AI News, How Machine Learning Is Helping Neuroscientists Crack Our Neural Code

How Machine Learning Is Helping Neuroscientists Crack Our Neural Code

Whenever you move your hand or finger or eyeball, the brain sends a signal to the relevant muscles containing the information that makes this movement possible.

This information is encoded in a special way that allows it to be transmitted through neurons and then actioned correctly by the relevant muscles.

Information travels along nerve fibers in the form of voltage spikes, or action potentials, that travel along nerve fibers.

They’ve trained macaque monkeys to move a screen cursor toward a target using a kind of computer mouse.In each test, the cursor and target appear on a screen at random locations, and the monkey has to move the cursor horizontally and vertically to reach the goal.

Having trained the animals, Glaser and co recorded the activity of dozens of neurons in the parts of their brains that control movement: the primary motor cortex, the dorsal premotor cortex, and the primary somatosensory cortex.

The job of a decoding algorithm is to determine the horizontal and vertical distance that the monkey moves the cursor in each test, using only the neural data.

This generally involves dividing the data set in two—80 percent being used to train the algorithm and the other 20 percent used to test it.

“These results suggest that modern machine-learning techniques should become the standard methodology for neural decoding.” In some ways, it’s not surprising that machine-learning techniques do so much better.

But Glaser and co deliberately reduced the amount of training data they fed to the algorithms and found the neural nets still outperformed the conventional techniques.

“Our networks have on the order of 100 thousand parameters, while common networks for image classification can have on the order of 100 million parameters,” they say.

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