced; instead, our brains use multiple redundant pathways to create a reliable sensory percept.

Thesis (Ph. D.)--Drexel University, 2004. / Includes abstract and vita. Includes bibliographical references (leaves 113-130).

Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references (leaves 113-121).

The hippocampus is a brain structure involved in episodic memory and spatial cognition. Neuronal activity within the hippocampus exhibits intricate temporal patterning, including oscillatory and sequential dynamics, which are believed to underlie these cognitive processes. In individual cells, a temporal activity pattern called phase precession occurs which leads to the organisation of neuronal populations into sequences. These sequences are hypothesised to form a substrate for episodic memory and the representation of spatial trajectories during navigation. In this thesis, I present a novel theory of the phenomenological properties of these neuronal activity sequences. In particular, I propose that the sequential organisation of population activity is governed by the independent phase precession of each cell. By comparison of models in which cells are independent and models in which cells exhibit coordinated activity against experimental data, I provide empirical evidence to support this hypothesis. Further, I show how independent coding affords a vast capacity for the generation of sequential activity patterns across distinct environments, allowing the representation of episodes and spatial experiences across a large number of contexts. This theory is then extended to account for grid cells, whose activity patterns form a hexagonal lattice over external space. By analysing simple forms of phase coding in populations of grid cells, I show how previously undocumented constraints on phase coding in two dimensional environments are imposed by the symmetries of grid cell firing fields. To overcome these constraints, I propose a more complex phenomenological model which can account for phase precession in both place cells and grid cells in two dimensional environments. Using insights from this theory, I then propose a biophysical circuit mechanism for hippocampal sequences. I show that this biophysical circuit model can account for the proposed phenomenological coding properties and provide experimentally testable predictions which can distinguish this model from existing models of phase precession. Finally, I outline a scheme by which this biophysical mechanism can implement supervised learning using spike time dependent plasticity in order to learn associations between events occurring on behavioural timescales. The models presented in this thesis challenge previous theories of hippocampal circuit function and suggest a much higher degree of flexibility and capacity for the generation of sequences than previously believed. This flexibility may underlie our ability to represent spatial experiences and store episodic memories across a seemingly unlimited number of distinct contexts.

Entender como o cérebro processa informação é um dos problemas mais fascinantes da ciência de nossos dias. Para resolvê-lo, é fundamental estudarmos os mecanismos de representação e transmissão de informação de um único neurônio, a unidade fundamental de processamento do cérebro. Grande parte dos neurônios representa a informação por seqüências de pulsos elétricos, ou potenciais de ação. Nós usamos a mosca como modelo para estudar como a arborização dendrítica do neurônio influencia a quantidade de informação transmitida pela estrutura temporal da seqüência de pulsos. O mapeamento retinotópico da informação no sistema visual da mosca permite que as arborizações dendríticas de certos neurônios sejam estimuladas localmente através da região que corresponde a essa localização no campo visual. Nós apresentamos imagens em movimento em várias regiões do campo visual da mosca e medimos a resposta do neurônio H1, sensível a movimentos horizontais. Usando a teoria da informação, calculamos a quantidade de informação transmitida para cada uma dessas regiões do campo visual e a relacionamos com outras propriedades do neurônio, como por exemplo a sensibilidade espacial e eficiência. Nossos resultados sugerem que a arborização dendrítica influencia a codificação temporal de maneira significativa, indicando que o neurônio pode usar a estrutura temporal da sequência de pulsos para codificar outros parâmetros do estímulo, ou para aumentar a confiabilidade da codificação dependendo da região excitada / Understanding how the brain processes information about the outside world is one of the most fascinating problems of modern science. This involves the analysis of information representation and transmission in the fundamental processing element of the brain - the neuron. In the cortex neurons represent information by sequences of electrical pulses, or spikes. We use the fly as a model to study how the amount of information transmitted by the temporal structure of the spike trains depends on the neuron\'s dendritic arborization. The retinotopic mapping of information in the fly\'s visual system allows the stimulation of specific regions of the neuron\'s dendritic tree through the visual stimulation of the respective region in the visual field of the fly. We show an image moving in the preferred direction of the motion-sensitive neuron H1 in specific regions of the fly\'s visual field and measure the electrical response of the neuron. Using information theory, we calculate the amount of information transmitted for each of these regions and compare it with other properties of the neuron, for example, the spatial sensitivity. Our results suggest that the dendritic arborization influentes the temporal coding in a significant way, indicating that the neuron could use the temporal structure of the spike train to codify other parameters of the stimulus, or to increase the reliability of the code depending on the excited region

How is information processed in the brain? This is one of the main and most challenging questions in Neuroscience. The established hypothesis is that information is encoded in the temporal dynamics of spikes. However, there is growing evidence that continuous signals such as Local Field Potentials (LFP) can play an important role in coding neural information. Recently, Montemurro et al. [2008] reported that the phase-of-firing code, a mechanism previously observed in the hippocampus, is used in the sensory cortices for information encoding. In the phase-of-firing code, the neurons communicate spikes with respect to the phase of continuous signals produced by population activity, such as the LFP. Using information-theoretic measures, it was shown that when the timing of spikes was measured with respect to the phase of the LFP, an extra amount of sensory information was revealed in the responses that was not available from the spike codes alone. On the one hand, it still remains to be established how widespread this novel coding mechanism is. So far it has been verified in a few sensory modalities and it is not clear whether it is a universal coding mechanism. On the other, the estimation of information from continuous signals poses serious challenges from a technical point of view. The main reason is that accurate estimations of information measures require unrealistic amount of experimental data, mostly due to the presence of correlated activity. When these measures are applied to assess the information content in continuous responses, they lead to severe biases in the results, which can affect the conclusions regarding the validity of specific neural codes. The main goal of this Thesis is to explore the universality of the phase-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 the role of continuous signals in sensory modalities where it has not been explored so far. We verified the presence of a phase-of-firing code in both the somatosensory cortex of the rat, and the visual thalamus of mice, thus giving support to the possible universality of this coding mechanism. While the phase-of-firing code found in these systems shares common features with those found in previous studies, we also characterised important differences. In the rat whisker system it was found that high frequency bands of the LFP play a more prominent role than that observed in the visual and auditory cortices of monkeys. This is compatible with the behavioural and mechanical constraints of this system, which require a high discrimination of finely structured temporal information in the stimulus. In the case of the visual thalamus of mice, we found that the phase-of-firing code contributes significantly to the encoding of irradiance information conveyed by melanopsin photoreceptors in the retina. We also investigated the source of the phase-of-firing codes in cortex by modelling the relationship between population spikes and LFP. In particular, we studied the interplay between the effective spatial integration of information resulting from population activity and the temporal memory imprinted in the LFP as a consequence of filtering mechanisms in the neural tissue. We found that most of the information in the LFP comes from a neural neighbourhood of a radius of about 150-350 μm, and a temporal history of 200-300 msec. Finally, we developed novel practical methods for quantifying the information content of continuous signals in the brain, which yield accurate results under realistic experimental conditions. These methods are based on the projection of the statistics of the response space into a lower dimensional manifold. In particular, we modelled continuous neural responses as a hierarchy of Markov models of increasing order, and found that the structure of temporal dependencies of real LFP can be captured by the lowest orders. This helped us put a new light on the previous studies regarding the phase-of-firing code. Altogether, these results contribute an advance both at the level of understanding information coding strategies combining spike and continuous signals, and the required computational methods to quantify accurately information in experimental neural responses.

Most of the work investigating the representation of concrete nouns in the brain has focused on the locations that code the information. We present a model to study the contributions of perceptual and semantic features to the neural code representing concepts over time and space. The model is evaluated using magnetoencephalography data from different paradigms and not only corroborates previous findings regarding a distributed code, but provides further details about how the encoding of different subcomponents varies in the space-time spectrum. The model also successfully generalizes to novel concepts that it has never seen during training, which argues for the combination of specific properties in forming the meaning of concrete nouns in the brain. The results across paradigms are in agreement when the main differences among the experiments (namely, the number of repetitions of the stimulus, the task the subjects performed, and the type of stimulus provided) were taken into consideration. More specifically, these results suggest that features specific to the physical properties of the stimuli, such as word length and right-diagonalness, are encoded in posterior regions of the brain in the first hundreds of milliseconds after stimulus onset. Then, properties inherent to the nouns, such as is it alive? and can you pick it up?, are represented in the signal starting at about 250 ms, focusing on more anterior parts of the cortex. The code for these different features was found to be distributed over time and space, and it was common for several regions to simultaneously code for a particular property. Moreover, most anterior regions were found to code for multiple features, and a complex temporal profile could be observed for the majority of properties. For example, some features inherent to the nouns were encoded earlier than others, and the extent of time in which these properties could be decoded varied greatly among them. These findings complement much of the work previously described in the literature, and offer new insights about the temporal aspects of the neural encoding of concrete nouns. This model provides a spatiotemporal signature of the representation of objects in the brain. Paired with data from carefully-designed paradigms, the model is an important tool with which to analyze the commonalities of the neural code across stimulus modalities and tasks performed by the subjects.

Entender como o cérebro processa informação é um dos problemas mais fascinantes da ciência de nossos dias. Para resolvê-lo, é fundamental estudarmos os mecanismos de representação e transmissão de informação de um único neurônio, a unidade fundamental de processamento do cérebro. Grande parte dos neurônios representa a informação por seqüências de pulsos elétricos, ou potenciais de ação. Nós usamos a mosca como modelo para estudar como a arborização dendrítica do neurônio influencia a quantidade de informação transmitida pela estrutura temporal da seqüência de pulsos. O mapeamento retinotópico da informação no sistema visual da mosca permite que as arborizações dendríticas de certos neurônios sejam estimuladas localmente através da região que corresponde a essa localização no campo visual. Nós apresentamos imagens em movimento em várias regiões do campo visual da mosca e medimos a resposta do neurônio H1, sensível a movimentos horizontais. Usando a teoria da informação, calculamos a quantidade de informação transmitida para cada uma dessas regiões do campo visual e a relacionamos com outras propriedades do neurônio, como por exemplo a sensibilidade espacial e eficiência. Nossos resultados sugerem que a arborização dendrítica influencia a codificação temporal de maneira significativa, indicando que o neurônio pode usar a estrutura temporal da sequência de pulsos para codificar outros parâmetros do estímulo, ou para aumentar a confiabilidade da codificação dependendo da região excitada / Understanding how the brain processes information about the outside world is one of the most fascinating problems of modern science. This involves the analysis of information representation and transmission in the fundamental processing element of the brain - the neuron. In the cortex neurons represent information by sequences of electrical pulses, or spikes. We use the fly as a model to study how the amount of information transmitted by the temporal structure of the spike trains depends on the neuron\'s dendritic arborization. The retinotopic mapping of information in the fly\'s visual system allows the stimulation of specific regions of the neuron\'s dendritic tree through the visual stimulation of the respective region in the visual field of the fly. We show an image moving in the preferred direction of the motion-sensitive neuron H1 in specific regions of the fly\'s visual field and measure the electrical response of the neuron. Using information theory, we calculate the amount of information transmitted for each of these regions and compare it with other properties of the neuron, for example, the spatial sensitivity. Our results suggest that the dendritic arborization influentes the temporal coding in a significant way, indicating that the neuron could use the temporal structure of the spike train to codify other parameters of the stimulus, or to increase the reliability of the code depending on the excited region

L’information visuelle est transmise de la rétine au cerveau par les cellules ganglionnaires. Il existe plusieurs types de cellules ganglionnaires, chaque type formant une mosaïque qui couvre l’intégralité de la scène visuelle. Comprendre la manière dont ces neurones encodent collectivement la scène visuelle est essentiel pour au moins deux raisons: - La manière dont une population de neurones encodent collectivement une information sensorielle reste jusqu’à aujourd’hui mystérieuse. La rétine est un système idéal pour étudier cette question: elle a en effet une structure en couches 2D qui se prête idéalement à l’enregistrement d’une population complète de neurones à grande échelle, et elle opère une transformation complexe de la scène visuelle. - Certaines maladies qui mènent à la cécité ne connaissent aujourd’hui pas de traitement. Plusieurs stratégies de restauration visuelle basées sur la stimulation directe de cellules ganglionnaires sont le sujet de recherches actives. Il pourrait être nécessaire de reproduire en imitant le code neural produit par la rétine pour optimiser les résultats de ces stratégies thérapeutiques. Au cours de ma thèse, j’ai travaillé sur deux questions complémentaires, qui sont liées à ces deux sujets respectivement. / Visual information is conveyed from the retina to the brain through ganglion cells. Ganglion cells are divided in different cell types, and each of them form a mosaic sampling the entire visual scene. Understanding how these neurons encode the visual scene is essential for at least two reasons: -It is still unclear how a population of neurons collectively code sensory information. The retina is an ideal system to study this issue: while it performs complex processing on the visual stimulus, its 2-D structure makes it suitable for large-scale recordings of complete populations of neurons. -Some diseases leading to blindness have currently no cure. Several visual restoration strategies based on the direct stimulation of ganglion cells are currently being investigated. Emulating the retinal code may be necessary to optimize the results of these therapeutic approaches. In my thesis I have worked on two complementary questions, that are related to these two topics.

We study two methods of data analysis which are common tools for the analysis of neuronal data. In particular, we examine how causal interactions between brain regions can be investigated using time series reflecting the neural activity in these regions. Furthermore, we analyze a method used to study the neural code that evaluates the discrimination of the responses of single neurons elicited by different stimuli. This discrimination analysis is based on the quantification of the similarity of the spike trains with time scale parametric spike train distances. In each case we describe the methods used for the analysis of the neuronal data and we characterize their specificity using simulated or exemplary experimental data. Taking into account our results, we comment the previous studies in which the methods have been applied. In particular, we focus on the interpretation of the statistical measures in terms of underlying neuronal causal connectivity and properties of the neural code, respectively. / Estudiem dos mètodes d'anàlisi de dades que són eines habituals per a l'anàlisi de dades neuronals. Concretament, examinem la manera en què les interaccions causals entre regions del cervell poden ser investigades a partir de sèries temporals que reflecteixen l'activitat neuronal d'aquestes regions. A més a més, analitzem un mètode emprat per estudiar el codi neuronal que avalua la discriminació de les respostes de neurones individuals provocades per diferents estímuls. Aquesta anàlisi de la discriminació es basa en la quantificació de la similitud de les seqüències de potencials d'acció amb distàncies amb un paràmetre d'escala temporal. Tenint en compte els nostres resultats, comentem els estudis previs en els quals aquests mètodes han estat aplicats. Concretament, ens centrem en la interpretació de les mesures estadístiques en termes de connectivitat causal neuronal subjacent i propietats del codi neuronal, respectivament.