Results 1  10
of
59
Multisector models
 In Handbook of Development Economics, eds., H. Chenery and T.N. Srinivasan
, 1989
"... To the best of my knowledge, this thesis contains no copy or paraphrase of work published by another person, except where duly acknowledged in the text. This thesis contains no material which has been presented for a degree at the University of Sydney or any other university. ..."
Abstract

Cited by 39 (8 self)
 Add to MetaCart
To the best of my knowledge, this thesis contains no copy or paraphrase of work published by another person, except where duly acknowledged in the text. This thesis contains no material which has been presented for a degree at the University of Sydney or any other university.
A neurobiological theory of meaning in perception. Part 1. Information and meaning in nonconvergent and nonlocal brain dynamics
 Int. J. Bifurc. Chaos
, 2003
"... Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integrati ..."
Abstract

Cited by 28 (14 self)
 Add to MetaCart
Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integration. However, propagation delays are obligatory. Delays vary with the lengths and conduction velocities of the axons carrying the information, causing phase dispersion. In order to determine how synchrony is achieved despite dispersion, we recorded EEG signals from multiple electrode arrays on five cortical areas in cats and rabbits, that had been trained to discriminate visual or auditory conditioned stimuli. Analysis by timelagged correlation, multiple correlation and PCA, showed that maximal correlation was at zero lag and averaged.7, indicating that 50 % of the power in the gamma range among the five areas was at zero lag irrespective of phase or frequency. There were no stimulusrelated episodes of transiently increased phase locking among the areas, nor EEG "bursts " of transiently increased amplitude above the sustained level of synchrony. Three operations were identified to account for the sustained correlation. Cortices broadcast their outputs over divergentconvergent axonal
Spatiotemporal Analysis of Prepyriform, Visual, Auditory, and Somesthetic Surface EEGs in Trained Rabbits
 J. Neurophysiol
, 1996
"... inst log frequency, revealed 1/f spectra in both pre and poststimulus segments for CS and CS+ stimuli. The yintercepts and slopes for average PSDs were significantly different between pre and poststimulus segments, owing to the evoked potentials, but not between CS and CS+ stimulus segments. ..."
Abstract

Cited by 28 (10 self)
 Add to MetaCart
inst log frequency, revealed 1/f spectra in both pre and poststimulus segments for CS and CS+ stimuli. The yintercepts and slopes for average PSDs were significantly different between pre and poststimulus segments, owing to the evoked potentials, but not between CS and CS+ stimulus segments. 6.##### Spatiotemporal patterns were invariant over all frequency bins from 20100 Hz in the 1/ f domain. Spatiotemporal patterns in the 220 Hz domain progressively differed from the invariant patterns with decreasing frequency. 7.##### In the spatial frequency domain, the logarithm of the average spatial FFT power spectra from pre and poststimulus neocortical EEG segments, when plotted against the log spatial frequency, fell monotonically from the maximum at the lowest spatial frequency, concavely curving to a linear 1/f spectral domain. This curve in the 1/f spectral domain extended from 0.133  0.880 cycles/mm in the PPC and from 0.095  0.624 cycles/mm in the neocortices. 8.#####
Quantum Neural Computing
, 1995
"... This article reviews the limitations of the standard computing paradigm and sketches the concept of quantum neural computing. Implications of this idea for the understanding of biological information processing and design of new kinds of computing machines are described. Arguments are presented in s ..."
Abstract

Cited by 22 (11 self)
 Add to MetaCart
This article reviews the limitations of the standard computing paradigm and sketches the concept of quantum neural computing. Implications of this idea for the understanding of biological information processing and design of new kinds of computing machines are described. Arguments are presented in support of the thesis that brains are to be viewed as quantum systems with their neural structures representing the classical measurement hardware. From a performance point of view, a quantum neural computer may be viewed as a collection of many conventional computers that are designed to solve different problems. A quantum neural computer is a single machine that reorganizes itself, in response to a stimulus, to perform a useful computation. Selectivity offered by such a reorganization appears to be at the basis of the gestalt style of biological information processing. Clearly, a quantum neural computer is more versatile than the conventional computing machine.
Pattern formation in intracortical neuronal fields
 Network
, 2003
"... This paper introduces a neuronal field model for both excitatory and inhibitory connections. A single integrodifferential equation with delay is derived and studied at a critical point by stability analysis, which yields conditions for static periodic patterns and wave instabilities. It turns out t ..."
Abstract

Cited by 17 (7 self)
 Add to MetaCart
This paper introduces a neuronal field model for both excitatory and inhibitory connections. A single integrodifferential equation with delay is derived and studied at a critical point by stability analysis, which yields conditions for static periodic patterns and wave instabilities. It turns out that waves only occur below a certain threshold of the activity propagation velocity. An additional brief study exhibits increasing phase velocities of waves with decreasing slope subject to increasing activity propagation velocities, which are in accordance with experimental results. Numerical studies near and far from instability onset supplement the work. (Some figures in this article are in colour only in the electronic version) 1.
Taming Chaos: Stabilization of Aperiodic Attractors by Noise
 IEEE Trans. Circ. Syst. – I. Fundamental Theory & Appl
, 1997
"... A model named "KIII" of the olfactory system contains an array of 64 coupled oscillators simulating the olfactory bulb (OB), with negative and positive feedback through lowpass filter lines from single oscillators simulating the anterior olfactory nucleus (AON) and prepyriform cortex (PC). It is im ..."
Abstract

Cited by 16 (5 self)
 Add to MetaCart
A model named "KIII" of the olfactory system contains an array of 64 coupled oscillators simulating the olfactory bulb (OB), with negative and positive feedback through lowpass filter lines from single oscillators simulating the anterior olfactory nucleus (AON) and prepyriform cortex (PC). It is implemented in C to run on Macintosh, IBM, or UNIX platforms. The output can be set by parameter optimization to point, limit cycle, quasiperiodic, or aperiodic (presumably chaotic) attractors. The first three classes of solutions are stable under variations of parameters and perturbations by input, but they are biologically unrealistic. Chaotic solutions simulate the properties of timedependent densities of olfactory action potentials and EEG's, but they transit into the basins of point, limit cycle, or quasiperiodic attractors after only a few seconds of simulated run time. Despite use of double precision arithmetic giving 64bit words, the KIII model is exquisitely sensitive to changes in the terminal bit of parameters and inputs. The global stability decreases as the number of coupled oscillators in the OB is increased, indicating that attractor crowding reduces the size of basins in the model to the size of the digitizing step (10 016 ). Chaotic solutions having biological verisimilitude are robustly stabilized by introducing lowlevel, additive noise from a random number generator at two biologically determined points: rectified, spatially incoherent noise on each receptor input line, and spatially coherent noise to the AON, a global control point receiving centrifugal inputs from various parts of the forebrain. Methods are presented for evaluating global stability in the high dimensional system from measurements of multiple chaotic outputs. Ranges of stability are sho...
LocalGlobal Interactions and the Role of Mesoscopic (intermediateRange) Elements in Brain Dynamics
"... 59 words; text 1,000 words. Abstract A unifing theory of spatiotemporal brain dynamics should incorporate multiple spatial and temporal scales. Between the microscopic (local) and macroscopic (global) components proposed by Nunez, mesoscopic (intermediaterange) elements should be integral parts o ..."
Abstract

Cited by 15 (8 self)
 Add to MetaCart
59 words; text 1,000 words. Abstract A unifing theory of spatiotemporal brain dynamics should incorporate multiple spatial and temporal scales. Between the microscopic (local) and macroscopic (global) components proposed by Nunez, mesoscopic (intermediaterange) elements should be integral parts of models. The corresponding mathematical formalism requires tools of nonlinear dynamics and the use of aperiodic (chaotic) attractors. Some relations between localmesoscopic and mesoscopicglobal components are outlined. Commentary on Nunez 2 Freeman & Kozma 2 Linear models, amplitudedependent nonlinearities, and phase transitions in neocortical dynamics The work by Nunez is a valuable contribution to studies on spatiotemporal dynamics of brain functions, and it opens a great adventure into the yet largely undiscovered territory of interpretation of electroencephalographic (EEG) measurements and brain imaging at t he macroscopic level. Nunez introduces a localglobal model of neocortic...
Consciousness, Intentionality, and Causality
, 1999
"... To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear causal chains from receptors after the first cortical synapse, so we use circular causality to explain neural pattern formation by selforganizing dynamics. But an aspect of intentional action is causalit ..."
Abstract

Cited by 15 (0 self)
 Add to MetaCart
To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear causal chains from receptors after the first cortical synapse, so we use circular causality to explain neural pattern formation by selforganizing dynamics. But an aspect of intentional action is causality, which we extrapolate to material objects in the world. Thus causality is a property of mind, not matter.
Spatiotemporal Forward Solution of the EEG and MEG Using Network Modeling
 IEEE Transactions on Medical Imaging
, 2002
"... Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dyna ..."
Abstract

Cited by 12 (3 self)
 Add to MetaCart
Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixedcurrent dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals.
Computation by asynchronously updating cellular automata
 Journal of Statistical Physics
, 2004
"... Abstract. A known method to compute on an asynchronously updating cellular automaton is the simulation of a synchronous computing model on it. Such a scheme requires not only an increased number of cell states, but also the simulation of a global synchronization mechanism. Asynchronous systems tend ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
Abstract. A known method to compute on an asynchronously updating cellular automaton is the simulation of a synchronous computing model on it. Such a scheme requires not only an increased number of cell states, but also the simulation of a global synchronization mechanism. Asynchronous systems tend to use synchronization only on a local scale—if they use it at all. Research on cellular automata that are truly asynchronous has been limited mostly to trivial phenomena, leaving issues such as computation unexplored. This paper presents an asynchronously updating cellular automaton that conducts computation without relying on a simulated global synchronization mechanism. The 2dimensional cellular automaton employs a Mooreneighborhood and 85 totalistic transition rules describing the asynchronous interactions between the cells. Despite the probabilistic nature of asynchronous updating, the outcome of the dynamics is deterministic. This is achieved by simulating delay insensitive circuits on it, a type of asynchronous circuit that is known for its robustness to variations in the timing of signals. We implement three primitive operators on the cellular automaton from which any arbitrary delay insensitive circuit can be constructed, and show how to connect the operators such that collisions of crossing signals are avoided.