11:42pm Sunday 31 May 2020

Research confounds previous view of how brain sorts sensory information in decision making

William Newsome

Our brains make such contextual decisions in a heartbeat. The mystery is how they do so.

In an article published Nov. 7 in Nature, a team of Stanford neuroscientists and engineers delve into this decision-making process and report some findings that confound the conventional wisdom.

Until now, neuroscientists believed that decisions of this sort involved two steps: One group of neurons would perform a gating function to ascertain whether motion or color was most relevant to the situation, and a second group of neurons would consider only the sensory input relevant to making a decision under the circumstances.

But in a study that combined brain recordings from trained monkeys and a sophisticated computer model based on that biological data, William Newsome, PhD, professor of neurobiology, and three other researchers discovered that the entire decision-making process may occur in a localized region of the prefrontal cortex.
Snap determinations

In this region of the brain, located in the frontal lobes just behind the forehead, they found that color and motion signals converged in a specific circuit of neurons. Based on their experimental evidence and computer simulations, the scientists hypothesized that these neurons act together to make two snap determinations: whether color or motion is the most relevant sensory input in the given situation, and what action to take as a result.
David Sussillo description of photo

This graphic shows how Stanford scientists believe one group of neurons in the prefrontal cortex “multitask” to make decisions — in this instance, choosing between color and motion.

“We were quite surprised,” said Newsome, who is also the Harman Family Provostial Professor and senior author of the paper.

He and lead author Valerio Mante, PhD, a former Stanford neurobiologist now at the University of Zurich and the Swiss Federal Institute of Technology, had begun the experiment expecting to find that the irrelevant signal, whether color or motion, would be gated out of the circuit long before the decision-making neurons went into action.

“What we saw instead was this complicated mix of signals that we could measure, but whose meaning and underlying mechanism we couldn’t understand,” said Newsome, who is director of the Stanford Neurosciences Institute. “These signals held information about the color and motion of the stimulus, which stimulus dimension was most relevant and the decision that the monkeys made. But the signals were profoundly mixed up at the single neuron level. We decided there was a lot more we needed to learn about these neurons, and that the key to unlocking the secret might lie in a population level analysis of the circuit activity.”

 To solve this brain puzzle, the neurobiologists began a cross-disciplinary collaboration with Krishna Shenoy, PhD, professor of electrical engineering and a co-author of the study, and David Sussillo, PhD, the study’s other lead author and a postdoctoral scholar in Shenoy’s lab.

Sussillo created a software model to simulate how these neurons worked. The idea was to build a model sophisticated enough to mimic the decision-making process, but easier to study than taking repeated electrical readings from a brain.
Becoming better

The general model architecture they used is called a recurrent neural network: a set of software modules designed to accept inputs and perform tasks similar to how biological neurons operate. The scientists designed this artificial neural network using computational techniques that enabled the software model to make itself more proficient at decision-making over time.

“We challenged the artificial system to solve a problem analogous to the one given to the monkeys,” Sussillo explained. “But we didn’t tell the neural network how to solve the problem.”

Krishna Shenoy

As a result, once the artificial network learned to solve the task, the scientists could study the model to develop inferences about how the biological neurons might be working.

The entire process was grounded in the biological experiments.

The neuroscientists trained two macaque monkeys to view a random-dot visual display that had two different features — motion and color. For any given presentation, the dots could move to the right or left, and the color could be red or green. The monkeys were taught to use sideways glances to answer two different questions depending on the currently instructed “rule” or context. Were there more red or green dots (ignore the motion)? Or, were the dots moving to the left or right (ignore the color)?

Eye-tracking instruments recorded the glances, or saccades, that the monkeys used to register their responses. Their answers were correlated with recordings of neuronal activity taken directly from an area in the prefrontal cortex known to control saccadic eye movements.

The neuroscientists collected 1,402 such experimental measurements. Each time the monkeys were asked one or the other question. The idea was to obtain brain recordings at the moment when the monkeys saw a visual cue that established the context (either the red/green or left/right question), and what decision the animal made regarding color or direction of motion.
Mirroring behavior

It was the puzzling mish-mash of signals in the brain recordings from these experiments that prompted the scientists to build the recurrent neural network as a way to rerun the experiment, in a simulated way, time and time again.

As the four researchers became confident that their software simulations accurately mirrored the actual biological behavior, they studied the model to learn exactly how it solved the task. This allowed them to form a hypothesis about what was occurring in that patch of neurons in the prefrontal cortex where perception and decision occurred.

“The idea is really very simple,” Sussillo said.

Their hypothesis revolves around two mathematical concepts: a line attractor and a selection vector.

The entire group of neurons being studied received sensory data about both the color and the motion of the dots. The line attractor is a mathematical representation for the amount of information that this group of neurons was getting about either of the relevant inputs, color or motion. The selection vector represented how the model responded when the experimenters flashed one of the two questions: red or green, left or right?

The model showed that when the question pertained to color, the selection vector directed the artificial neurons to accept color information while ignoring the irrelevant motion information. Color data became the line attractor. After a split second these neurons registered a decision, choosing the red or green answer based on the data they were supplied.

If the question was about motion, the selection vector directed motion information to the line attractor and the artificial neurons chose left or right.

“The amazing part is that a single neuronal circuit is doing all of this,” Sussillo said. “If our model is correct, then almost all neurons in this biological circuit appear to be contributing to almost all parts of the information selection and decision-making mechanism.”
‘Multitasking like crazy’

Newsome put it like this: “We think that all of these neurons are interested in everything that’s going on, but they’re interested to different degrees. They’re multitasking like crazy.”

Other researchers who were not directly involved commended the Stanford team.

“This is a spectacular example of excellent experimentation combined with clever data analysis and creative theoretical modeling,” said neuroscientist Larry Abbott, PhD, of Columbia University.

Christopher Harvey, PhD, a neurobiologist at Harvard Medical School, said the paper “provides major new hypotheses about the inner-workings of the prefrontal cortex, which is a brain area that has frequently been identified as significant for higher cognitive processes but whose mechanistic functioning has remained mysterious.”

The Stanford scientists are now designing an experiment to ascertain whether the interplay between selection vector and line attractor, which they deduced from their software model, can be measured in actual brain signals.

“The model predicts a very specific type of neural activity under very specific circumstances,” Sussillo said. “If we can stimulate the prefrontal cortex in the right way, and then measure this activity, we will have gone a long way to proving that the model mechanism is indeed what is happening in the biological circuit.”

The work was supported by the Howard Hughes Medical Institute, the Air Force Research Laboratory, a National Institutes of Health Director’s Pioneer Award and the Defense Advanced Research Projects Agency.

Tom Abate is the associate director of communications at the School of Engineering.

Stanford Medicine integrates research, medical education and patient care at its three institutions – Stanford University School of Medicine, Stanford Hospital & Clinics and Lucile Packard Children’s Hospital. For more information, please visit the Office of Communication & Public Affairs

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