A new paper by Steve Gotts, myself, and Alex Martin has officially been published in the journal Cognitive Neuroscience:
Stephen J. Gotts, Carson C. Chow & Alex Martin (2012): Repetition priming and repetition suppression: Multiple mechanisms in need of testing, Cognitive Neuroscience, 3:3-4, 250-259 [PDF]
This paper is a review of the topic but is partially based on the PhD thesis work of Steve Gotts when we were both in Pittsburgh over a decade ago. Steve was a CNBC graduate student at Carnegie Mellon University and came to visit me one day to tell me about his research project to reconcile the psychological phenomenon of repetition priming with a neurophysiological phenomenon called repetition suppression. It is well known that performance improves when you repeat a task. For example, you will respond faster to words on a random list if you have seen the word before. This is called repetition priming. The priming effect can occur over time scales as short as a few seconds to your life time. Steve was focused on the short time effect. A naive explanation for why you would respond faster to priming is that the pool of neurons that code for the word become slightly more active so when the word reappears they fire more readily. This hypothesis could only be tested when electrophysiological recordings of cells in awake behaving monkeys and functional magnetic resonance imaging data in humans finally became available in the mid-nineties. As is often the case in science, the opposite was observed. Neural responses actually decreased and this was called repetition suppression. So an interesting question arose: How do you get priming with suppression? Steve had a hypothesis and it involved work I had done so he came to see if I wanted to collaborate.
I joined the math department at Pitt in the fall of 1998 (the webpage has a nice picture of Bard Ermentrout, Rodica Curtu and Pranay Goel standing at a white board). I had just come from doing a post doc with Nancy Kopell at BU. At that time, the computational neuroscience community was interested in how a population of spiking neurons would become synchronous. The history of synchrony and coupled oscillators is long with many threads but I got into the game because of the weekly meetings Nancy organized at BU, which we dubbed “N-group”. People from all over the Boston area would participate. It was quite exciting at that time. One day Xiao Jing Wang, who was at Brandeis at the time, came to give a seminar on his joint work with Gyorgy Buzsaki on gamma oscillations in the hippocampus, which resulted in this highly cited paper. What the paper was really about was how inhibition could induce synchrony in a network with heterogeneous connections. It had already been shown by a number of people that a network with inhibitory synapses could synchronize a network of spiking neurons. This was somewhat counter intuitive because the conventional wisdom was that inhibition would lead to anti-synchrony. The key ingredient was that the inhibition had to be slow. Xiao Jing argued from his simulations that the hippocampus had a sweet spot for synchronization for the gamma band (i.e. frequencies around 40Hz). I was highly intrigued by his result and spent the next two years trying to understand the simulations mathematically. This resulted in four papers:
C.C. Chow, J.A. White, J. Ritt, and N. Kopell, `Frequency control in synchronized networks of inhibitory neurons’, J. Comp. Neurosci. 5, 407-420 (1998). [PDF]
J.A. White, C.C. Chow, J. Ritt, C. Soto-Trevino, and N. Kopell, `Synchronization and oscillatory dynamics in heterogeneous, mutually inhibited neurons’, J. Comp. Neurosci. 5, 5-16 (1998). [PDF]
C.C. Chow, `Phase-locking in weakly heterogeneous neuronal networks’, Physica D 118, 343-370 (1998). [PDF]
C.C. Chow and N. Kopell, `Dynamics of spiking neurons with electrical coupling’, Neural Comp. 12, 1643-1678 (2000). [PDF]
In a nutshell, these papers showed that in a heterogeneous network, neurons will tend to synchronize around the time scale of the synaptic inhibition, which in the case of the inhibitory neurotransmitter receptor GABA_A is around 25 ms or 40 Hz. When the firing frequency is too high the neurons tend to fire asynchronously and when the frequency is too slow, neurons tend to stop firing all together.
Steve read my papers (and practically everything else) and thought that this might be the resolution of his question. Now, it had also been known for a while that when neurons fire they tend to slow down. This is due to both spike-frequency adaptation and synaptic depression, so repetition suppression is not entirely surprising since when neurons are stimulated they will tend to fire slower. What is surprising is that slowing down makes you respond faster. Steve thought that maybe suppression synchronized neurons and made them more effective in getting downstream neurons to fire. In essence, what he needed to find was a mechanism that increases the gain of a neuron for a decrease in input and synchrony was a solution. I helped him work out some technical details and he wrote a very nice thesis showing how this could work and match the data. He then went on to work with Bob Desimone and Alex Martin at NIH. However, we never wrote the theoretical paper from his thesis because of a critique that we never got around to answering. The issue was that if a lowering of network frequency can elicit priming then why does a reduction in contrast in the primed stimulus, which also reduces network frequency, not do the same? This came up after Steve had left and I turned my attention to other things. The answer is probably because not all frequency reductions are equal. A reduction in contrast lowers the total input to the early part of the visual system while synaptic depression will have the largest effect on the most active neurons. The ensuing dynamics will likely be different but we never had the time to fully flesh this out. Although, I always wanted to get back to this, the project sat idle for me for about eight years until Steve sent me an email one day saying that he’s writing a review with Alex on the topic and wanted to know if I wanted to be included. I was delighted. The paper covers all the current theories for priming and suppression and is accompanied by commentaries from many of the key players in the field. I’ve just covered a small part of the many interesting issues brought up in the review.