Search results

The purpose of this paper is to outline an integrative, high-level, neurocomputational theory of brain function based on temporal codes, neural timing nets, and active regeneration of temporal patterns of spikes within recurrent neural circuits that provides a time-domain alternative to connectionist approaches.

This conceptual-theoretical paper draws from cybernetics, theoretical biology, neurophysiology, integrative and computational neuroscience, psychology, and consciousness studies.

The high-level functional organization of the brain involves adaptive cybernetic, goal-seeking, switching, and steering mechanisms embedded in percept-action-environment loops. The cerebral cortex is conceived as a network of reciprocally connected, re-entrant loops within which circulate neuronal signals that build up, decay, and/or actively regenerate. The basic signals themselves are temporal patterns of spikes (temporal codes), held in the spike correlation mass-statistics of both local and global neuronal ensembles. Complex temporal codes afford multidimensional vectorial representations, multiplexing of multiple signals in spike trains, broadcast strategies of neural coordination, and mutually reinforcing, autopoiesis-like dynamics. Our working hypothesis is that complex temporal codes form multidimensional vectorial representations that interact with each other such that a few basic processes and operations may account for the vast majority of both low- and high-level neural informational functions. These operational primitives include mutual amplification/inhibition of temporal pattern vectors, extraction of common signal dimensions, formation of neural assemblies that generate new temporal pattern primitive “tags” from meaningful, recurring combinations of features (perceptual symbols), active regeneration of temporal patterns, content-addressable temporal pattern memory, and long-term storage and retrieval of temporal patterns via a common synaptic and/or molecular mechanism. The result is a relatively simplified, signal-centric view of the brain that utilizes universal coding schemes and pattern-resonance processing operations. In neurophenomenal terms, waking consciousness requires regeneration and build up of temporal pattern signals in global loops, whose form determines the contents of conscious experience at any moment.

Understanding how brains work as informational engines has manifold long-reaching practical implications for design of autonomous, adaptive robotic systems. By proposing how new concepts might arise in brains, the theory bears potential implications for constructivist theories of mind, i.e. how observer-actors interacting with one another can self-organize and complexify.

The theory is highly original and heterodox in its neural coding and neurocomputational assumptions. By providing a possible alternative to standard connectionist theory of brain function, it expands the scope of thinking about how brains might work as informational systems.

Using cortical neurons of animals to control external devices allows experimenters a unique opportunity to study the capability of the brain to utilize a new actuator. The purpose of this paper is to investigate the ability of unrestrained rats to control a directional task using motor cortical signals.

In freely moving rats, signals recorded from the motor cortex of the brain enabled the use of a closed loop brain machine interface (BMI) system to replace paddle pressing for a directional task. In this system, ring rates from several (two to ten) motor cortical neurons at several consecutive time points were used as input to a support vector machine (SVM) classifier. The decision‐function value obtained from the SVM was then used to determine which relay should be activated to produce paddle‐pressing signals in the task. All five animals tested were able to use this interface immediately and significant changes in neural activity arise in a single, 45‐min, experimental session. Neural data from three of the subjects were examined for changes from the calibration phase (data used to build the SVM model) to the late cortically controlled phase.

Detailed analysis shows that neural activity changes significantly from the calibration phase to the cortically controlled phase, furthermore, the decision‐function values arising from these neural signals change to support better performance. By examining which neurons and times are selected by the SVM to have significant impact on the decision‐function value as well as which of these elements change significantly, a mechanism of adaptation begins to emerge in which the SVM properly assigns high importance to dimensions that easily predict the desired output, however, under closed‐loop control, the animal selects a small number of neurons (at most or all times) and chooses to make the firing rates more distinguishable. Video taken of one of the subjects further suggests the nature of the behavioral correlates of these changes.

In the design of practical BMI devices for human patients, one effective strategy might involve creating mappings from multi‐neuron ensembles using state‐of‐the‐art machine learning techniques, but expect that the patients who use the devices will adapt to the devices using single neuron modulation changes.

In the design of practical BMI devices for human patients, one effective strategy might involve creating mappings from multi‐neuron ensembles using state‐of‐the‐art machine learning techniques, but expect that the patients who use the devices will adapt to the devices using single neuron modulation changes. The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.

The purpose of this paper is to build a neural model of an aircraft from flight data and online estimation of the aerodynamic derivatives from established neural model.

A neural model capable of predicting generalized force and moment coefficients of an aircraft using measured motion and control variable is used to extract aerodynamic derivatives. The use of neural partial differentiation (NPD) method to the multi-input-multi-output (MIMO) aircraft system for the online estimation of aerodynamic parameters from flight data is extended.

The estimation of aerodynamic derivatives of rigid and flexible aircrafts is treated separately. In the case of rigid aircraft, longitudinal and lateral-directional derivatives are estimated from flight data. Whereas simulated data are used for a flexible aircraft in the absence of its flight data. The unknown frequencies of structural modes of flexible aircraft are also identified as part of estimation problem in addition to the stability and control derivatives. The estimated results are compared with the parameter estimates obtained from output error method. The validity of estimates has been checked by the model validation method, wherein the estimated model response is matched with the flight data that are not used for estimating the derivatives.

Compared to the Delta and Zero methods of neural networks for parameter estimation, the NPD method has an additional advantage of providing the direct theoretical insight into the statistical information (standard deviation and relative standard deviation) of estimates from noisy data. The NPD method does not require the initial value of estimates, but it requires a priori information about the model structure of aircraft dynamics to extract the flight stability and control parameters. In the case of aircraft with a high degree of flexibility, aircraft dynamics may contain many parameters that are required to be estimated. Thus, NPD seems to be a more appropriate method for the flexible aircraft parameter estimation, as it has potential to estimate most of the parameters without having the issue of convergence.

This paper highlights the application of NPD for MIMO aircraft system; previously it was used only for multi-input and single-output system for extraction of parameters. The neural modeling and application of NPD approach to the MIMO aircraft system facilitate to the design of neural network-based adaptive flight control system. Some interesting results of parameter estimation of flexible aircraft are also presented from established neural model using simulated data as a novelty. This gives more value addition to analyzing the flight data of flexible aircraft as it is a challenging problem in parameter estimation of flexible aircraft.

In many applications, very fast methods are required for estimating of parameters of harmonic signals distorted by noise. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper new parallel algorithms are proposed, which can be implemented by analogue adaptive circuits employing some neural networks principles. Algorithms based on the least‐squares (LS) and the total least‐squares (TLS) criteria are developed and compared. The problems are formulated as optimization problems and solved by using the steepest descent continuous‐time optimization algorithm. The corresponding architectures of analogue neuron‐like adaptive processors are also shown. The developed networks are more robust against noise in the measured signal than other known neural network algorithms. The network based on the TLS criterion optimizes the estimation under the assumption that the signal model can also be perturbated (frequency or sampling interval fluctuation and so forth). The TLS estimates are better and more reliable than the corresponding LS estimates, when applying a higher sampling frequency and a wider sampling window. The TLS algorithm is a generalization of the well known LMS rule and could be in some applications superior to the family of LMS algorithms. Extensive computer simulations confirm the validity and performance of the proposed algorithms.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

Robinson (1998) found that women have a larger cerebral arousal than men, and men have a larger Gsar factor of intelligence than women. It is suggested that this finding had been predicted by a previously published theory of this author. This is a continuation of a discussion, most of it in cybernetical journals, between Robinson and the present author about the biological origin of intelligence. Robinson relates intelligence to arousability, which he defined as the maximal level of activity which the cortex can obtain without activation by the brain‐stem. The author’s theory also takes into account the probability of transmission errors in the synapses and individual differences due to hemisphericity. The development of the ideas of this theory is surveyed; in each stage this theory encompassed more biological theories of intelligence. An appendix provides empirical evidence of sex‐related and hemispheric differences.

In recent years, evidence has accumulated that there are four distinct states of consciousness under normal conditions; in addition to the three commonly experienced states of wakefulness, sleep, and dreaming, there is a fourth state achieved by various Eastern meditation techniques. A model of man is discussed that could account for these states. This model, inspired by the concepts of cybernetics, holds that man's behavior and experience can be accounted for by feedback‐control processes and that these processes are hierarchically organized. This paper first attempts to demonstrate that the existence of four distinct states of consciousness is consistent with a general cybernetic view of man. Then, a specific information‐flow model of the mind, developed by Powers, is introduced and it is shown that with some revision this model well accounts for the four states.

To begin, therefore by establishing certain parameters to both delimit and evoke the discussion, one might first note before side-stepping the well-recognised ethical issues that announce themselves within these early ethnological texts (see for instance Hsu, 1979). The ‘pith-helmet’ terminology and exoticised intentionality, borne with such unselfconscious assurance, can in fact serve to effect complacency on the part of the contemporary ethnographer – were they to believe that one could completely escape such tendencies. In fact, Western thought has always displayed just these acquisitive geometries in its surveying, arraying and apprehending of the world.¹ Obviously, therefore, this is not to criticise in a naive or petulant manner a fundamental comportment of the Western intellectual tradition, which clearly structures this and every enquiry couched within its terrain. Nor is it to suggest that certain keywords (‘colonialism’ for example) might somehow name this tendency without repeating its form, or that earnest mantras concerning ‘emancipation’ or ‘respect for alterity’ immediately authorise its continuation. For one to deal responsibly with the ensuing philosophical and ethical motifs would require a measured and careful analysis beyond the remit of the present discussion. Nonetheless, the basic geometry of this disposition is assumed for ethnography in the analysis that follows. Ethnography, that is to say, is actively oriented towards an object, here referred to variously as ‘lived experience’ or ethnos, and is always to some measure engaged in the apprehension and transmission of that object.