People can play out a huge swath of mental tasks and change their social reactions based on external instructions and inward convictions. MIT scientists have discovered this mechanism using a mathematical framework known as dynamical systems analysis.
They wanted to get insights on the logic that governs the evolution of neural activity across large populations of neurons.
Mehrdad Jazayeri, the Robert A. Swanson Career Development Professor of Life Sciences said, “What makes this remarkable is that we can make adjustments to our behavior at a much faster timescale than the brain’s hardware can change. As it turns out, the same hardware can assume many different states, and the brain uses instructions and beliefs to select between those states.”
According to the previous study, the brain can control when it will initiate a movement by altering the speed at which patterns of neural activity evolve over time. This study suggests that the brain controls this speed flexibly based on two factors: external sensory inputs and adjustment of internal states, which correspond to knowledge about the rules of the task being performed.
Evan Remington, a McGovern Institute postdoc, is the lead author of the paper, which appears in the June 6 edition of Neuron. Other authors are former postdoc Devika Narain and MIT graduate student Eghbal Hosseini.
Scientists hypothesized that the brain similarly transforms instructions and beliefs to inputs and internal states that control the behavior of neural circuits.
To test this, the researchers recorded neural activity in the frontal cortex of animals trained to perform a flexible timing task called “ready, set, go.” In this task, the animal sees two visual flashes — “ready” and “set” — that are separated by an interval anywhere between 0.5 and 1 second, and initiates a movement — “go” — sometime after “set.” The animal has to initiate the movement such that the “set-go” interval is either the same as or 1.5 times the “ready-set” interval. The instruction for whether to use a multiplier of 1 or 1.5 is provided in each trial.
Neural signs recorded amid the “set-go” interim plainly conveyed data about both the multiplier and the measured length of the “ready set” interval, yet the idea of these portrayals appeared to be bewilderingly complex. To decipher the logic behind these representations, the specialists utilized the dynamical systems analysis framework. This examination is utilized as a part of the investigation of a wide range of physical systems, from simple electrical circuits to space shuttles.
The application of this approach to neural data in the “ready, set, go” task enabled Jazayeri and his colleagues to discover how the brain adjusts the inputs to and initial conditions of frontal cortex to control movement times flexible. A switch-like operation sets the input associated with the correct multiplier, and a dial-like operation adjusts the state of neurons based on the “ready-set” interval. These two complementary control strategies allow the same hardware to produce different behaviors.
David Sussillo, a research scientist at Google Brain and an adjunct professor at Stanford University, says a key to the study was the research team’s development of new mathematical tools to analyze huge amounts of data from neuron recordings, allowing the researchers to uncover how a large population of neurons can work together to perform mental operations related to timing and rhythm.”
“They have very rigorously brought the dynamical systems approach to the problem of timing.”
Jazayeri said, “We haven’t connected all the dots from behavioral flexibility to neurobiological details. But what we have done is to establish an algorithmic understanding based on the mathematics of dynamical systems that serve as a bridge between behavior and neurobiology.”
Scientists are now trying to find out what part of the brain sends information about the multiplier to the frontal cortex, and they also hope to study what happens in these neurons as they first learn tasks that require them to respond flexibly. They also hope to explore whether this type of model could help to explain the behavior of other parts of the brain that have to perform computations flexibly.