Music of the brain: each synapse has its own natural rhythm

October 4, 2011

In a discovery that challenges conventional wisdom on the brain mechanisms of learning, UCLA neuro-physicists have found there is an optimal brain “rhythm,” or frequency, for changing synaptic strength, and each synapse is tuned to a different optimal frequency for learning.

“Our work suggests that some problems with learning and memory are caused by synapses not being tuned to the right frequency,” said Mayank R. Mehta, an associate professor in UCLA’s departments of neurology, neurobiology, physics and astronomy. The findings may lead to possible new therapies for treating learning disabilities, the scientists say.

A change in the strength of a synapse in response to stimuli — known as synaptic plasticity — is induced through “spike trains,” a series of neural signals that occur with varying frequency and timing. Previous experiments demonstrated that stimulating neurons at a very high frequency (e.g., 100 spikes per second) strengthened the connecting synapse, while low-frequency stimulation (e.g., one spike per second) reduced synaptic strength.

These earlier experiments used hundreds of consecutive spikes in the very high-frequency range to induce plasticity. Yet when the brain is activated during real-life behavioral tasks, neurons fire only about 10 consecutive spikes, not several hundred. And they do so at a much lower frequency — typically in the 50 spikes-per-second range.

Until now, researchers had been unable to conduct experiments that simulated more naturally occurring levels. But Mehta and co-author Arvind Kumar, a former postdoctoral fellow in his lab, were able to obtain these measurements for the first time, using a sophisticated mathematical model they developed and validated with experimental data.

Synapses have their own preferred rhythms

Contrary to what was previously assumed, Mehta and Kumar found that stimulating the neurons at the highest frequencies was not the best way to increase synaptic strength. “To our surprise, we found that beyond the optimal frequency, synaptic strengthening actually declined as the frequencies got higher.”

The knowledge that a synapse has a preferred frequency for maximal learning led the researchers to compare optimal frequencies based on the location of the synapse on a neuron. Neurons are shaped like trees, with the nucleus being the base of the tree, the dendrites resembling the extensive branches and the synapses resembling the leaves on those branches.

The optimal frequency for inducing synaptic learning changed depending on where the synapse was located. The farther the synapse was from the neuron’s cell body, the higher its optimal frequency.

“Incredibly, when it comes to learning, the neuron behaves like a giant antenna, with different branches of dendrites tuned to different frequencies for maximal learning,” Mehta said.

How to sync synapses

The researchers found that not only does each synapse have a preferred frequency for achieving optimal learning, but for the best effect, the frequency needs to be perfectly rhythmic — timed at exact intervals. Even at the optimal frequency, if the rhythm was thrown off, synaptic learning was substantially diminished.

Their research also showed that once a synapse learns, its optimal frequency changes.

This learning-induced “detuning” process has important implications for treating disorders related to forgetting, such as post-traumatic stress disorder, the researchers said. Although much more research is needed, the findings raise the possibility that drugs could be developed to “retune” the brain rhythms of people with learning or memory disorders, or that learning for anyone could be improved if the optimal brain rhythm is delivered to each synapse.

“We already know there are drugs and electrical stimuli that can alter brain rhythms,” Mehta said. “Our findings suggest that we can use these tools to deliver the optimal brain rhythm to targeted connections to enhance learning.”

Ref.: Arvind Kumar and Mayank R. Mehta, Frequency-dependent changes in NMDAR-dependent synaptic plasticity, Frontiers in Computational Neuroscience, 2011; [DOI: 10.3389/fncom.2011.00038] [Open Access]