Distinct activity patterns in neuromodulatory centers are associated with differential modulation of cortical low and high gamma oscillations

No clue if this means that the current theory on recovery needs to be changed. 'Neurons that fire together, wire together.' Ask your doctor and not politely. 'How the fuck are YOU going to get me 100% recovered?' If 'All strokes are different, all stroke recoveries are different' comes out of her/his mouth, you have a stroke doctor that is useless, fire them. Another sign of a useless doctor is writing prescriptions of E.T.(Evaluate and Treat) to your PT, OT and ST. That is a sure sign your doctors know nothing about stroke recovery.

Distinct activity patterns in neuromodulatory centers are associated with differential modulation of cortical low and high gamma oscillations

Citation

Totah, N., van Keulen, S., Logothetis, N., & Eschenko, O. (2018). Distinct activity patterns in neuromodulatory centers are associated with differential modulation of cortical low and high gamma oscillations. Poster presented at AREADNE 2018: Research in Encoding And Decoding of Neural Ensembles, Santorini, Greece.

Cite as: http://hdl.handle.net/21.11116/0000-0001-9437-A

Abstract

Neuromodulatory systems are thought to gate cortical neuronal excitability — a generalized state consisting of high frequency (20–200 Hz) local field potential (LFP) oscillations. Stimulating neuromodulatory centers evokes a non-specific response across all components of the excited cortical state (beta and gamma band LFP oscillations). The non-specificity of neuromodulation is at odds with the fact that neuromodulators regulate distinct cognitive functions that are affliated with different cortical LFP oscillations. For example, various dopamine and norepinephrine-dependent cognitive functions (e.g., working memory, spatial navigation, and top-down attention) are each accompanied by power increases within different LFP frequency bands. How can neuromodulators contribute to cognitive processes associated with different LFP frequency bands if they non-specifically modulate cortical LFP? Here, rather than perturbing neuromodulatory systems with stimulation, we recorded spontaneous unit activity from two primary sources of cortical neuromodulators (the noradrenergic locus coeruleus, LC, and dopaminergic ventral tegmental area, VTA) and correlated it with LFP power fluctuations in the prefrontal, visual, and somatosensory cortex of urethaneanesthetized rats. We found that neuromodulatory population spike rate rhythmically fluctuates at 1–2 Hz (delta band) in both LC and VTA. But, in the LC, an additional 5–7 Hz (theta band) fluctuation of spike rate occurred. While neuromodulatory delta oscillations non-specifically regulated the power of all cortical LFP oscillations over 20 Hz, theta spike rate oscillations were exclusively associated with cortical high gamma band (60–200 Hz) activity. As LC population spiking rhythmically rose and fell, two types of LC single units (characterized by narrow or wide action potentials) fired in phasic opposition, potentially providing differential cortical state regulation. Our results demonstrate that the noradrenergic system is a unique neuromodulatory center that can affect specific cortical activity patterns, rather than merely gate a generalized state of cortical excitability.

The brain learns completely differently than we've assumed since the 20th century

How long before your doctor stops using 'neurons that fire together wire together' as an explanation for neuroplasticity?
https://www.sciencedaily.com/releases/2018/03/180323084818.htm

Date:

March 23, 2018

Source:

Bar-Ilan University

Summary:

Based on experimental evidence physicists publish revolutionary new theory on brain learning that contradicts the most common assumption in neuroscience, will transform our understanding of brain function, and open new horizons for advanced deep learning algorithms.

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FULL STORY

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Image representing the old synaptic (red) and new dendritic (green) learning scenarios of the brain. In the center a neuron with two dendritic trees collects incoming signals via many thousands of tiny adjustable learning parameters, the synapses, represented by red valves. In the new dendritic learning scenario (right) only two adjustable red valves are located in close proximity to the computational element, the neuron. The scale is such that if a neuron collecting its incoming signals is represented by a person's faraway fingers, the length of its hands would be as tall as a skyscraper (left).

Credit: Ido Kanter

The brain is a complex network containing billions of neurons, where each of these neurons communicates simultaneously with thousands of other via their synapses (links). However, the neuron actually collects its many synaptic incoming signals through several extremely long ramified "arms" only, called dendritic trees.

In 1949 Donald Hebb's pioneering work suggested that learning occurs in the brain by modifying the strength of the synapses, whereas neurons function as the computational elements in the brain. This has remained the common assumption until today.
Using new theoretical results and experiments on neuronal cultures, a group of scientists, led by Prof. Ido Kanter, of the Department of Physics and the Gonda (Goldschmied) Multidisciplinary Brain Research Center at Bar-Ilan University, has demonstrated that the central assumption for nearly 70 years that learning occurs only in the synapses is mistaken.
In an article published today in the journal Scientific Reports, the researchers go against conventional wisdom to show that learning is actually done by several dendrites, similar to the slow learning mechanism currently attributed to the synapses.
"The newly discovered process of learning in the dendrites occurs at a much faster rate than in the old scenario suggesting that learning occurs solely in the synapses. In this new dendritic learning process, there are a few adaptive parameters per neuron, in comparison to thousands of tiny and sensitive ones in the synaptic learning scenario," said Prof. Kanter, whose research team includes Shira Sardi, Roni Vardi, Anton Sheinin, Amir Goldental and Herut Uzan.
The newly suggested learning scenario indicates that learning occurs in a few dendrites that are in much closer proximity to the neuron, as opposed to the previous notion. "Does it make sense to measure the quality of air we breathe via many tiny, distant satellite sensors at the elevation of a skyscraper, or by using one or several sensors in close proximity to the nose? Similarly, it is more efficient for the neuron to estimate its incoming signals close to its computational unit, the neuron," says Kanter. Hebb's theory has been so deeply rooted in the scientific world for 70 years that no one has ever proposed such a different approach. Moreover, synapses and dendrites are connected to the neuron in a series, so the exact localized site of the learning process seemed irrelevant.
Another important finding of the study is that weak synapses, previously assumed to be insignificant even though they comprise the majority of our brain, play an important role in the dynamics of our brain. They induce oscillations of the learning parameters rather than pushing them to unrealistic fixed extremes, as suggested in the current synaptic learning scenario.
The new learning scenario occurs in different sites of the brain and therefore calls for a reevaluation of current treatments for disordered brain functionality. Hence, the popular phrase "neurons that fire together wire together," summarizing Donald Hebb's 70-year-old hypothesis, must now be rephrased. In addition, the learning mechanism is at the basis of recent advanced machine learning and deep learning achievements. The change in the learning paradigm opens new horizons for different types of deep learning algorithms and artificial intelligence based applications imitating our brain functions, but with advanced features and at a much faster speed.

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Story Source:
Materials provided by Bar-Ilan University. Note: Content may be edited for style and length.

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Journal Reference:

1. Shira Sardi, Roni Vardi, Amir Goldental, Anton Sheinin, Herut Uzan & Ido Kanter. Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links. Scientific Reports, 2018 DOI: 10.1038/s41598-018-23471-7

'Neurons that fire together wire together'

This simplistic explanation of how neuroplasticity works is totally worthless.
Neurons that fire together wire together tells us nothing about how to make this repeatable for stroke survivors. Seems to be meant for high school students.

Enhancing the plasticity of the brain: Max Cynader at TEDxStanleyPark