The fractal brain, from a single neuron’s perspective

Summary: Researchers say measuring signals from a single neuron may be as good as capturing information from many neurons at once.

Source: WUSTL

Hacking into brain signals may be more straightforward than once thought.

Physicists studying the brain at Washington University in St. Louis have shown how measuring signals from a single neuron may be as good as capturing information from many neurons at once using big, expensive arrays of electrodes.

The new work continues the discussion about how the brain seems to function in a “critical” state, operating at the cusp between two phases of activity in a way that offers advantages for information transmission and processing. The research is reported June 12 in the Journal of Neuroscience.

What information single neurons receive about general neural circuit activity is a fundamental question of neuroscience. Researchers in the laboratory of Ralf Wessel, professor of physics in Arts & Sciences, have been exploring sensory information processing in the brain for years, using advanced neurotechnology and physics-inspired data analysis.

“We know that in critical systems you can zoom in or out really far, and get the same statistical patterns. This property is called scale-freeness — or fractalness — and criticality may explain the origins of widely observed fractal activity in the brain,” said James K. Johnson, first author of the paper and a graduate student in the Wessel laboratory.

A drawing of a brain made up of neurons

Physicists studying the brain at Washington University in St. Louis have shown how measuring signals from a single neuron may be as good as capturing information from many neurons at once using big, expensive arrays of electrodes. The image is credited to Wessel laboratory.

For this new work, the researchers wanted to zoom all the way down. Evidence for criticality has been observed at all larger scales, they explained.

“The scale of the single cell was the last frontier,” Johnson said. “We cheated a bit, though. The statistical patterns used to evince criticality in the brain are called neuronal avalanches. Essentially, it’s just a spurt of ‘spiking,’ or messaging between neurons.”

“We cannot know if two randomly selected neurons are directly connected — and (even) if we could, spiking between those two is so rare that we would need hours of recordings from those two neurons,” Johnson said. “So instead, we ignored spiking and looked to see what neuronal avalanches look like from the neuron’s perspective.”

Single-cell recordings go back at least 70 years, but have been eclipsed by new ways of recording many neurons at once. The Washington University researchers updated and mastered a previously used technique to record electrochemical input fluctuations from inside a single neuron.

By placing a tiny glass tube containing an electrode on the cell body — actually breaking into the cell, and tricking it into thinking the tube was a piece of its cell membrane — the researchers were able to record voltage changes caused by ion exchange. The method itself is not new, but the team was able to record data inside a living turtle brain for much longer than normal (more than 30 minutes).

“When our cell receives inputs, it looks like ‘blips’ or ‘piles of blips’ in our recordings,” Johnson said. “Usually, the neuroscience community focuses on the average value or some summative measure, and fluctuations are often modeled as pure noise. We did something new. We did the same statistical analysis on the precise geometry of the ‘blips’ that one normally does on neuronal avalanches when testing for criticality.”


Johnson prepared this 22-minute explainer video about the new paper in the Journal of Neuroscience. The new work demonstrates how synaptic activity is scale free and shows that synaptic avalanches agree with neuronal avalanches, among other implications. (Video courtesy: Wessel laboratory, Washington University).

When run through an exhaustive battery of tests, the single-cell data that the researchers collected was consistent with systems at their critical point almost as often as when using data from large arrays.

“Being at the critical point offers many advantages for information transmission and processing that may underlie the resilience, adaptability and variability of brain function,” Johnson said.

“The neurons of your primary visual cortex never fire in the same sequence twice, yet you can see the same thing twice. In a critical system, this is no mystery; it’s completely normal and no complicated model is needed to explain it,” Johnson said.

The new work also advances the understanding of physics theories related to emergent properties and coordination between neurons.

“If our research community is right, then the brain will be the first commonly found natural system to exhibit self-organized criticality,” Johnson said.

About this neuroscience research article

Source:
WUSTL
Media Contacts:
Talia Ogliore – WUSTL
Image Source:
The image is credited to Wessel laboratory.

Original Research: Closed access
“Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality”. James K. Johnson, Nathaniel C. Wright, Jì Xià and Ralf Wessel.
Journal of Neuroscience. doi:10.1523/JNEUROSCI.3163-18.2019

Abstract

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential (Vm) fluctuations are driven by the convergence of synaptic inputs from a diverse cross-section of upstream neurons. Furthermore, neural activity is often scale-free, implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scale-freeness support the hypothesis that single Vm recordings carry useful information about high-dimensional cortical activity. Conveniently, the theory of “critical branching networks” (one purported explanation for scale-freeness) provides testable predictions about scale-free measurements that are readily applied to Vm fluctuations. To investigate, we obtained whole-cell current-clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in Vm below the firing threshold and analyzed them by adapting the definition of “neuronal avalanches” (i.e., spurts of population spiking). The Vm fluctuations which we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays described previously (Shew et al., 2015). Simultaneously recorded single-unit local field potential did not provide a good match, demonstrating the specific utility of Vm. Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence of critical phenomena while also establishing subthreshold pyramidal neuron Vm fluctuations as an informative gauge of high-dimensional cortical population activity.

SIGNIFICANCE STATEMENT
The relationship between membrane potential (Vm) dynamics of single neurons and population dynamics is indispensable to understanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from single-neuron whole-cell recordings with population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic Vm scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain’s own subsampling method (convergence of synaptic inputs) while extending the range of fundamental evidence for critical phenomena in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, Vm fluctuations.

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