PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

Diversity, Heterogeneity and Orientation Dependent Variation of Spike Count Correlation in the Cat Visual Cortex

京都産業大学201

Information-geometric method for multiple neuronal spike data analysis

This dissertation explores a novel statistical technique—information geometric method for theory and its application in analysis of multiple neuronal spike data. The previous studies have indicated that information-geometric method provides a powerful tool of estimating neuronal interactions from observed spiking data. However, these studies were conducted based on simplified neural network structure, which has limitations in the real brain. We systematically extended the previous studies by using intensive mathematical analysis and numerical simulations of realistic and complex neural network. The studies show that information geometric approach provide robust estimation for the sum of the connection weights between neuronal pairs in a complex recurrent network, providing a way of investigating the underlying network structures from neuronal spike data.Alberta Innovates Technology Futures (SCH001),National Science Foundation(CRCNS-1010172),Alberta Innovates Health Solution

Contour Integration via Cortical Interactions in Visual Cortex

The visual system possesses a remarkable ability to group fragmented line segments into coherent contours and to segregate them from background. This process, known as contour integration, is critical to identifying object boundaries in complex visual scenes, and thus particularly important for performing shape discrimination, image segmentation and ultimately object recognition. Current evidence supports the idea that long-range horizontal connections in early visual cortex contribute to the process of contour integration, but the underling cortical circuitry, particularly the top-down feedback influence from higher visual areas, is not fully understood. Throughout the thesis, we took computational approaches to systematically examine how contour information is represented across the network of cortical areas and the circuitry by which this information is encoded. Three closely related projects, each having new methods development and hypothesis testing, were performed to analyze and interpret a very large set of neural data. The data set consists of recently acquired multi-electrode multi-unit spikes and local field potentials (LFPs) simultaneously recorded in visual areas V1 and V4 of monkeys performing a visual contour detection task. In the first project, well-established Granger causality measure was extended to the analysis of spiking trains data, which enabled us to quantify the causal interactions within and between areas V1 and V4. Our findings provided clear evidence that there is a top-down V4 feedback influence upon early visual area V1 during contour integration. In the second project, we investigated whether the contour signals in V1 are derived from feedback inputs alone, or whether they are mediated by an intimate interaction between feedback and horizontal connections within V1. Conditional causality measure was developed to dissect the respective contributions of V1 horizontal connections and V4 feedback to contour grouping. Our results suggest that feedback and lateral connections closely interact to mediate the contour integration process. In the third project, a novel Granger causality measure was proposed for the analysis of mixed neural data of spikes and LFP. Spikes and LFP are generated by separate sources with distinct signal characteristics. A joint analysis of spikes and LFP was performed to address the fundamental question about how contour regulates cortical communication between individual neurons and local network activity. The results conform to the general input-output relationship between LFP and spikes within an area. Importantly, we found that contour-related causality is only observed from spikes to LFP, but not in the opposite direction. These findings suggest that Granger causality from spikes to LFP, rather than that from LFP to spikes, carries contour-related information. Taken together, these results indicate that cortical interactions underlie contour integration, thus contribute to a better understanding of the cortical circuitry for parsing visual images and for sensory processing in general. Given the increasing use of multi-electrode recordings in multiple cortical areas, the methodology developed in this thesis should also have a broad impact.Ph.D., Biomedical Engineering -- Drexel University, 201

Information Encoding by Individual Neurons and Groups of Neurons in the Primary Visual Cortex

How is information about visual stimuli encoded into the responses of neurons in the cerebral cortex? In this thesis, I describe the analysis of data recorded simultaneously from groups of up to eight nearby neurons in the primary visual cortices of anesthetized macaque monkeys. The goal is to examine the degree to which visual information is encoded into the times of action potentials in those responses (as opposed to the overall rate), and also into the identity of the neuron that fires each action potential (as opposed to the average activity across a group of nearby neurons). The data are examined with techniques modified from systems analysis, statistics, and information theory. The results are compared with expectations from simple statistical models of action-potential firing and from models that are more physiologically realistic. The major findings are: (1) that cortical responses are not renewal processes with time-varying firing rates, which means that information can indeed be encoded in the detailed timing of action potentials; (2) that these neurons encode the contrast of visual stimuli primarily into the time difference between stimulus and response onset, which is known as the latency; (3) that this so-called temporal coding serves as a mechanism by which the brain might discriminate among stimuli that evoke similar firing rates; (4) that action potentials preceded by interspike intervals of different durations can encode different features of a stimulus; (5) that the rate of overall information transmission can depend on the type of stimulus in a manner that differs from one neuron to the next; (6) that the rate at which information is transmitted specifically about stimulus contrast depends little on stimulus type; (7) that a substantial fraction of the information rate can be confounded among multiple stimulus attributes; and, most importantly, (8) that averaging together the responses of multiple nearby neurons leads to a significant loss of information that increases as more neurons are considered. These results should serve as a basis for direct investigation into the cellular mechanisms by which the brain extracts and processes the information carried in neuronal responses