Self-organized and self-sustained ensemble activity patterns in simulation of mouse primary motor cortex.

The idea of self-organized signal processing in the cerebral cortex has become a focus of research since Beggs and Plentz reported avalanches in local field potential recordings from organotypic cultures and acute slices of rat somatosensory cortex. How the cortex intrinsically organizes signals remains unknown. A current hypothesis was proposed by the condensed matter physicists Bak, Tang, and Wiesenfeld when they conjectured that if neuronal avalanche activity followed inverse power law distributions, then brain activity may be set around phase transitions within self-organized signals.

Decreased cellular excitability of pyramidal tract neurons in primary motor cortex leads to paradoxically increased network activity in simulated parkinsonian motor cortex.

Decreased excitability of pyramidal tract neurons in layer 5B (PT5B) of primary motor cortex (M1) has recently been shown in a dopamine-depleted mouse model of parkinsonism. We hypothesized that decreased PT5B neuron excitability would substantially disrupt oscillatory and non-oscillatory firing patterns of neurons in layer 5 (L5) of primary motor cortex (M1). To test this hypothesis, we performed computer simulations using a previously validated computer model of mouse M1.

Mechanism of an Intrinsic Oscillation in Rat Geniculate Interneurons.

Depolarizing current injections produced a rhythmic bursting of action potentials - a bursting oscillation - in a set of local interneurons in the lateral geniculate nucleus (LGN) of rats. The current dynamics underlying this firing pattern have not been determined, though this cell type constitutes an important cellular component of thalamocortical circuitry, and contributes to both pathologic and non-pathologic brain states. We thus investigated the source of the bursting oscillation using pharmacological manipulations in LGN slices and .

GPT-4 Performance for Neurologic Localization.

Background And Objectives: In health care, large language models such as Generative Pretrained Transformers (GPTs), trained on extensive text datasets, have potential applications in reducing health care disparities across regions and populations. Previous software developed for lesion localization has been limited in scope. This study aims to evaluate the capability of GPT-4 for lesion localization based on clinical presentation.