Neural perturbations can have minimal effects even in structures that are causal for behavior

November 9, 2015

mattThis post is written by guest blogger, Matt Kaufman, a postdoc in my lab (left).

Last week our lab read a recent Neuron paper out of the Brody lab, by Kopec, Erlich, Brunton, Deisseroth & Brody, titled “Cortical and subcortical contributions to short-term memory for orienting movements.” This paper continues with that lab’s recent strategy of using optogenetics to briefly inactivate brain areas during decision making.

The experiments were straightforward. They trained rats to judge whether a click train was faster or slower than 50 Hz, then used optogenetics (eNpHR3.0) to inactivate either the Frontal Orienting Fields (FOF) or superior colliculus (SC) on one side of the brain at different points in the trial. This allowed Kopec et al. to see when these areas contributed to making the decision. The key experimental finding was that the rats’ decisions were most biased when either FOF or SC was silenced during the stimulus, a little less biased when silenced early in the subsequent delay, and less biased still when silenced late in the delay. Decisions were essentially unaffected when silencing was performed during the response period.

This finding is initially surprising, because tuning in the FOF increases over the course of the trial (as known from previous studies). They argue, however, that this seeming mismatch makes sense in the context of an attractor dynamics model (below). Since the evidence from the stimulus is not fluctuating in this task, the animal should be able to make his decision quickly. The increasing tuning might be due to attractor dynamics that amplify the tuning with time, while perturbations should mostly impact decisions before the neural activity has had time to settle in an attractor. Additional comparisons, including inactivating both areas together and comparing hard vs. easy trials, quantitatively fit their simple attractor model.

kopecFig

This study forms an interesting contrast with their paper from earlier this year, Hanks et al. 2015 in Nature. There, they took a similar approach but with a temporal integration task. In that task, the FOF was only critical at the end of such a stimulus. This again makes sense; you don’t want attractor dynamics if you need to integrate instead.

The question on many of our minds was: do these areas “really” exhibit attractor dynamics? On further reflection, though, this is a bit like asking whether the planets follow Newton’s laws. What I mean by that is: neurons, like orbiting planets, aren’t solving equations. Dynamical models, like Newton’s equations, are a mathematical description of how the system behaves over time. But if a model is an easy way to think about a system, and makes intuitive, useful predictions that hold up experimentally, then the model does useful work.

Many questions remain unanswered, of course. In terms of separation of function, are FOF and SC really doing the exact same thing? Are there other tasks where they would function very differently? Regarding dynamics, how does the system learn to produce these attractor dynamics? Since the FOF can apparently be trained to produce different dynamics in animals trained on different tasks, can it support either computation in an animal trained on both tasks? If so, how would it switch its dynamics? We’ll look forward to the next installment.

One Response to “Neural perturbations can have minimal effects even in structures that are causal for behavior”

  1. Those are the same questions that I have. Clearly, the FOF and the SC are not equivalent, so why do they appear so in this task? My guess is that in delayed response tasks (whether memory-guided or sensory guided) many many brain areas end up encoding the same thing. My guess is that we would see important differences between FOF and SC in a more dynamic environment (maybe a rat chasing down some insects for lunch).

    Based on the Mante, Sussillo, et al work, we know that it is possible to train a frontal network to have different dynamics in different contexts. In their case, it was color vs motion, but it seems that a similar network could learn to switch from integration dynamics to bistable dynamics. Of course, they had 100 neurons in their network and we only had 2!

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Fairhall lab

Computational neuroscience at the University of Washington

Pillow Lab Blog

Neural Coding and Computation Lab @ Princeton University

Churchland lab

Perceptual decision-making at Cold Spring Harbor

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