A long-standing controversy in neuroscience centers on a simple question: how do neurons in the brain share information? Sure, it’s well-known that neurons are wired together by synapses, and that when one of them fires, it sends an electrical signal to other neurons connected to it. But that simple model leaves a lot of unanswered questions—for example, where exactly in neurons’ firing is information stored? Resolving these questions could help us understand the physical nature of a thought.
Two theories attempt to explain how neurons encode information, the rate code model and the temporal code model. In the rate code model, the rate at which neurons fire is the key feature: Count the number of spikes in a certain time interval, and that number gives you the information. In the temporal code model, the relative timing between firings matters more—information is stored in the specific pattern of intervals between spikes, vaguely like Morse code. But the temporal code model faces a difficult question: if a gap is “longer” or “shorter,” it has to be longer or shorter relative to something. For the temporal code model to work, the brain needs to have a kind of metronome, a steady beat to allow the gaps between firings to hold meaning.
Every computer has an internal clock to synchronize its activities across different chips. If the temporal code model is right, the brain should have something similar. Some neuroscientists posit that the clock is in the gamma rhythm, a semi-regular oscillation of brain waves. But it doesn’t stay consistent. It can speed up or slow down depending on what a person experiences, such as a bright light. Such a fickle clock didn’t seem like the full story for how neurons synchronize their signals, leading to ardent disagreements in the field about whether the gamma rhythm meant anything at all.
That’s why Christopher Moore and Hyeyoung Shin, researchers at Brown University who study gamma rhythms, were surprised when they found a type of neuron that not only fired at a relatively steady rate, but maintained that rate regardless of stimulus.
“That right away suggests there’s something interesting going on here that we just haven’t seen before,” Moore says. “Something big is lurking in there.” Moore and Shin’s results were published in July in the journal Neuron.
Moore’s group had previously shown that artificially driving natural gamma rhythms in mice helped the rodents detect weaker touches of their whiskers; their ability to detect these faint touches is interpreted as a proxy for how attentive they are. In this recent study, Shin was again ever-so-faintly touching mice on the whiskers, but this time she was taking a closer look at the role of inhibitory neurons in the process. Inhibitory neurons regulate the activity of the neurons around them, making sure the brain doesn’t have any runaway bursts of electricity. They also contribute to the gamma rhythms in the brain.
She found three types of inhibitory neurons: one type whose firing spiked at a whisker touch, one type that seemed to fire randomly, and one type that spiked with a surprising regularity at a gamma rhythm frequency.
For Vikaas Sohal, a neuroscientist at the University of California, San Francisco, who was not involved in the work, the discovery of these cells might help the field move away from conflicts about gamma rhythms.
“I think it’s really exciting,” Sohal says. Neuroscientists have considered the purpose of gamma rhythms in a very general way, he says, but the discovery of these neurons suggests they may have more specific functions. “It really expands the way we think about gamma oscillations, and that’s important because gamma oscillations have been a very controversial topic.”