PubMed · 413060
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G Suarez-Kurtz, I Parker. Birefringence signals and calcium transients in skeletal muscle.. https://doi.org/10.1038/270746a0
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Slabs of cat parietal cortex with some 2 mm of underlying white matter were surgically isolated from the rest of the nervous system, without interference with the superficial blood supply. Wire micro-recording electrodes were inserted into the isolated cortex; bone, muscle and skin wounds were repaired and the animal allowed to recover from anaesthesia. The adequacy of surgical isolation was examined histologically 8--12 weeks after operation. Only one of the six preparations reported here showed surviving neural connections with the rest of the brain. Soon after operation, spontaneous bursts of neural activity appeared within the isolated area. These became more frequent until neural discharge was continuous but irregular. Our records were made from this time onwards. The interval distributions obtained from neurons within the isolated area did not differ significantly from log-normal curves. When the unrestrained animal fell asleep, there was no significant alteration in the model interval or geometric standard deviation of interval distributions recorded from cells in isolated cortex. The interval distributions of neurons in isolated cerebral cortex resembled those of neurons in the intact cortex of an alarmed animal. It is concluded that the reduction of modal interval that is shown by neurons in intact cortex when an animal falls asleep is probably due to the neural influence of infracortical structures.
Techniques are given to represent oscillating neural networks by asynchronous logical switching networks, and to analyze the oscillating networks using a directed graph called a state transition diagram. Consideration is restricted to network oscillators containing no rhythm determining pacemaker neurons, and no neurons exhibiting self-limiting properties such as post-inhibitory rebound or accumulating refractoriness. In the state transition diagrams, stable oscillations are associated with a particular geometric configuration called a cyclic attractor (the heavy cycle in Fig. 2). We show that given the network connectivity it is possible to predict autonomous dynamic behaviour, as well as behaviour following hyperpolarizing or depolarizing inputs to neurons of the network. Conversely, given information about patterns of firing activity during cycles and transients of neural networks, the network connectivity can be predicted. The theoretical techniques can be used to generate a census of network structures capable of generating stable oscillations. Several representative network oscillators are discussed in the context of previous theoretical and experimental studies of the structure of neural network oscillators. Although the number of theoretically possile network oscillators capable of generating sustained oscillations is very large, the techniques which are given should be useful in the design of experiments capable of distinguishing between equally plausible hypotheses.
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