“neuronal noise” may not be a nuisance at all, but instead the brain’s way of providing as comprehensive a statistical representation of the observable world as possible.

2018-11-15: Meditation’s effect in brain waves

What’s going on in the brains of people who meditate? Anecdotal evidence suggests that meditation does something to people’s minds and bodies…quiets and calms them. Davidson brought a number of “Olympic level meditators” into his lab and hooked them up to a brain scanner. He found that the brains of these expert meditators have different brain wave patterns than the rest of us.

2019-08-06: Ripples for memory

researchers have offered up proof that sharp wave ripples play a part in memory: Artificially prolonging the ripples in rats improved their performance

2019-08-12: Keeping the Brain’s Cells in Sync with Gamma Rhythm Synchronization

“It may make a lot of sense that gamma rhythms matter in the brain”. But rather than measuring that rhythm’s aggregate signal across the entire brain, neuroscientists might need to look at several signals, each one accounting for some smaller section of the brain. “You have to go down to the level of local groups of neurons to really see what they’re doing.”

2021-02-23: 1/f noise

The amplitudes for power spectra are usually plotted in logarithmic coordinates because of the wide range in their values. For purely random white noise, the power spectrum curve is relatively flat and horizontal, with a slope of zero, because it’s about the same at all frequencies. But neural data produces curves with a negative slope such that lower frequencies have higher amplitudes, and the intensity drops off exponentially for higher frequencies. This shape is called 1/f, referring to that inverse relationship between the frequency and the amplitude. Neuroscientists are interested in what the flatness or steepness of the slope might indicate about the brain’s inner workings.

2021-05-27: Brain synchronization sociality

Mr. Wu zapped the 2 mice at the same time and at the same rapid frequency — putting that portion of their brains quite literally in sync. Within 1 minute, any animus between the 2 creatures seemed to disappear, and they clung to each other like long-lost friends. “Those animals actually stayed together, and 1 animal was grooming the other”. If brain-to-brain synchrony does turn out to be a real driver of social interaction, it could have some meaningful applications for people who struggle with social anxiety disorders, for example. Several noninvasive techniques, like transcranial magnetic stimulation, can stimulate people’s brain activity and are being tested as treatments for a range of psychiatric disorders. “The human sociality spectrum is very broad, and there’s probably a subset of people who wouldn’t mind if it was possible to influence their level of sociality.”

2021-07-09: Phase precession

Along with rate, there’s also timing: As the rat passes through a place field, the associated place cell fires earlier and earlier with respect to the cycle of the background theta wave. As the rat crosses from 1 place field into another, the very early firing of the first place cell occurs close in time with the late firing of the next place cell. Their near-coincident firings cause the synapse, or connection, between them to strengthen, and this coupling of the place cells ingrains the rat’s trajectory into the brain. (Information seems to be encoded through the strengthening of synapses only when 2 neurons fire within 10s of milliseconds of each other.)

“It’s so prominent and prevalent in the rodent brain that it makes you want to assume it’s a generalizable mechanism”. Scientists had also identified phase precession in the spatial processing of bats and marmosets, but the pattern was elusive in humans until now. Studies suggest that phase precession allows the brain to link sequences of times, images and events in the same way as it does spatial positions. Phase Precession might facilitate very rapid learning of sequences, explaining why artificial neural networks train on 100s or 100s of examples of a pattern before the synapse strengths adjust enough for the network to learn, while humans can typically learn from just 1 or a handful of examples.

2023-02-04: Critical brain hypothesis

The brain is always teetering between 2 phases of activity: a random phase, where it is mostly inactive, and an ordered phase, where it is overactive and on the verge of a seizure. The hypothesis predicts that between these phases, at a sweet spot known as the critical point, the brain has a perfect balance of variety and structure and can produce the most complex and information-rich activity patterns. This state allows the brain to optimize multiple information processing tasks, from carrying out computations to transmitting and storing information, all at the same time.

Early critiques pointed out that proving a network was near the critical point required improved statistical tests. The field responded constructively, and this type of objection is rarely heard these days. More recently, some work has shown that what was previously considered a signature of criticality might also be the result of random processes. Researchers are still investigating that possibility, but many of them have already proposed new criteria for distinguishing between the apparent criticality of random noise and the true criticality of collective interactions among neurons.

Research in this area has steadily become more visible. The breadth of methods being used to assess it has also grown. The biggest questions now focus on how operating near the critical point affects cognition, and how external inputs can drive a network to move around the critical point. Ideas about criticality have also begun to spread beyond neuroscience. Citing some of the original papers on criticality in living neural networks, engineers have shown that self-organized networks of atomic switches can be made to operate near the critical point so that they compute many functions optimally. The deep learning community has also begun to study whether operating near the critical point improves artificial neural networks.

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