The phrase "neurons that fire together wire together" was first conceived about 70 years ago in an attempt to explain the mechanisms behind the neural activity. It means that neuronal circuits need specific wiring to amplify sensory stimuli quickly and accurately.
However, computational neuroscience models showed that neurons wired together usually end up in an explosion of neural activity and become unstable, which is not observed in neurobiology. But now, Medical Xpress reported that a group of scientists describes the possible and straightforward mechanism that could help avoid this issue.
How Neurons Work: Computational Neuroscience Model of Hebbian Theory Brings Scientists Closer to Understanding Neural Activity
Hebbian Theory of How the Brain Works
When people see something familiar, the neurons work together in a split second to tell the person what the object is and connects it to past learnings. Perception works like that - wherein the brain amplifies or suppresses signals before spreading them to numerous brain areas.
The phrase "neurons that fire together wire together" was first coined by psychologist Donald Hebb in 1949 when he laid out his "assembly theory" to explain how the brain achieved this fast feat. Hebb said that those neurons that react to similar neurons tend to form "neuronal ensembles" or associations mediated through synapses, which are tiny spaces between neurons where they communicate.
The Hebbian theory postulates that some growth process or metabolic change happens when an axon of one neuron that is near enough to the axon of another neuron persistently takes part in firing it. The change occurs in either one or both cells that increase their efficiency. It could be summarized in his famous phrase: "neurons that fire together wire together."
However, computational neuroscience models showed that the Hebbian assembly often results in an explosion of neural activity, causing instability that is rarely observed in neurobiology.
According to SuperCamp, an example of this would be a child who had experienced a traumatic event or abuse. Any reminder of the event, even a slight physical contact of any kind, could be enough to trigger a fight-or-flight response.
Understanding Hebbian Theory in the Computational Neuroscience Perspective
Computational neuroscience expert Friedemann Zenke and former Ph.D. student Yue Kris Wu studied this discrepancy of the Hebbian theory from the perspective of computational neuroscience, Medical Xpress reported.
They identified an oxymoron at the center of the problem in which one end explains synaptic connections in the Hebbian ensemble should be strong to trigger instant memory recall. On the other hand, it also says that connections should not be too strong to prevent an explosion of neural activity to avoid subsequent stimulus processing.
In their study, titled "Nonlinear Transient Amplification in Recurrent Neural Networks With Short-Term Plasticity," published in eLife, researchers described a straightforward way that offers a solution to the oxymoron.
They call it the Nonlinear Transient Amplification, a mechanism with two phases starting with strong excitatory feedback that selectively amplifies stimuli above its threshold and subsequently triggers short-term plasticity. It will help stabilize the system and drop it into an inhibitory stabilized network site.
The findings of the study add to the information on how neural activity works and bring scientists a step closer to understanding the mechanism behind it.
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