The neurons of cerebral cortex are largely autonomous and generate activity that is manifested in trains of microscopic axonal action potentials. The neurons interact by sparse but numerous synaptic connections to generate macroscopic dendritic activity patterns that are observed in electroencephalographic (EEG) waves. The macroscopic patterns are constructed by the populations and they shape the output of cortical neurons in parallel arrays. Sensory cortexes receive sensory information in the form of microscopic action potentials, which induce state transitions in population dynamics. Each state transition transforms sensory information to perceptual meaning. The EEG reflects both kinds of activity. The sensory input is accessed by time ensemble averaging, whereas the perceptual output is found by spatial ensemble averaging. Spatial phase gradients in the EEG are useful for identifying EEG segments in a sequence of state transitions in response to sensory input. The rapidity and flexibility with which they take place give strong reason to postulate that the mechanism for the construction of these sequences of patterns is a dynamical system operating in a chaotic domain.

Action Potentials↗

PubMedDate not supplied

Valium, histamine, and neural networks.

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Arousal↗

PubMedDate not supplied

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.

Animals↗

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Spatial representation of frequency-modulated tones in gerbil auditory cortex revealed by epidural electrocorticography.

The present study investigated the topography of epidurally recorded middle latency components P1 and N1 evoked by spectrally dynamic stimuli (linearly frequency-modulated (FM) tones) with respect to the tonotopic structure of the right primary auditory cortex, field AI. Whereas the gross topography corresponded to the spectral content of the FM tones, specific tonotopic offsets were found between the potential distributions evoked by FM tones of different modulation direction (i.e. 'rising' vs. 'falling'). Potentials evoked by rising FM tones were located at tonotopic positions corresponding to higher frequencies compared with potentials evoked by falling FM tones. Data indicated that the magnitude of these offsets can be attributed to the local tonotopic resolution in AI and are not dependent on the modulation rate.

Acoustic Stimulation↗