Model shows that the speed neurons fire impacts their ability to
synchronize

Date:
September 8, 2020
Source:
Okinawa Institute of Science and Technology (OIST) Graduate
University
Summary:
Research has shown for the first time that a computer model can
replicate and explain a unique property displayed by a crucial
brain cell. Their findings shed light on how groups of neurons
can self-organize by synchronizing when they fire fast.



FULL STORY ========================================================================== Research conducted by the Computational Neuroscience Unit at the Okinawa Institute of Science and Technology Graduate University (OIST) has shown
for the first time that a computer model can replicate and explain a
unique property displayed by a crucial brain cell. Their findings,
published today in eLife, shed light on how groups of neurons can
self-organize by synchronizing when they fire fast.


==========================================================================
The model focuses on Purkinje neurons, which are found within the
cerebellum.

This dense region of the hindbrain receives inputs from the body and
other areas of the brain in order to fine-tune the accuracy and timing
of movement, among other tasks.

"Purkinje cells are an attractive target for computational modeling as
there has always been a lot of experimental data to draw from," said
Professor Erik De Schutter, who leads the Computation Neuroscience
Unit. "But a few years ago, experimental research into these neurons
uncovered a strange behavior that couldn't be replicated in any existing models." These studies showed that the firing rate of a Purkinje neuron affected how it reacted to signals fired from other neighboring neurons.

The rate at which a neuron fires electrical signals is one of the most
crucial means of transmitting information to other neurons. Spikes,
or action potentials, follow an "all or nothing" principle -- either
they occur, or they don't -- but the size of the electrical signal
never changes, only the frequency. The stronger the input to a neuron,
the quicker that neuron fires.

But neurons don't fire in an independent manner. "Neurons are connected
and entangled with many other neurons that are also transmitting
electrical signals. These spikes can perturb neighboring neurons through synaptic connections and alter their firing pattern," explained Prof. De Schutter.



========================================================================== Interestingly, when a Purkinje cell fires slowly, spikes from connected
cells have little effect on the neuron's spiking. But, when the firing
rate is high, the impact of input spikes grows and makes the Purkinje
cell fire earlier.

"The existing models could not replicate this behavior and therefore
could not explain why this happened. Although the models were good at
mimicking spikes, they lacked data about how the neurons acted in the
intervals between spikes," Prof. De Schutter said. "It was clear that
a newer model including more data was needed." Testing a new model Fortunately, Prof. De Schutter's unit had just finished developing
an updated model, an immense task primarily undertaken by now former postdoctoral researcher, Dr. Yunliang Zang.

Once completed, the team found that for the first time, the new model
was able to replicate the unique firing-rate dependent behavior.



==========================================================================
In the model, they saw that in the interval between spikes, the Purkinje neuron's membrane voltage in slowly firing neurons was much lower than
the rapidly firing ones.

"In order to trigger a new spike, the membrane voltage has to be high
enough to reach a threshold. When the neurons fire at a high rate,
their higher membrane voltage makes it easier for perturbing inputs,
which slightly increase the membrane voltage, to cross this threshold
and cause a new spike," explained Prof. De Schutter.

The researchers found that these differences in the membrane voltage
between fast and slow firing neurons were because of the specific types
of potassium ion channels in Purkinje neurons.

"The previous models were developed with only the generic types of
potassium channels that we knew about. But the new model is much more
detailed and complex, including data about many Purkinje cell-specific
types of potassium channels. So that's why this unique behavior could
finally be replicated and understood," said Prof. De Schutter.

The key to synchronization The researchers then decided to use their
model to explore the effects of this behavior on a larger-scale, across
a network of Purkinje neurons. They found that at high firing rates,
the neurons started to loosely synchronize and fire together at the
same time. Then when the firing rate slowed down, this coordination was
quickly lost.

Using a simpler, mathematical model, Dr. Sungho Hong, a group leader
in the unit, then confirmed this link was due to the difference in how
fast and slow firing Purkinje neurons responded to spikes from connected neurons.

"This makes intuitive sense," said Prof. De Schutter. He explained that
for neurons to be able to sync up, they need to be able to adapt their
firing rate in response to inputs to the cerebellum. "So this syncing
with other spikes only occurs when Purkinje neurons are firing rapidly,"
he added.

The role of synchrony is still controversial in neuroscience, with its
exact function remaining poorly understood. But many researchers believe
that synchronization of neural activity plays a role in cognitive
processes, allowing communication between distant regions of the
brain. For Purkinje neurons, they allow strong and timely signals to be
sent out, which experimental studies have suggested could be important
for initiating movement.

"This is the first time that research has explored whether the rate at
which neurons fire affects their ability to synchronize and explains how
these assemblies of synchronized neurons quickly appear and disappear,"
said Prof. De Schutter. "We may find that other circuits in the brain also
rely on this rate- dependent mechanism." The team now plans to continue
using the model to probe deeper into how these brain cells function, both individually and as a network. And, as technology develops and computing
power strengthens, Prof. De Schutter has an ultimate life ambition.

"My goal is to build the most complex and realistic model of a neuron possible," said Prof. De Schutter. "OIST has the resources and computing
power to do that, to carry out really fun science that pushes the boundary
of what's possible. Only by delving into deeper and deeper detail in
neurons, can we really start to better understand what's going on."

========================================================================== Story Source: Materials provided by Okinawa_Institute_of_Science_and_Technology_(OIST)
Graduate_University. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Yunliang Zang, Sungho Hong, Erik De Schutter. Firing rate-dependent
phase
responses of Purkinje cells support transient oscillations. eLife,
2020; 9 DOI: 10.7554/eLife.60692 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200908131044.htm

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