How is the information needed to support familiarity judgements stored so rapidly and held so reliably for such a long time? Most theoretical work aimed at understanding the brain's one-shot learning mechanisms has been based on drastically simplified neuron models which omit any representation of the most visually prominent features of neurons-their extensive dendritic arbors. Humans can effortlessly recognize a pattern as familiar even after a single presentation and a long delay, and our capacity to do so even with complex stimuli such as images has been called 'almost limitless'. Our model provides the first normative theory that explains how dendrites increase the brain’s capacity for online learning predicts which combinations of parameter settings we should expect to find in the brain under normal operating conditions leads to novel interpretations of an array of existing experimental results and provides a tool for understanding which changes associated with neurological disorders, aging, or stress are most likely to produce memory deficits-knowledge that could eventually help in the design of improved clinical treatments for memory loss. Thus, in comparison to entire neurons, dendrites increase storage capacity by providing a larger number of better-sized learning units. We show that over a several-fold range of both of these parameters, and over multiple orders-of-magnitude of memory size, capacity is maximized when dendrites contain a few hundred synapses-roughly the natural number found in memory-related areas of the brain. We used the model to determine what dendrite size maximizes storage capacity under varying assumptions about pattern density and noise level. Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic, network, pattern, and task-related parameters. However, little is known about the computational strategies or neural mechanisms that underlie the brain's ability to perform this type of 'online' learning. The samples thickness was about 10 times smaller than the average diameter of human hair.In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. In their paper, Operando Measurements of Dendrite-Induced Stresses in Ceramic Electrolytes using Photoelasticity, they managed to overcome challenges associated with measurements of easy to break, very tiny solid electrolyte samples. Georgia Tech Professor Christos Athanasiou and the multidisciplinary team used photoelasticity to measure the stress on batteries caused during the battery cycle. Dendrites pierce through solid electrolytes, eventually crossing from one electrode to the other and shorting out the solid-state battery cell. Scientists have long thought that stresses can build up around dendrites, thin metallic projects that can ultimately short out solid-electrolyte batteries, but they haven’t been precisely measured.Ī team of scientists at Georgia Tech, Brown University, Nanyang Technological University, and MIT have measured the mechanical stresses that develop in dendrites – solving a long-standing hypothesis that high stresses can be developed around dendrites. Researchers have documented for the first time the stresses that build up around solid-state battery electrolytes, helping set the stage for the development of improved and more efficient batteries.

Dendrite stories