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News

Neuroscience

Brain gains seen in elderly mice injected with human umbilical cord plasma

Memory protein that declines with aging also identified in mouse study

By

Laura Sanders

1:00pm, April 19, 2017

Magazine issue: Vol. 191 No. 9, May 13, 2017, p. 7

Plasma taken from human umbilical cords can rejuvenate old mice’s brains and improve their memories, a new study suggests. The results, published online April 19 in Nature, may ultimately help scientists develop ways to stave off aging.

Earlier studies have turned up youthful effects of young mice’s blood on old mice (SN: 12/27/14, p. 21). Human plasma, the new results suggest, confers similar benefits, says study coauthor Joseph Castellano, a neuroscientist at Stanford University. The study also identifies a protein that’s particularly important for the youthful effects, a detail that “adds a nice piece to the puzzle,” Castellano says.

Identifying the exact components responsible for rejuvenating effects is important, says geroscientist Matt Kaeberlein of the University of Washington in Seattle. That knowledge will bring scientists closer to understanding how old tissues can be rejuvenated. And having the precise compounds in hand means that scientists might have an easier time translating therapies to people.

Kaeberlein cautions that the benefits were in mice, not people. Still, he says, “there is good reason to be optimistic that some of these approaches will have similar effects on health span in people.”

Like people, as mice age, brain performance begins to slip. Compared with younger generations, elderly mice perform worse on some tests of learning and memory, taking longer to remember the location of an escape route out of a maze, for instance. Researchers suspect that these deficits come from age-related trouble in the hippocampus, a brain structure important for learning and memory.

Every fourth day for two weeks, Castellano and colleagues injected old mice with human plasma taken from umbilical cords, young adults and elderly adults. The source of plasma infusion changed the behavior of genes in the hippocampus, the researchers found. Elderly mice that had received umbilical cord or young adult plasma showed gene behavior changes that go along with improved hippocampal functioning. And after infusions of human cord plasma, more hippocampus cells churned out a protein called c-Fos, a marker of a busy brain that’s known to decline with age. Elderly mice that received elderly human plasma showed no such changes.

Story continues below image

Brain reset

An elderly mouse that received plasma infusions derived from human umbilical cords (right) had nerve cells in the hippocampus that produced the protein c-Fos (red dots pointed out by black arrows), a marker of nerve cell activity. Some c-Fos protein was seen in elderly brains that received plasma from young adults (middle right). Very little c-Fos was present when the plasma came from elderly people (middle left) or when no plasma was injected (left).

 These brain changes came with behavioral improvements, too. Elderly mice that received umbilical cord plasma were quicker to learn and better at remembering the location of an escape hatch in a maze than elderly mice that didn’t receive the plasma. Mice that received injections were also more adept at learning associations between a room and a painful electric shock, and even better at making nests for babies, a skill that usually suffers with age.

Castellano and colleagues searched for the ingredient responsible for the effects by comparing plasma proteins whose abundance changes with age in mice and people. One candidate seemed particularly promising: Levels of a protein called TIMP2 started out high early in life but then dropped with age, in both mice and people. 

Infusions of mouse TIMP2 had positive effects on elderly mice, both in their brains and their behavior, the team found. And when researchers removed TIMP2 from young mice, the animals grew worse at remembering new objects.

The study doesn’t explain how TIMP2 might work in the brain, says Gillian Murphy, a molecular cell biologist at the University of Cambridge who studies TIMP proteins and the proteins that TIMPs interact with. “Before any realistic interpretation of these data can be made,” it’s essential to figure out how TIMP2 affects hippocampal cells, she says.

In the meantime, a clinical trial designed to test whether young human plasma can slow the cognitive decline of people with Alzheimer’s disease is under way. Data have been collected and are being analyzed, says study coauthor Tony Wyss-Coray, an Alzheimer’s researcher at Stanford. Wyss-Coray and Castellano have ties to the company Alkahest, which is involved with the clinical trial and therapies to counter aging.  

Citations

J.M. Castellano et al. Human umbilical cord plasma proteins revitalize hippocampal function in aged mice. Nature. Published online April 19, 2017. doi:10.1038/nature22067.

Further Reading

L. Sanders. Year in Review: Young blood aids old brains. Science News. Vol. 186, December 27, 2014, p. 21.

 

AI will upload and access our memories, predicts Siri co-inventor

Tuesday, 9 May 2017 by System Administrator

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AI will upload and access our memories, predicts Siri co-inventor

"Instead of asking how smart we can make our machines, let's ask how smart our machines can make us."

April 26, 2017

 “Hey Siri, what’s the name of that person I met yesterday?” (credit: Apple Inc.)

Instead of replacing humans with robots, artificial intelligence should be used more for augmenting human memory and other human weaknesses, Apple Inc. executive Tom Gruber suggested at the TED 2017 conference yesterday (April 25, 2017).

Thanks to the internet and our smartphones, much of our  personal data is already being captured, notes Gruber, who was one the inventors of voice-controlled intelligent-assistant Siri. Future AI memory enhancement could be especially life-changing for those with Alzheimer’s or dementia, he suggested.

Limitless

“Superintelligence should give us super-human abilities,” he said. “As machines get smarter, so do we. Artificial intelligence can enable partnerships where each human on the team is doing what they do best. Instead of asking how smart we can make our machines, let’s ask how smart our machines can make us.

“I can’t say when or what form factors are involved, but I think it is inevitable,” he said. “What if you could have a memory that was as good as computer memory and is about your life? What if you could remember every person you ever met? How to pronounce their name? Their family details? Their favorite sports? The last conversation you had with them?”

Gruber’s ideas mesh with a prediction by Ray Kurzweil: “Once we have achieved complete models of human intelligence, machines will be capable of combining the flexible, subtle human levels of pattern recognition with the natural advantages of machine intelligence, in speed, memory capacity, and, most importantly, the ability to quickly share knowledge and skills.”

Two projects announced last week aim in that direction: Facebook’s plan to develop a non-invasive brain-computer interface that will let you type at 100 words per minute and Elon Musks’ proposal that we become superhuman cyborgs to deal with superintelligent AI.

But trusting machines also raises security concerns, Gruber warned. “We get to choose what is and is not recalled,” he said. “It’s absolutely essential that this be kept very secure.”

 

 

Ultra-low-power artificial synapse for neural-network computing

Saturday, 25 March 2017 by System Administrator

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An ultra-low-power artificial synapse for neural-network computing

Brain-like device with 500 states instead of binary could one day communicate with live neurons, merging computers with the brain

February 24, 2017

(Left) Illustration of a synapse in the brain connecting two neurons. (Right) Schematic of artificial synapse (ENODe), which functions as a transistor. It consists of two thin, flexible polymer films (black) with source, drain, and gate terminals, connected by an electrolyte of salty water that permits ions to cross. A voltage pulse applied to the “presynaptic” layer (top) alters the level of oxidation in the “postsynaptic layer” (bottom), triggering current flow between source and drain. (credit: Thomas Splettstoesser/CC and Yoeri van de Burgt et al./Nature Materials)

Stanford University and Sandia National Laboratories researchers have developed an organic artificial synapse based on a new memristor (resistive memory device) design that mimics the way synapses in the brain learn. The new artificial synapse could lead to computers that better recreate the way the human brain processes information. It could also one day directly interface with the human brain.

The new artificial synapse is an electrochemical neuromorphic organic device (dubbed “ENODe”) — a mixed ionic/electronic design that is fundamentally different from existing and other proposed resistive memory devices, which are limited by noise, required high write voltage, and other factors*, the researchers note in a paper published online Feb. 20 in Nature Materials.

Like a neural path in a brain being reinforced through learning, the artificial synapse is programmed by discharging and recharging it repeatedly. Through this training, the researchers have been able to predict within 1 percent of uncertainly what voltage will be required to get the synapse to a specific electrical state and, once there, remain at that state.

“The working mechanism of ENODes is reminiscent of that of natural synapses, where neurotransmitters diffuse through the cleft, inducing depolarization due to ion penetration in the postsynaptic neuron,” the researchers explain in the paper. “In contrast, other memristive devices switch by melting materials at relatively high temperatures (PCMs) or by voltage-induced breakdown/filament formation and ion diffusion in dense oxide layers (FFMOs).”

The ENODe achieves significant energy savings** in two ways:

  • Unlike a conventional computer, where you save your work to the hard drive before you turn it off, the artificial synapse can recall its programming without any additional actions or parts. Traditional computing requires separately processing information and then storing it into memory. Here, the processing creates the memory.
  • When we learn, electrical signals are sent between neurons in our brain. The most energy is needed the first time a synapse is traversed. Every time afterward, the connection requires less energy. This is how synapses efficiently facilitate both learning something new and remembering what we’ve learned. The artificial synapse, unlike most other versions of brain-like computing, also fulfills these two tasks simultaneously, and does so with substantial energy savings.

“More and more, the kinds of tasks that we expect our computing devices to do require computing that mimics the brain because using traditional computing to perform these tasks is becoming really power hungry,” said A. Alec Talin, distinguished member of technical staff at Sandia National Laboratories in Livermore, California, and co-senior author of the paper. “We’ve demonstrated a device that’s ideal for running these type of algorithms and that consumes a lot less power.”

A future brain-like computer with 500 states

Only one artificial synapse has been produced so far, but researchers at Sandia used 15,000 measurements to simulate how an array of them would work in a neural network. They tested the simulated network’s ability to recognize handwriting of digits 0 through 9. Tested on three datasets, the simulated array was able to identify the handwritten digits with an accuracy between 93 to 97 percent.

This artificial synapse may one day be part of a brain-like computer, which could be especially useful for processing visual and auditory signals, as in voice-controlled interfaces and driverless cars, but without energy-consuming computer hardware.

This device is also well suited for the kind of signal identification and classification that traditional computers struggle to perform. Whereas digital transistors can be in only two states, such as 0 and 1, the researchers successfully programmed 500 states in the artificial synapse, which is useful for neuron-type computation models. In switching from one state to another they used about one-tenth as much energy as a state-of-the-art computing system needs to move data from the processing unit to the memory.

However, this is still about 10,000 times as much energy as the minimum a biological synapse needs in order to fire**. The researchers hope to attain neuron-level energy efficiency once they test the artificial synapse in smaller devices.

Linking to live organic neurons

This new artificial synapse may one day be part of a brain-like computer, which could be especially beneficial for computing that works with visual and auditory signals. Examples of this are seen in voice-controlled interfaces and driverless cars. Past efforts in this field have produced high-performance neural networks supported by artificially intelligent algorithms but these depend on energy-consuming traditional computer hardware.

Every part of the device is made of inexpensive organic materials. These aren’t found in nature but they are largely composed of hydrogen and carbon and are compatible with the brain’s chemistry. Cells have been grown on these materials and they have even been used to make artificial pumps for neural transmitters. The switching voltages applied to train the artificial synapse (about 0.5 mV) are also the same as those that move through human neurons — about 1,000 times lower than the “write” voltage for a typical memristor.

That means it’s possible that the artificial synapse could communicate with live neurons, leading to improved brain-machine interfaces. The softness and flexibility of the device also lends itself to being used in biological environments.

This research was funded by the National Science Foundation, the Keck Faculty Scholar Funds, the Neurofab at Stanford, the Stanford Graduate Fellowship, Sandia’s Laboratory-Directed Research and Development Program, the U.S. Department of Energy, the Holland Scholarship, the University of Groningen Scholarship for Excellent Students, the Hendrik Muller National Fund, the Schuurman Schimmel-van Outeren Foundation, the Foundation of Renswoude (The Hague and Delft), the Marco Polo Fund, the Instituto Nacional de Ciência e Tecnologia/Instituto Nacional de Eletrônica Orgânica in Brazil, the Fundação de Amparo à Pesquisa do Estado de São Paulo and the Brazilian National Council.

* “A resistive memory device has not yet been demonstrated with adequate electrical characteristics to fully realize the efficiency and performance gains of a neural architecture. State-of-the-art memristors suffer from excessive write noise, write non-linearities, and high write voltages and currents.  Reducing the noise and lowering the switching voltage significantly below 0.3 V (~10 kT) in a two-terminal device without compromising long-term data retention has proven difficult.” … Organic memristive devices have been recently proposed, but are limited by “the slow kinetics of ion diffusion through a polymer to retain their states or on charge storage in metal nanoparticles, which inherently limits performance and stability.” — Yoeri van de Burgt et al., Nature Materials

** ENODe switches at low voltage and energy (< 10 pJ for 1000-square-micrometer devices), compared to an estimated 1–100 fJ per synaptic event for the human brain.

 


Abstract of A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

The brain is capable of massively parallel information processing while consuming only ~1–100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 103 μm2 devices), displays >500 distinct, non-volatile conductance states within a ~1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

References:

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Topics: Cognitive Science/Neuroscience | Computers/Infotech/UI

 

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