Synchronized brain waves enable rapid learning | MIT News Office

Synchronized brain waves enable rapid learning | MIT News Office:  Brain waves known as “beta bands,” produced independently by the prefrontal cortex and the striatum, began to synchronize with each other. This suggests that a communication circuit is forming between the two regions, Miller says.

“There is some unknown mechanism that allows these resonance patterns to form, and these circuits start humming together,” he says. “That humming may then foster subsequent long-term plasticity changes in the brain, so real anatomical circuits can form. But the first thing that happens is they start humming together..."

Previous studies have shown that during cognitively demanding tasks, there is increased synchrony between the frontal cortex and visual cortex, but Miller’s lab is the first to show specific patterns of synchrony linked to specific thoughts.

How neurons ‘decide’ to create axons or dendrites | KurzweilAI

How neurons ‘decide’ to create axons or dendrites | KurzweilAI: They found that embryonic nerve cells manufacture a signaling enzyme called Atypical Protein Kinase C (aPKC) in two varieties: a full-length one and a shorter one...
When the researchers blocked the production of the short form, the nerve cell grew multiple axons and no dendrites. When they created an artificial abundance of the short form, dendrites formed at the expense of axons.

IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer | KurzweilAI

IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer | KurzweilAI: IBM and LBNL achieved an unprecedented scale of 2.084 billion neurosynaptic cores* containing 53×1010  (530 billion) neurons and 1.37×1014 (100 trillion) synapses running only 1542 times slower than real time.

“We have not built a biologically realistic simulation of the complete human brain,” explains an abstract....  “Computation (‘neurons’), memory (‘synapses’), and communication (‘axons,’ ‘dendrites’) are mathematically abstracted away from biological detail toward engineering goals of maximizing function (utility, applications) and minimizing cost (power, area, delay) and design complexity of hardware implementation.”

Wasp has hints of a clockwork brain

Wasp has hints of a clockwork brain: A tiny wasp has brain cells so small, physics predicts they shouldn't work at all. These miniature neurons might harbour subtle modifications, or they might work completely differently from all other known neurons - mechanically...
...Of 528 axons measured, a third were less than 0.1 micrometre in diameter, an order of magnitude narrower than human axons. The smallest were just 0.045 μm...
...That makes the axon impossibly noisy...
...The tiny axons might each carry a long rigid rod stretching down the centre. Pulling the rod could create a physical rather than electrical trigger for the release of a chemical that passes the signal on to the neighbouring neuron...

Ferroelectric memristors may lead to brain-like computers

Ferroelectric memristors may lead to brain-like computers: In a new study, a team of researchers from France, the UK, and Japan has demonstrated that a device called a ferroelectric tunnel junction (FTJ) that experiences voltage-controlled resistance variation represents a new class of memristor. Due to the FTJ's quasi-continuous resistance variations exceeding two orders of magnitude, along with its rapid 10-ns operation speed, the device could one day serve as the basic hardware of neuromorphic computational architectures, or computers that function like brains...

"We have conceptualized, designed and realized a completely new type of memristor that performs as well as classical ionic memristors, but operates through an electronic mechanism," coauthor Manuel Bibes, a CNRS research scientist, told Phys.org. "While this should have clear advantages in terms of reproducibility, the key breakthrough is that our ferroelectric memristor behaves according to well-established physical models. This allows a precise understanding of the memristive response, and also opens the door for engineering memristive functions on-demand."

Blue Brain project accurately predicts connections between neurons

Blue Brain project accurately predicts connections between neurons: To their great surprise, they found that the locations on the model matched that of synapses found in the equivalent real-brain circuit with an accuracy ranging from 75 percent to 95 percent...

This means that neurons grow as independently of each other as physically possible and mostly form synapses at the locations where they randomly bump into each other.
A few exceptions were also discovered, pointing out special cases where signals are used by neurons to change the statistical connectivity. By taking these exceptions into account, the Blue Brain team can now make a near perfect prediction of the locations of all the synapses formed inside the circuit.

$1.3B 'Brain in a Box' Project Faces Skepticism: Scientific American

$1.3B 'Brain in a Box' Project Faces Skepticism: Scientific American: At the heart of that approach is Markram's conviction that a good unifying model has to assimilate data from the bottom up. In his view, modelers should start at the most basic level--he focuses on ion channels because they determine when a neuron fires--and get everything working at one level before proceeding to the next. This requires a lot of educated guesses, but Markram argues that the admittedly huge gaps in knowledge about the brain can be filled with data as experiments are published--the Blue Brain model is updated once a week. The alternative approach, approximating and abstracting away the biological detail, leaves no way to be sure that the model's behavior has anything to do with how the brain works, said Markram.

This is where other computational neuroscientists gnash their teeth. Most of them are already using simple models of individual neurons to explore high-level functions such as pattern recognition. Markram's bottom-up approach risks missing the wood for the trees, many of them argued in Bern: the model could be so detailed that it is no easier to understand than the real brain. And that is if Markram can build it at all. Judging by what Blue Brain has accomplished in the past six years, critics said, that seems unlikely. The tiny swathe of simulated rat cortex has no inputs from sensory organs or outputs to other parts of the brain, and produces almost no interesting behavior, pointed out Kevan Martin, co-director of the INI, in an e-mail. It is "certainly not the case" that Markram has simulated the column as it works in a whole animal, he said.

More than glue: Glia cells found to regulate synapses

More than glue: Glia cells found to regulate synapses: The brain is like a social network, says Prof. Ben-Jacob. Messages may originate with the neurons, which use the synapses as their delivery system, but the glia serve as an overall moderator, regulating which messages are sent on and when. These cells can either prompt the transfer of information, or slow activity if the synapses are becoming overactive. This makes the glia cells the guardians of our learning and memory processes, he notes, orchestrating the transmission of information for optimal brain function.

Mimicking the brain, in silicon - MIT News Office

Mimicking the brain, in silicon - MIT News Office: With about 400 transistors, the silicon chip can simulate the activity of a single brain synapse...
The MIT researchers designed their computer chip so that the transistors could mimic the activity of different ion channels...

Previously, researchers had built circuits that could simulate the firing of an action potential, but not all of the circumstances that produce the potentials. “If you really want to mimic brain function realistically, you have to do more than just spiking. You have to capture the intracellular processes that are ion channel-based,” Poon says.

IBM eyes brain-like computing - Hardware - Technology - News - iTnews.com.au

IBM eyes brain-like computing - Hardware - Technology - News - iTnews.com.au: IBM is building arrays of electronic, synapse-like devices under its SyNAPSE project that aim to physically mimic the human brain.

The project delivered prototype silicon chips in August that contained 256 neurons and either 262,144 programmable synapses or 65,536 “learning synapses”.

The next step, Kelly said, is to replace components of the chip with materials that support multiple states, and not just the traditional ones and zeros.

Music of the brain: each synapse has its own natural rhythm | KurzweilAI

Music of the brain: each synapse has its own natural rhythm | KurzweilAI: Contrary to what was previously assumed, Mehta and Kumar found that stimulating the neurons at the highest frequencies was not the best way to increase synaptic strength. “To our surprise, we found that beyond the optimal frequency, synaptic strengthening actually declined as the frequencies got higher.”

The knowledge that a synapse has a preferred frequency for maximal learning led the researchers to compare optimal frequencies based on the location of the synapse on a neuron...

The optimal frequency for inducing synaptic learning changed depending on where the synapse was located. The farther the synapse was from the neuron’s cell body, the higher its optimal frequency.

“Incredibly, when it comes to learning, the neuron behaves like a giant antenna, with different branches of dendrites tuned to different frequencies for maximal learning,” Mehta said.

Inside IBM's cognitive chip : Nature News

Inside IBM's cognitive chip : Nature News: The theory is that the computational components act as 'neurons', while the RAM units act as the 'synapses'...

In the cognitive chip, a pattern of signals from the RAM can cause a computational element to carry out a simple operation. The result goes to another RAM synapse, which can send signals to other computational neurons...

New Computer Chip Modeled on a Living Brain Can Learn and Remember | Popular Science

New Computer Chip Modeled on a Living Brain Can Learn and Remember | Popular Science: Built on 45 nanometer silicon/metal oxide semiconductor platform, both chips have 256 neurons. One chip has 262,144 programmable synapses and the other contains 65,536 learning synapses — which can remember and learn from their own actions...
 IBM's chip doesn't use a memristor architecture, but it does integrate memory with computation power — and it uses computer neurons and axons to do it. The building blocks are simple, but the architecture is unique..
"When a neuron changes its state, the state it is modifying is its own state, not the state of something else. So you can physically co-locate the circuit to do the computation, and the circuit to store the state. They can be very close to each other, so that cooperation becomes very efficient," he said...
It integrates memory with processor capability on a typical SOI-CMOS platform, using traditional transistors in a new design. Along with integrated memory to stand in for synapses, the neurosynaptic “core” uses typical transistors for input-output capability, i.e. neurons.

A new device with memorizing and forgetting functions like human brain is reported

A new device with memorizing and forgetting functions like human brain is reported: The device is made with the atomic switch which consists of an Ag2S-coated metal Ag electrode and a counter electrode of platinum Pt, having a nanometer gap between the two electrodes. The atomic switch works by the formation and annihilation of an Ag-atom bridge between the electrodes, which is realized by controlling the solid-state electrochemical reaction of a mixed ionic and electronic conductor Ag2S.
The research group discovered that the device emulates two types of synaptic function, short-term plasticity and long-term potentiation by varying input pulse repetition time which controls the formation of the Ag-atom bridges.

DVD alloys help make computers that think like us - tech - 12 July 2011 - New Scientist

DVD alloys help make computers that think like us: GST is known as a "phase-change" alloy, because of its ability to change its molecular structure from a crystalline to a disordered amorphous "phase" when heated...
Different areas within a tiny spot of GST can be crystalline or amorphous to differing degrees, which means it can store information across a much wider range of values than simply 0 or 1...
Wright's neuron is able to mimic this threshold firing because GST's electrical resistance drops suddenly when it moves from its amorphous phase to the crystalline. So incoming signals in the form of pulses of current are applied to the artificial neuron - and it is deemed to have fired when its resistance plummets...
GST's ability to change its resistance has allowed them to program it to dynamically modify the strength of the nanoscale artificial synapses they have built...
a system with 10^10 synapses would consume just 10 watts...

How to make a human neuron : Nature News

How to make a human neuron : Nature News: "Those three genes also made human cells that looked like nerve cells but that did not fire the electric pulses characteristic of neurons. However, the addition of a fourth virus-delivered gene, found through trial and error, pushed fibroblast cells — connective tissue cells found throughout the body and involved in wound healing — collected from aborted fetuses and the foreskin of newborns to become bona fide neurons. After a couple of weeks in culture, many of the neurons responded to electric jolts by pumping ions across their membranes. A few weeks later still, these neurons started to form connections, or synapses, with the mouse neurons they were grown alongside."

A Synthetic Brain Synapse Is Constructed From Carbon Nanotubes | Popular Science

A Synthetic Brain Synapse Is Constructed From Carbon Nanotubes | Popular Science: "In tests, their synapse circuit functions very much like a real neuron--neurons being the very building blocks of the brain. Tapping the unique properties of carbon nanotubes, their lab was able to essentially recreate brain function in a very fractional way."

Observations: Consider the crayfish: How a claw-full of neurons makes crustaceans crawl [Video]

Observations: Consider the crayfish: How a claw-full of neurons makes crustaceans crawl [Video]: "Kagaya and Takahata found that just 45 individual neurons spiked right before the crayfish started walking on its own—whether it was walking forward or backward. Some of the readiness discharge neurons were those that were also active while the crayfish was walking and others that were involved in the stopping process, suggesting that 'the main synaptic activation for voluntarily initiated walking of crayfish takes place in the medial protocerebrum,' the area of the arthropod brain that also receives visual input and information from other organs. This means that the neural signals the animal employs to get going are 'organized and activated by presynaptic brain neurons, not by endogenous mechanisms,' they explained in their study."

Mapping connectomes in the visual cortex | KurzweilAI

Mapping connectomes in the visual cortex | KurzweilAI: The researchers used high-resolution imaging of neurons in the visual cortex of mouse brains, which contains thousands of neurons and millions of different connections. Using high-resolution imaging, they were able to detect which of these neurons responded to a particular stimulus, for example, a horizontal edge.

Taking a slice of the same tissue, the researchers then applied small currents to a subset of neurons in turn to see which other neurons responded and which of them were synaptically connected. By repeating this technique many times, the researchers were able to trace the function and connectivity of hundreds of nerve cells in the visual cortex.

Reverse signals in neurons found | KurzweilAI

Reverse signals in neurons found | KurzweilAI: Northwestern University scientists have discovered that axons can operate in reverse: they can also send signals to the neuron cell body, too. Previously, it was thought that axons only carry signals away from neurons (output).

It also turns out axons can talk to each other. Before sending signals in reverse, axons can perform their own neural computations without any involvement from the cell body or dendrites. This is contrary to typical neuronal communication where an axon of one neuron is in contact with another neuron’s dendrite or cell body, not its axon. And, unlike the computations performed in dendrites, the computations occurring in axons are thousands of times slower, potentially creating a means for neurons to compute fast things in dendrites and slow things in axons.