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2012 investigation that pioneered this memory enhancement strategy

The 2012 study showed a significant increase in long-term memory in healthy sea snails

study's co-lead author and a research scientist

has developed a sophisticated mathematical model that can predict when the biochemical processes in the snail's brain are primed for learning

model is based on five training sessions scheduled at different time intervals ranging from 5 to 50 minutes

can generate 10,000 different schedules and identify the schedule most attuned to optimum learning

Memory is due to a change in the strength of the connections among neurons. In many diseases associated with memory deficits, the change is blocked

senior research scientist

simulated a brain disorder in a cell culture by taking sensory cells from the sea snails and blocking the activity of a gene that produces a memory protein

This resulted in a significant impairment in the strength of the neurons' connections, which is responsible for long-term memory

To mimic training sessions, cells were administered a chemical at intervals prescribed by the mathematical model

After five training sessions, which like the earlier study were at irregular intervals, the strength of the connections returned to near normal in the impaired cells

This methodology may apply to humans if we can identify the same biochemical processes in humans

results suggest a new strategy for treatments of cognitive impairment

Mathematical models might help design therapies that optimize the combination of training protocols with traditional drug treatments

Combining these two could enhance

effectiveness

while compensating at least in part for any limitations or undesirable side effects of drugs

two approaches are likely to be more effective together than separately and may have broad generalities in treating individuals with learning and memory deficits."

and how it can learn very similar tasks without interference between them.

The key

is that neurons are constantly changing their connections with other neurons

not all of the changes are functionally relevant - they simply allow the brain to explore many possible ways to execute a certain skill, such as a new tennis stroke

As the brain learns a new motor skill, neurons form circuits that can produce the desired output

according to this theory

As the brain explores different solutions, neurons can become specialized for specific tasks

brain is always trying to find the configurations that balance everything so you can do two tasks, or three tasks, or however many you're learning

Perfection is usually not achieved on the first try, so feedback from each effort helps the brain to find better solutions

complications arise when the brain is trying to learn many different skills at once

Because the same distributed network controls related motor tasks, new modifications to existing patterns can interfere with previously learned skills.

particularly tricky when you're learning very similar things

such as two different tennis strokes

computer chip,

instructions for each task would be stored in a different location on the chip.

the brain is not organized like a computer chip. Instead, it is massively parallel and highly connected - each neuron connects to, on average, about 10,000 other neurons

That connectivity offers an advantage, however, because it allows the brain to test out so many possible solutions to achieve combinations of tasks

neurons

have a very low signal to noise ratio, meaning that they receive about as much useless information as useful input from their neighbors

The constant changes in these connections,

researchers call hyperplasticity

balanced by another inherent trait of

Most models of neural activity don't include noise, but the MIT team says noise is a critical element of the brain's learning ability

This model helps to explain how the brain can learn new things without unlearning previously acquired skills

the paper shows is that, counterintuitively, if you have neural networks and they have a high level of random noise, that actually helps instead of hindering the stability problem

Without noise, the brain's hyperplasticity would overwrite existing memories too easily

low plasticity would not allow any new skills to be learned, because the tiny changes in connectivity would be drowned out by all of the inherent noise

The constantly changing connections explain why skills can be forgotten unless they are practiced often, especially if they overlap with other routinely performed tasks

skills such as riding a bicycle, which is not very similar to other common skills, are retained more easily

Once you've learned something, if it doesn't overlap or intersect with other skills, you will forget it but so slowly that it's essentially permanent

researchers are now investigating whether this type of model could also explain how the brain forms memories of events, as well as motor skills