Study of a chaotic olfactory neural network model and its applications on pattern classification.

Based on the research of biological olfactory system, a novel chaotic neural network model - K set model has been established. This chaotic neural network can not only simulate the real brain activity of olfactory system, but also present novel chaotic concept for signal processing and pattern recognition. This paper investigates the characteristics of the K set models. Experimental result shows that KIII model can be used for various area of pattern classification efficiently.

Early and late patterns of stimulus-related activity in auditory cortex of trained animals.

Epidural electrocorticograms over the right auditory cortex (field AI) were measured using implanted 18-channel (3 x 6) electrode arrays in four animals (Mongolian gerbil) trained to discriminate between a rising and a falling frequency modulated tone (frequency range 2-4 kHz). Using a previously introduced classification procedure, transient patterns of cortical activity suitable to discriminate between the rising and the falling modulation were identified. Early (locked to stimulus onset) and late (emerging at variable times poststimulus) patterns could be differentiated. Deletion of increasing numbers of randomly selected electrodes was used to determine a critical density of recording channels required to capture the discriminative power of the early and late patterns. Statistical analysis of the classification revealed a sigmoid dependence of the discriminative power from the number of remaining electrodes with an inflection point at 12 electrodes. The analysis of the minima of the classification statistic revealed that in the early patterns discriminative information was focal on regions corresponding to the tonotopic representation of the stimuli, whereas in late patterns this information seemed to be distributed nonfocally across larger cortical regions. This analysis supports the previous notion of the coexistence of topographically organized activity states related to the physical stimulus features and nontopographically organized states determined largely by intrinsic factors (Ohl et al. 2001).

Change in pattern of ongoing cortical activity with auditory category learning.

Humans are able to classify novel items correctly by category; some other animals have also been shown to do this. During category learning, humans group perceptual stimuli by abstracting qualities from similarity relationships of their physical properties. Forming categories is fundamental to cognition and can be independent of a 'memory store' of information about the items or a prototype. The neurophysiological mechanisms underlying the formation of categories are unknown. Using an animal model of category learning, in which frequency-modulated tones are distinguished into the categories of 'rising' and 'falling' modulation, we demonstrate here that the sorting of stimuli into these categories emerges as a sudden change in an animal's learning strategy. Electro-corticographical recording from the auditory cortex shows that the transition is accompanied by a change in the dynamics of cortical stimulus representation. We suggest that this dynamic change represents a mechanism underlying the recognition of the abstract quality (or qualities) that defines the categories.

Response dynamics of entorhinal cortex in awake, anesthetized, and bulbotomized rats.

The generation of oscillatory activity may be crucial to brain function. The coordination of individual neurons into rhythmic and coherently active populations is thought to result from interactions between excitatory and inhibitory cells mediated by local feedback connections. By using extracellular recording wires and silicon microprobes to measure electrically evoked damped oscillatory responses at the level of neural populations in the entorhinal cortex, and by using current-source density analysis to determine the spatial pattern of evoked responses, we show that the propagation of activity through the cortical circuit and consequent oscillations in the local field potential are dependent upon background neural activity. Pharmacological manipulations as well as surgical disconnection of the olfactory bulb serve to quell the background excitatory input incident to entorhinal cortex, resulting in evoked responses without characteristic oscillations and showing no signs of polysynaptic feedback. Electrical stimulation at 200 Hz applied to the lateral olfactory tract provides a substitute for the normal background activity emanating from the bulb and enables the generation of oscillatory responses once again. We conclude that a non-zero background level of activity is necessary and sufficient to sustain normal oscillatory responses and polysynaptic transmission through the entorhinal cortex.

Biocomplexity: adaptive behavior in complex stochastic dynamical systems.

Existing methods of complexity research are capable of describing certain specifics of bio systems over a given narrow range of parameters but often they cannot account for the initial emergence of complex biological systems, their evolution, state changes and sometimes-abrupt state transitions. Chaos tools have the potential of reaching to the essential driving mechanisms that organize matter into living substances. Our basic thesis is that while established chaos tools are useful in describing complexity in physical systems, they lack the power of grasping the essence of the complexity of life. This thesis illustrates sensory perception of vertebrates and the operation of the vertebrate brain. The study of complexity, at the level of biological systems, cannot be completed by the analytical tools, which have been developed for non-living systems. We propose a new approach to chaos research that has the potential of characterizing biological complexity. Our study is biologically motivated and solidly based in the biodynamics of higher brain function. Our biocomplexity model has the following features, (1) it is high-dimensional, but the dimensionality is not rigid, rather it changes dynamically; (2) it is not autonomous and continuously interacts and communicates with individual environments that are selected by the model from the infinitely complex world; (3) as a result, it is adaptive and modifies its internal organization in response to environmental factors by changing them to meet its own goals; (4) it is a distributed object that evolves both in space and time towards goals that is continually re-shaping in the light of cumulative experience stored in memory; (5) it is driven and stabilized by noise of internal origin through self-organizing dynamics. The resulting theory of stochastic dynamical systems is a mathematical field at the interface of dynamical system theory and stochastic differential equations. This paper outlines several possible avenues to analyze these systems. Of special interest are input-induced and noise-generated, or spontaneous state-transitions and related stability issues.

Analysis of spatial patterns of phase in neocortical gamma EEGs in rabbit.

Arrays of 64 electrodes (8 x 8, 7 x 7 mm) were implanted epidurally on the surface of the visual, auditory or somatosensory cortex of rabbits trained to discriminate conditioned stimuli in the corresponding modality. The 64 electroencephalographic (EEG) traces at all times displayed a high degree of spatial coherence in wave form, averaging >90% of the variance in the largest principal components analysis component. The EEGs were decomposed with the fast Fourier transform (FFT) to give the spatial distributions of amplitude and phase modulation (AM and PM) in segments 128 ms in duration. Spatial (2-dimensional) and temporal (1-dimensional) filters were designed to optimize classification of the spatial AM patterns in the gamma range (20-80 Hz) with respect to discriminative conditioned stimuli. No evidence was found for stimulus-dependent classification of the spatial PM patterns. Instead some spatial PM distributions conformed to the pattern of a cone. The location and sign (maximal lead or lag) of the conic apex varied randomly with each recurrence. The slope of the phase gradient varied in a range corresponding to that of the conduction velocities reported of axons to extend parallel to the cortical surfaces. The durations and times of recurrence of the phase cones corresponded to those of the optimally classified spatial AM patterns. The interpretation is advanced that the phase cones are manifestations of state transitions in the mesoscopic dynamics of sensory cortices by which the intermittent AM patterns are formed. The phase cones show that the gamma EEG spatial coherence is not due to volume conduction from a single deep-lying dipole generator nor to activity at the site of the reference lead on monopolar recording. The random variation of the apical sign shows that gamma AM patterns are self-organized and are not imposed by thalamic pacemakers. The half-power radius of the phase gradient provides a useful measure of the soft boundary condition for the formation and read-out of cooperative cortical domains responsible for binding sensory information into the context of prior experience in the process of perception.

Topographic analysis of epidural pure-tone-evoked potentials in gerbil auditory cortex.

This study investigated the tonotopic organization of pure-tone-evoked middle latency auditory evoked potentials (MAEPs) recorded at the auditory cortical surface in unanesthetized gerbils. Multielectrode array recording and multiple linear regression analysis of the MAEP demonstrated different degrees of tonotopic organization of early and late MAEP components. The early MAEP components P1 and N1 showed focal topography and clear dependence in location and size of cortical area covered on pure-tone frequency. The later components P2 and N2 showed a widespread topography which was largely unaffected in location and size of cortical area covered by pure-tone frequency. These results allow delimitation of the neural generators of the early and late MAEP components in terms of the spectral properties of functionally defined neural populations.

Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands.

Spatial spectral analysis is essential for deriving spatial patterns from simultaneous recordings of electrocorticograms (ECoG), in order to determine the optimal interval between electrodes in arrays, and to design spatial filters, particularly for extraction of information about the dynamics of human gamma activity. ECoG were recorded from up to 64 electrodes 0.5 mm apart in a linear array 3.2 cm long, which was placed on the exposed superior temporal gyrus or motor cortex of volunteers undergoing diagnostic surgery. Visual displays of multiple traces revealed broad spectrum oscillations in episodic bursts having a common aperiodic wave form with recurring patterns of spatial amplitude modulation (AM patterns) on selected portions of the array. The one-dimensional spatial spectrum of the human ECoG was calculated at successive time samples and averaged over periods of up to 20 s. Log power decreased monotonically with increasing log spatial frequency in cycles/mm (c/mm) to the noise level approximately 2 log units below maximal power at minimal frequency (0.039+/-0.002 c/mm). The inflection point at 0.40+/-0.05 c/mm specified an optimal value for a low pass spatial filter to remove noise, and an optimal interelectrode spacing of 1.25 mm to avoid undersampling and aliasing. An 8 x 8 array with that spacing would be 10 x 10 mm.

Mesoscopic neurodynamics: from neuron to brain.

Intelligent behavior is characterized by flexible and creative pursuit of endogenously defined goals. Intentionality is a key concept by which to link neuron and brain to goal-directed behavior through brain dynamics. An archetypal form of intentional behavior is an act of observation in space-time, by which information is sought for the guidance of future action to explore unpredictable and ever-changing environments. These acts are based in the brain dynamics that creates spatiotemporal patterns of neural activity, serving as images of goals, of command sequences by which to act to reach goals, and of expected changes in sensory input resulting from intended actions. Prediction of the sensory consequences of intended action and evaluation of performance is by reafference. An intentional act is completed upon modification of the system by itself through learning. These principles are well known among psychologists and philosophers. What is new is the development of nonlinear mesoscopic brain dynamics, by which the theory of chaos can be used to understand and simulate the constructions of meaningful patterns of neural activity that implement the process of observation. The design of neurobiological experiments, analysis of the resulting data, and synthesis of explanatory models require an understanding of the hierarchical nature of brain organization, here conceived as single neurons and neural networks at the microscopic level; clinically defined cortical and subcortical systems studied by brain imaging (for example, fMRI) at the macroscopic level, and self-organizing neural populations at an intermediate mesoscopic level, at which synaptic interactions create novel activity patterns through nonlinear state transitions. The constructive neurodynamics of sensory cortices, when they are engaged in pattern recognition, is revealed by learning-dependent spatial patterns of amplitude modulation and by newly discovered radially symmetric spatial gradients of the phase of aperiodic carrier waves in multichannel subdural EEG recordings.