Brain Functional Architecture and Human Understanding

The opening line in Aristotle’s Metaphysics asserts that “humans desire to understand”, establishing understanding as the defining characteristic of the human mind and human species. What is understanding and what role does it play in cognition, what advantages does it confer, what brain mechanisms are involved? The Webster’s Dictionary defines understanding as “apprehending general relations in a multitude of particulars.” A proposal discussed in this chapter defines understanding as a form of active inference in self-adaptive systems seeking to expand their inference domains while minimizing metabolic costs incurred in the expansions. Under the same proposal, understanding is viewed as an advanced adaptive mechanism involving self-directed construction of mental models establishing relations between domain entities. Understanding complements learning and serves to overcome the inertia of learned behavior when conditions are unfamiliar or deviate from those experienced in the past. While learning is common across all animals, understanding is unique to the human species. This chapter will unpack these notions, focusing on different facets of understanding. The proposal formulates hypotheses regarding the underlying neuronal mechanisms, attempting to assess their plausibility and reconcile them with the recent ideas and findings concerning brain functional architecture

A model for cerebral cortical neuron group electric activity and its implications for cerebral function

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 245-265).The electroencephalogram, or EEG, is a recording of the field potential generated by the electric activity of neuronal populations of the brain. Its utility has long been recognized as a monitor which reflects the vigilance states of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural activity, understanding the neural origins of EEG oscillations has been limited. With the advent of recording techniques, however, and as an influx of experimental evidence on cellular and network properties of the neocortex has become available, a closer look into the neuronal mechanisms for EEG generation is warranted. Accordingly, we introduce an effective neuronal skeleton circuit at a neuronal group level which could reproduce basic EEG-observable slow ( 3mm). The effective circuit makes use of the dynamic properties of the layer 5 network to explain intra-cortically generated augmenting responses, restful alpha, slow wave (< 1Hz) oscillations, and disinhibition-induced seizures. Based on recent cellular evidence, we propose a hierarchical binding mechanism in tufted layer 5 cells which acts as a controlled gate between local cortical activity and inputs arriving from distant cortical areas. This gate is manifested by the switch in output firing patterns in tufted(cont.) layer 5 cells between burst firing and regular spiking, with specific implications on local functional connectivity. This hypothesized mechanism provides an explanation of different alpha band (10Hz) oscillations observed recently under cognitive states. In particular, evoked alpha rhythms, which occur transiently after an input stimulus, could account for initial reogranization of local neural activity based on (mis)match between driving inputs and modulatory feedback of higher order cortical structures, or internal expectations. Emitted alpha rhythms, on the other hand, is an example of extreme attention where dominance of higher order control inputs could drive reorganization of local cortical activity. Finally, the model makes predictions on the role of burst firing patterns in tufted layer 5 cells in redefining local cortical dynamics, based on internal representations, as a prelude to high frequency oscillations observed in various sensory systems during cognition.by Fadi Nabih Karameh.Ph.D

Intégration des signaux complexes dans le système visuel

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN . These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing

Aspects spatial et temporel de l'intégration visuelle au niveau de la voie dorsale du système visuel du chat : le cortex suprasylvien latéral comme modèle

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

High Efficiency Real-Time Sensor and Actuator Control and Data Processing

The advances in sensor and actuator technology foster the use of large multitransducer networks in many different fields. The increasing complexity of such networks poses problems in data processing, especially when high-efficiency is required for real-time applications. In fact, multi-transducer data processing usually consists of interconnection and co-operation of several modules devoted to process different tasks. Multi-transducer network modules often include tasks such as control, data acquisition, data filtering interfaces, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, fuzzy-rules used to implement such tasks may introduce module interconnection and co-operation issues. To help dealing with these problems the author here presents a software library architecture for a dynamic and efficient management of multi-transducer data processing and control techniques. The framework’s base architecture and the implementation details of several extensions are described. Starting from the base models available in the framework core dedicated models for control processes and neural network tools have been derived. The Facial Automaton for Conveying Emotion (FACE) has been used as a test field for the control architecture

Functional MRI of focal and generalised interictal epileptiform discharges.

Localizing the source of epileptic discharges is important in gaining a greater understanding of the disease, classifying epilepsy, and identifying areas suitable for potentially curable surgical resection. Functional imaging measures haemodynamic, metabolic or neurochemical correlates to localise neural activity. Combining EEG with functional MRI (EEG-fMRI) allows the localisation of haemodynamic correlates of neuronal events recorded on surface EEG. The work in this thesis aims to identify the spatial haemodynamic correlates of interictal epileptiform discharges (IED) in patients with epilepsy using EEG-fMRI. Five studies form the main body of this thesis. In the first study, 46 patients with frequent generalised spike wave activity (GSW) were studied with EEG-fMRI on a 1.5 Tesla scanner. The main finding was of a characteristic pattern of fMRI signal decrease in frontal, parietal and posterior cingulate cortex, areas of association cortex, during GSW. In the second study, 4 patients from this first series were re-studied with a 3 Tesla scanner. A high degree of reproducibility was seen in the spatial distribution of fMRI changes. Perfusion MRI with an arterial spin label sequence was used that showed a decrease in blood flow to these areas during GSW. In the third study, a novel method for the analysis of fMRI data in epilepsy, temporal clustering analysis (TCA) was assessed. The technique was confounded by subject motion, and we were unable to reliably detect correlates of IED. The fourth study moves away from correlating visually identified IEDs on the EEG, and correlates power fluctuations in the delta frequency band with simultaneously acquired fMRI. Finally a combination of EEG-fMRI and MR tractography were used to study a patient with temporal lobe epilepsy. The issues surrounding potential use of EEG-fMRI as a clinical tool are discussed