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Neir Eshel, an MD-PhD graduate from Harvard Medical School and a former member of the Uchida Lab, was named the grand prize winner of an essay contest sponsored by the journal Science, the Science for Life Laboratory (SciLifeLab), and the American Association for the Advancement of Science (AAAS). Eshel was also the 2016 winner of the Larry Katz Memorial Prize Lecture contest.

Eshel’s essay, which will be published in Science, won him $30,000 and a trip with three other finalists to the Nobel Prize ceremonies in Stockholm, Sweden in December.

“I get to watch the Nobel lecture in Medicine and the Nobel prize ceremony; give various talks to scientists and high school and college students; tour labs in Stockholm and Uppsala; and receive trophies in the room where the Nobel prizes were originally given,” said Eshel. “All in all, a once-in-a-lifetime experience!”

The research that provided material for Eshel’s essay was conducted while he was studying animal learning theory at Harvard under Professor Naoshige Uchida. His research with Professor Uchida was published in the journals Trends in Cognitive Science, Neuron, Nature, and Nature Neuroscience. For a complete list of Eshel’s published work, see his PubMed page.

“Neir came to my lab with extensive backgrounds in human experiments,” Uchida said. “His transition to mouse experiments did not come easily, but he made great efforts to learn new techniques in the lab. I greatly appreciate his dedication to science and critical thinking that made his achievement possible.”

The essay discusses the role of dopamine in the brain’s ability to predict rewards for certain behaviors. Dopamine is already known to be part of the brain’s processing of feelings like want and pleasure, but its role in our ability to predict consequences for our actions is still under investigation.

“Broadly, I’m interested in how we learn about rewards and punishments, how we make decisions based on this knowledge, and how these systems break down in neuropsychiatric disease,” Eshel said. “In particular, I’m fascinated by the concept that we learn by constantly making predictions and comparing those predictions to reality. I wanted to understand the precise brain mechanisms that let us refine our predictions over time. That led me to dopamine neurons and to the Uchida lab.”
“Dopamine reward prediction error has been studied for more than 20 years, but it remains unknown how such signals are computed in the first place,” Uchida said. “[Neir’s] study addressed the very core of this question. Although we do not still understand the full picture of the underlying mechanisms, Neir’s work provides crucial insights into an important aspect in the question.”

Besides the ceremony itself, Eshel said there is much for him to look forward to in Sweden.

“It’s impossible for me to pick just one thing I’m excited to do in Stockholm,” Eshel said. “I’m also giving a talk to high school students, where I’ll do my best to excite them about science; quite a responsibility! Oh, and I can’t wait to eat Swedish meatballs in Sweden!”

Read more on AAAS


The winning essay:

Dopamine and the Neural Circuit Underlying Learning

We are all prediction-making machines. Granted, our predictions are often wrong—as the old saying goes, “It’s tough to make predictions—especially about the future.” But even wrong predictions serve a purpose: they help us learn. Each time we make a choice, we predict the outcome of that choice. When the outcome matches our prediction, there is no need to learn. When the outcome is unexpected, however, we update our predictions, hoping to do better next time.

The idea that we learn by comparing predictions to reality has been a mainstay of animal learning theory since the 1950s (1–3), and is one of the foundations of machine learning (4). Remarkably, the brain has evolved a simple mechanism to make precisely these comparisons. In the 1990s, Wolfram Schultz and colleagues found that dopamine neurons in the midbrains of monkeys showed a curious response to reward (5). When the monkeys received an unexpected reward (in this case, a squirt of juice), dopamine neurons fired a burst of action potentials. When that same reward was expected, the neurons no longer fired. And if Schultz et al. played a trick on the monkeys, making them expect reward but ultimately withholding that reward, the dopamine neurons dipped below their normal firing rate (6).  Together, these results demonstrated that dopamine neurons signal prediction error, or the difference between actual and predicted value. If an outcome is better than predicted, dopamine neurons fire; if an outcome is the same as predicted, there is no change in firing; and if an outcome is worse than predicted, dopamine neurons dip below baseline. The level of dopamine release then informs the rest of the brain when a prediction needs to be fixed, and in what direction.

This basic finding — that dopamine neurons signal errors in  reward prediction— revolutionized the study of learning in the brain, supplying a powerful, mechanistic model for how reinforcement affects behavior (7). Despite extensive study, however, little is known about how dopamine neurons actually calculate prediction error. What inputs do dopamine neurons combine and how do they combine them? To answer these questions, we merged molecular biology, electrophysiology, and computational analysis.

We focused on the ventral tegmental area (VTA), a small brainstem nucleus that produces dopamine. Although a majority of neurons in this region are dopamine neurons, a substantial minority use the inhibitory neurotransmitter GABA instead. A recent study from our lab showed that these GABA neurons do not signal prediction error; rather, they encode reward expectation (8).

This finding raised a fascinating question: could dopamine neurons use the GABA expectation signal to calculate prediction error? To find out, we used a virus to introduce the light-sensitive protein channelrhodopsin (ChR2) selectively in VTA GABA neurons. This enabled us to control the activity of VTA GABA neurons with light, a technique called optogenetics. We then implanted a set of electrodes surrounding a fiber optic cable into the VTA. Once the mice recovered from surgery, we recorded from the VTA and manipulated VTA GABA neuron activity, all while the mice performed simple learning tasks.

Optogenetics offers formidable precision, but there are potential pitfalls. In particular, it is easy to manipulate neural activity in ways that never occur in real life, producing results that are difficult to interpret. Our system avoided this pitfall because we knew how VTA GABA neurons normally fire in our task. By recording during the manipulation, we made sure to mimic natural firing patterns.

When we stimulated VTA GABA neurons, dopamine neurons responded to unexpected rewards as if they were expected (9). Conversely, when we inhibited VTA GABA neurons, dopamine neurons responded to expected rewards as if they were unexpected. Finally, if we manipulated VTA GABA neurons simultaneously on both sides of the brain, we even changed the animals’ behavior. After training mice to expect a certain size of reward, we artificially increased the expectation level by stimulating VTA GABA neurons during the anticipation period. The reward level, meanwhile, stayed the same. After several trials in which expectation exceeded reality, the disappointed mice stopped licking in anticipation of reward. When we turned off the laser, their behavior slowly returned to normal. We concluded that VTA GABA neurons convey to dopamine neurons how much reward to expect. In short, they put the ‘prediction’ in ‘prediction error’.

The VTA GABA expectation signal is only part of the puzzle. Another vital question is how dopamine neurons actually use this input. What arithmetic do they perform? Again, we used molecular techniques to ‘tag’ neurons with ChR2, but this time, we tagged dopamine neurons instead of GABA neurons. In each recording session, we shined pulses of light and identified neurons as dopaminergic if they responded reliably to each pulse. This ensured that the recorded neurons were indeed dopamine neurons, eliminating the need for other, less accurate identification methods (10).

Using insights from the sensory literature (11), we designed a task to assess the input-output function of identified dopamine neurons and determine how expectation transforms this function. We found that dopamine neurons use simple subtraction (9). Although this arithmetic is assumed in computational models, it is remarkably rare in the brain; division is much more common, as exemplified by gain control. However, subtraction is an ideal calculation because it allows for consistent results over a wide range of rewards. Moreover, we found that individual dopamine neurons calculated prediction error in exactly the same way (12). Each neuron produced an identical signal, just scaled up or down. In fact, even on single trials, individual neurons fluctuated together around their mean activity. Such uniformity greatly simplifies information coding, allowing prediction errors to be broadcasted robustly and coherently throughout the brain—a prerequisite for any learning signal.  Presumably, target neurons rely on this consistent prediction error signal to guide optimal behavior.

Our work begins to uncover both the arithmetic and the local circuitry underlying dopamine prediction errors. The method of evidence accumulation, the inputs that signal reward, and the biophysics underlying subtraction remain to be discovered—prime material for fresh predictions and unforeseen rewards.

1.     R. R. Bush, F. Mosteller, A mathematical model for simple learning. Psychol Rev. 58, 313–323 (1951).
2.     L. Kamin, in Fundamental issues in associative learning (1969), pp. 42–64.
3.     R. A. Rescorla, A. R. Wagner, in Classical conditioning II: current research and theory, A. Black, W. Prokasy, Eds. (1972), pp. 64–99.
4.     R. S. Sutton, A. G. Barto, Reinforcement learning: An introduction (Cambridge Univ Press, 1998), vol. 1.
5.     W. Schultz, P. Dayan, P. R. Montague, A neural substrate of prediction and reward. Science. 275, 1593–1599 (1997).
6.     J. R. Hollerman, L. Tremblay, W. Schultz, Influence of reward expectation on behavior-related neuronal activity in primate striatum. J. Neurophysiol. 80, 947–963 (1998).
7.     W. Schultz, Updating dopamine reward signals. Curr. Opin. Neurobiol. 23, 229–238 (2013).
8.     J. Y. Cohen, S. Haesler, L. Vong, B. B. Lowell, N. Uchida, Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature. 482, 85–88 (2012).
9.     N. Eshel et al., Arithmetic and local circuitry underlying dopamine prediction errors. Nature. 525, 243–246 (2015).
10.     E. B. Margolis, H. Lock, G. O. Hjelmstad, H. L. Fields, The ventral tegmental area revisited: is there an electrophysiological marker for dopaminergic neurons? J. Physiol. (Lond.). 577, 907–924 (2006).
11.     S. R. Olsen, V. Bhandawat, R. I. Wilson, Divisive normalization in olfactory population codes. Neuron. 66, 287–299 (2010).
12.     N. Eshel, J. Tian, M. Bukwich, N. Uchida, Dopamine neurons share common response function for reward prediction error. Nat. Neurosci. 19, 479–486 (2016).

Neir Eshel

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