The Computational Brain.

Keywords: reductionism, neural networks, distributed coding, Karl Pribram, computational neuroscience, receptive field 1.1 The broad goal of this book, expressed at the start, is ``to understand how neurons give rise to a mental life.'' A mental reductionism is assumed in this seductively simple formulation. Indeed, the book represents reductionism at its best, as the authors guide the reader through the many intermediate levels that link neurons with mental life. In so doing they attack a problem that has persisted for some decades in the neurosciences, since the development of single-cell recording methods. The problem is that millions of neurons participate in every behaviorally meaningful activity, but we normally record from only one neuron at a time, or at best a handful. The temptation is great to overestimate the one-millionth sample obtained from a single neuron, to interpret its activity as detecting a perceptual situation or driving a motor response. This approach, seemingly inescapable in the 1960s, became untenable, but there were no concrete alternatives. Evoked potential techniques gave only a gross average of activity, too vague to pin down mechanisms, and early PDP (parallel distributed processing, or artificial neural network) models were too biologically unrealistic to provide viable interpretations of the single-cell data. Churchland and Sejnowski show how distributed models can now attack this problem, providing significant insights into brain function in a number of domains. 1.2 The book has several parts. First, the authors introduce their approach, combining anatomical, physiological, behavioral and modelling methods in an integrated interdisciplinary attack on specific functional systems. There follows a review of enough anatomy and neurophysiology to make the authors' viewpoint clear and to provide a background for integrating PDP modelling into specific problems in the neurosciences. The heart of the book is a series of chapters reviewing particular models that have been successful in increasing our understanding of the functioning of biological brains. Models of reflex reactions in invertebrates, of locomotion, the vestibulo-ocular reflex in primates

Stability Analysis of Asynchronous States in Neuronal Networks with Conductance-Based Inhibition

Oscillations in networks of inhibitory interneurons have been reported at various sites of the brain and are thought to play a fundamental role in neuronal processing. This Letter provides a self-contained analytical framework that allows numerically efficient calculations of the population activity of a network of conductance-based integrate-and-fire neurons that are coupled through inhibitory synapses. Based on a normalization equation this Letter introduces a novel stability criterion for a network state of asynchronous activity and discusses its perturbations. The analysis shows that, although often neglected, the reversal potential of synaptic inhibition has a strong influence on the stability as well as the frequency of network oscillations

Spin as Primordial Self-Referential Process Driving Quantum Mechanics, Spacetime Dynamics and Consciousness

We have recently theorized that consciousness is intrinsically connected to quantum mechanical spin since said spin is embedded in the microscopic structure of spacetime and is more fundamental than spacetime itself, that is, spin is the “mind-pixel.” Applying these ideas to the particular structures and dynamics of the brain, we have developed a qualitative model of quantum consciousness. In this paper, we express our fundamental view that spin is a primordial self-referential process driving quantum mechanics, spacetime dynamics and consciousness. To justify such a view, we will draw support from existing literatures, discuss from a reductionist perspective the essential properties said spin should possess as mind-pixel and explore further the nature of spin to see whether said properties are present. Our conclusion is that these properties are indeed endowed to spin by Nature. One of the implications from our fundamental view is that the probabilistic structure of quantum mechanics is due to the self-referential collapse of spin state that is contextual, non-local, non-computable and irreversible. Therefore, a complete theory of the self-referential spin process is necessarily semantic, that is, it should be based on internally meaningful information

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher