Amazing Science

Hummingbirds can see more colors than humans, and new research suggests the skills give them an advantage in foraging and communication.

Though tiny and delicate, hummingbirds are keen at adapting to their environment — whether building a nest with a leaf as a roof or using their expert eyes to scout out their surroundings. Hummingbirds are also able to see colors that humans can't — and that gives them an edge when it comes to foraging, mating, and avoiding predators, a new study finds.

In the eyes of animals, including humans, cells called cones and rods take in light. While rods deal with the intensity of light — making it possible to see in low light, for instance — cones help one perceive color. Humans have three types of cones, which respectively allow us to see blue, red, and green. Thanks to that ability, the full spectrum of colors that humans see include red, orange, green, blue, indigo, and violet. But we can also see combinations of colors from widely separated parts of the spectrum — like purple, which combines red and blue.vBirds, meanwhile, have four types of cones, so their color-combination possibilities are multiplied, compared to humans.

In this study, researchers conducted experiments with broad-tailed hummingbirds, Selasphorus platycercus, to see whether the birds could differentiate between spectral colors and spectral colors combined with UV. The goal was to evaluate how important nonspectral colors are to these birds. The researchers used a sugar solution as a reward. For example, they tested hummingbirds to see if they could tell the difference between a green light and green light combined with UV light, based on whether they could access the reward.

Humans can't see UV light, but birds can. By combining spectral light with UV, researchers proved that birds can differentiate between those colors. This means that when the birds look at objects we can see as spectral light, they are likely seeing many more colors because that fourth cone gives them the ability to see many more color combinations.

Brain imprints on cranial bones from great apes and humans refute the long-held notion that the human pattern of brain asymmetry is unique.

The left and right side of the brain are involved in different tasks. This functional lateralization and associated brain asymmetry are well documented in humans, but little is known about brain asymmetry in our closest living relatives, the great apes. Using endocasts (imprints of the brain on cranial bones), scientists now challenge the long-held notion that the human pattern of brain asymmetry is unique. They found the same asymmetry pattern in chimpanzees, gorillas, and orangutans. However, humans were the most variable in this pattern. This suggests that lateralized, uniquely human cognitive abilities, such as language, evolved by adapting a presumably ancestral asymmetry pattern.

The left and right side of our brain are specialized for some cognitive abilities. For example, in humans, language is processed predominantly in the left hemisphere, and the right hand is controlled by the motor cortex in the left hemisphere. The functional lateralization is reflected by morphological asymmetry of the brain. Left and right hemisphere differ subtly in brain anatomy, the distribution of nerve cells, their connectivity and neurochemistry. Asymmetries of outer brain shape are even visible on endocasts. Most humans have a combination of a more projecting left occipital lobe (located in the back of the brain) with a more projecting right frontal lobe. Brain asymmetry is commonly interpreted as crucial for human brain function and cognition because it reflects functional lateralization. However, comparative studies among primates are rare and it is not known which aspects of brain asymmetry are really uniquely human. Based on previously available data, scientists assumed that many aspects of brain asymmetry evolved only recently, after the split between the human lineage from the lineage of our closest living relatives, the chimpanzees.

In a recent paper researchers from the Max Planck Institute for Evolutionary Anthropology and the University of Vienna measured the magnitude and pattern of shape asymmetry of endocasts from humans and apes. "Great ape brains are rarely available for study, but we have developed methods to extract brain asymmetry data from skulls, which are easier to access. This made our study possible in the first place," says lead author Simon Neubauer.

The team found that the magnitude of asymmetry was about the same in humans and most great apes. Only chimpanzees were, on average, less asymmetric than humans, gorillas, and orangutans. They also investigated the pattern of asymmetry and could demonstrate that not only humans, but also chimpanzees, gorillas, and orangutans showed the asymmetry pattern previously described as typically human: the left occipital lobe, the right frontal lobe, as well as the right temporal pole and the right cerebellar lobe projecting more relatively to their contralateral parts.

"What surprised us even more," says Philipp Mitteroecker, a co-author of the study, "was that humans were least consistent in this asymmetry with a lot of individual variation around the most common pattern." The authors interpret this as a sign of increased functional and developmental modularization of the human brain. For example, the differential projections of the occipital lobe and the cerebellum are less correlated in humans than in great apes. This finding is interesting because the cerebellum in humans underwent dramatic evolutionary changes and it seems that thereby its asymmetry was affected as well.

The finding of a shared asymmetry pattern but greater variability in humans is intriguing for the interpretation of human brain evolution. An endocast of one of our fossil ancestors that shows this asymmetry can no longer be interpreted as evidence for human-specific functional brain lateralization without other (archaeological) data. Philipp Gunz, a co-author of the study, explains: "This shared asymmetry pattern of the brain evolved already before the origin of the human lineage. Humans seem to have built upon this morphological pattern to establish functional brain lateralization related to typical human behaviors."

By uniting efforts, the International Brain Initiative can help shape the future of neuroscience research at a global scale.

The initiative, at the time of writing, includes Japan’s Brain/Minds, Australian Brain Alliance, the EU’s Human Brain Project (HBP), Canadian Brain Research Strategy, the US’ BRAIN Initiative (BRAINI), the Korea Brain Initiative, and the China Brain Project.

Few times in history has mankind ever united to solve a single goal. Even the ultimate moonshot in history—putting a man on the moon—was driven by international competition rather than unification. So it’s perhaps fitting that mankind is now uniting to understand the organ that fundamentally makes us human: our brain. First envisioned in 2016 through a series of discussions on the “grand challenges” in neuroscience at Johns Hopkins University, the International Brain Initiative (IBI) “came out” this week in a forward-looking paper in Neuron.

Rather than each country formulating their own brain projects independently, the project argues, it’s high time for the world to come together and share their findings, resources, and expertise across borders. By uniting efforts, the IBI can help shape the future of neuroscience research at a global scale—for promoting brain and mental health, for stimulating international collaboration, for ethical neuroscience practices, and for crafting future generations of scientists.

“It takes a world to understand the brain,” said Caroline Montojo of the Kavli Foundation, which offered support to the project. “When we have the best brains and the best minds working together, sharing information and research that could benefit us all.”

The IBI comes at a time when global research divisions are prominent. Established national projects, such as the BRAINI and the HBP, have notably different goals at the operational level. The BRAINI, for example, prominently champions developing new tools to study brain functions, whereas the HBP’s ultimate goal is to recreate the function of a human brain inside machines.

Even within single countries, divisions in practical paths forward have been, mildly put, chaotic. China’s Brain Project, announced officially in 2016 and kicked off two years later, was plagued by different opinions on focus: should it be on solving brain disorders, or understanding the neurobiology behind cognition, or focused on engineering problems that more intimately link human brains with AI?

Then there’s the underlying political milieu, where certain countries are cracking down on international researchers for fear that they may be stealing or selling trade secrets. To all these divisions, the IBI took a stance and said no—it’s time to work together.

“The biggest challenge that we’re facing is to really understand how the brain works, the mystery of the brain, to crack the code,” said Dr. Yves De Koninck of the Canadian Brain Research Strategy.

“If we’re going to make the really big leap changes in the level of understanding of how the brain works in health and disease, we need to have global collaboration, I mean that’s just absolutely vital,” added Dr. Linda Lanyon at the IBI Data Standards and Sharing Working Group.

The olfactory bulbs (OBs) are the first site of odor representation in the mammalian brain, and their unique ultrastructure is considered a necessary substrate for spatiotemporal coding of smell. Given this, we were struck by the serendipitous observation at MRI of two otherwise healthy young left-handed women, yet with no apparent OBs. Standardized tests revealed normal odor awareness, detection, discrimination, identification, and representation.

Functional MRI of these women’s brains revealed that odorant-induced activity in piriform cortex, the primary OB target, was similar in its extent to that of intact controls. Finally, review of a public brain-MRI database with 1,113 participants (606 women) also tested for olfactory performance, uncovered olfaction without anatomically defined OBs in ∼0.6% of women and ∼4.25% of left-handed women. Thus, humans can perform the basic facets of olfaction without canonical OBs, implying extreme plasticity in the functional neuroanatomy of this sensory system.

Deep learning has had enormous success on perceptual tasks but still struggles in providing a model for inference. To address this gap, we have been developing networks that support memory, attention, composition, and reasoning. Our MACnet and NSM designs provide a strong prior for explicitly iterative reasoning, enabling them to learn explainable, structured reasoning, as well as achieve good generalization from a modest amount of data.

The Neural State Machine (NSM) design also emphasizes the use of a more symbolic form of internal computation, represented as attention over symbols, which have distributed representations. Such designs impose structural priors on the operation of networks and encourage certain kinds of modularity and generalization. We demonstrate the models’ strength, robustness, and data efficiency on the CLEVR dataset for visual reasoning (Johnson et al. 2016), VQA-CP, which emphasizes disentanglement (Agrawal et al. 2018), and our own GQA (Hudson and Manning 2019). Joint work with Drew Hudson.

Engineers have mimicked the human brain with an electronic chip that uses light to create and modify memories.

Researchers from RMIT University drew inspiration from an emerging tool in biotechnology -- optogenetics -- to develop a device that replicates the way the brain stores and loses information.

Optogenetics allows scientists to delve into the body's electrical system with incredible precision, using light to manipulate neurons so that they can be turned on or off. The new chip is based on an ultra-thin material that changes electrical resistance in response to different wavelengths of light, enabling it to mimic the way that neurons work to store and delete information in the brain.

Research team leader Dr Sumeet Walia said the technology moves us closer towards artificial intelligence (AI) that can harness the brain's full sophisticated functionality. "Our optogenetically-inspired chip imitates the fundamental biology of nature's best computer -- the human brain," Walia said.

"Being able to store, delete and process information is critical for computing, and the brain does this extremely efficiently. We're able to simulate the brain's neural approach simply by shining different colours onto our chip. This technology takes us further on the path towards fast, efficient and secure light-based computing. It also brings us an important step closer to the realization of a bionic brain -- a brain-on-a-chip that can learn from its environment just like humans do."

Dr Taimur Ahmed, lead author of the study published in Advanced Functional Materials, said being able to replicate neural behavior on an artificial chip offered exciting avenues for research across sectors. "This technology creates tremendous opportunities for researchers to better understand the brain and how it's affected by disorders that disrupt neural connections, like Alzheimer's disease and dementia," Ahmed said.

The researchers, from the Functional Materials and Microsystems Research Group at RMIT, have also demonstrated the chip can perform logic operations -- information processing -- ticking another box for brain-like functionality.

Scientists have known insects experience something like pain since 2003, but new research published today from Associate Professor Greg Neely and colleagues at the University of Sydney proves for the first time that insects also experience chronic pain that lasts long after an initial injury has healed.

Chronic pain is defined as persistent pain that continues after the original injury has healed. It comes in two forms: inflammatory pain and neuropathic pain. The study of fruit flies looked at neuropathic 'pain', which occurs after damage to the nervous system and, in humans, is usually described as a burning or shooting pain. Neuropathic pain can occur in human conditions such as sciatica, a pinched nerve, spinal cord injuries, postherpetic neuralgia (shingles), diabetic neuropathy, cancer bone pain, and in accidental injuries.

Testing pain in fruit flies

In the study, Associate Professor Neely and lead author Dr Thang Khuong from the University's Charles Perkins Centre, damaged a nerve in one leg of the fly. The injury was then allowed to fully heal. After the injury healed, they found the fly's other legs had become hypersensitive. "After the animal is hurt once badly, they are hypersensitive and try to protect themselves for the rest of their lives," said Associate Professor Neely. "That's kind of cool and intuitive."

Next, the team genetically dissected exactly how that works.

"The fly is receiving 'pain' messages from its body that then go through sensory neurons to the ventral nerve cord, the fly's version of our spinal cord. In this nerve cord are inhibitory neurons that act like a 'gate' to allow or block pain perception based on the context," Associate Professor Neely said. "After the injury, the injured nerve dumps all its cargo in the nerve cord and kills all the brakes, forever. Then the rest of the animal doesn't have brakes on its 'pain'. The 'pain' threshold changes and now they are hypervigilant."

"Animals need to lose the 'pain' brakes to survive in dangerous situations but when humans lose those brakes it makes our lives miserable. We need to get the brakes back to live a comfortable and non-painful existence."

A new imaging tool promises to open the door to all sorts of new insights about the brain and how it works. The tool lets scientists see each of the human brain's 100 trillion synapses and 100 billion neurons, individually, plus all other cellular objects and many of their sub-cellular components.

Researchers report ultraprecise imaging of a postmortem human brain.

Over 100 hours of scanning has yielded a 3-D picture of the whole human brain that’s more detailed than ever before. The new view, enabled by a powerful MRI, has the resolution potentially to spot objects that are smaller than 0.1 millimeters wide. “We haven’t seen an entire brain like this,” says electrical engineer Priti Balchandani of the Icahn School of Medicine at Mount Sinai in New York City, who was not involved in the study. “It’s definitely unprecedented.”

The scan shows brain structures such as the amygdala in vivid detail, a picture that might lead to a deeper understanding of how subtle changes in anatomy could relate to disorders such as post-traumatic stress disorder.   

To get this new look, researchers at Massachusetts General Hospital in Boston and elsewhere studied a brain from a 58-year-old woman who died of viral pneumonia. Her donated brain, presumed to be healthy, was preserved and stored for nearly three years.

Before the scan began, researchers built a custom spheroid case of urethane that held the brain still and allowed interfering air bubbles to escape. Sturdily encased, the brain then went into a powerful MRI machine called a 7 Tesla, or 7T, and stayed there for almost five days of scanning.

The strength of the 7T, the length of the scanning time and the fact that the brain was perfectly still led to the high-resolution images, which are described May 31 at bioRxiv.org. Associated videos of the brain, as well as the underlying dataset, are publicly available.

Simulations of large-scale networks of neurons are a key element in the European Human Brain Project (HBP). In a new perspective article scientists from the HBP argue why such simulations are indispensable for bridging the scales between the neuron and system levels in the brain. The authors describe the need for open general-purpose simulation engines that can run a multitude of different candidate models of the brain at different levels of biological detail. Comparing predictions derived from such simulations with experimental data will allow systematic testing and refinement of models in a loop between computational and experimental neuroscience.

A wide variety of experimental techniques are used in neuroscience today to gain insight into neural function from measured brain signals. But to understand the complex nonlinear dynamics at play in the brain and to explain how the underlying activity gives rise to the signals, computational modeling is required. Simulations provide the crucial link between data generated by experimentalists and these models, a multi-author team, all affiliated with the European Flagship Human Brain Project, now writes in the new article “The scientific case for brain simulation”.

The basis for such simulations has been created on the HBP’s Brain Simulation Platform, a part of the projects Research Infrastructure for brain science that is openly accessible to the neuroscience community. The platform provides a set of continuously improved brain network simulators and has driven the construction of computational models and simulations at various scales, from single neurons to large-scale brain-wide networks.

As the terms can easily be confused, the researchers emphasize that simulation and model are not identical. While mathematical models can embody many different hypotheses about how the brain works in equations and experimental parameters, “brain simulators can be viewed as ‘mathematical observatories’ to test various candidate hypotheses. A brain simulator is thus a tool, not a hypothesis, and can as such be likened to tools used to image brain structure or brain activity”, the scientists write. Simulation in this context means using sophisticated software tools to set complex models of the brain that represent large numbers of interconnected neurons into motion – and to observe what testable predictions emerge from them.

“The simulation does not represent the goal itself, but serves as a powerful new way for testing competing hypotheses about the brain”, explains Gaute Einevoll, Professor at the Norwegian University of Life Sciences (NMBU) and lead author of the paper. This then serves to enable a systematic “biological imitation game” where models that provide the best predictions of experimental data “win”.

To illustrate the point, Einevoll draws an analogy to the history of physics: “Our project can be compared to Isaac Newton’s development of a new branch in mathematics. Newton needed to develop a type of mathematics called calculus to check whether his proposed law of gravitation of how masses such as planets attract each other was correct or not. With it, he could calculate the planetary paths in his model and verify that his theory was consistent with observations. With the simulation infrastructure we have developed, we can similarly test whether our candidate network models provide predictions that are consistent with brain measurements. This workflow will be important for further scientific progress, says Einevoll.

While the creation of a detailed mathematical model has to integrate and generalize a wide range of data provided by experiments, the foundations of the network simulators are simpler and well established, the paper explains – “biophysical principles of how to model electrical activity in neurons and how neurons integrate synaptic inputs from other neurons and generate action potentials. These principles […] are the only hypotheses underlying the construction of brain network simulators. […] This is the reason why many models can be represented in the same simulator and why it is possible to develop generally applicable simulators for network neuroscience.”

What happens when an algorithm can ask neurons what they want to see?

What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? Neuroscientists now investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories. A genetic algorithm searched this space for stimuli that maximized neuronal firing. This led to the evolution of rich synthetic images of objects with complex combinations of shapes, colors, and textures, sometimes resembling animals or familiar people, other times revealing novel patterns that did not map to any clear semantic category.

These results expand our conception of the dictionary of features encoded in the cortex, and the approach can potentially reveal the internal representations of any system whose input can be captured by a generative model.

The technology could one day restore the voices of people who have lost the ability to speak due to paralysis and other forms of neurological damage.

A state-of-the-art brain-machine interface created by UC San Francisco neuroscientists can generate natural-sounding synthetic speech by using brain activity to control a virtual vocal tract – an anatomically detailed computer simulation including the lips, jaw, tongue and larynx. The study was conducted in research participants with intact speech, but the technology could one day restore the voices of people who have lost the ability to speak due to paralysis and other forms of neurological damage. 

Stroke, traumatic brain injury, and neurodegenerative diseases such as Parkinson’s disease, multiple sclerosis and amyotrophic lateral sclerosis (ALS, or Lou Gehrig’s disease) often result in an irreversible loss of the ability to speak. Some people with severe speech disabilities learn to spell out their thoughts letter-by-letter using assistive devices that track very small eye or facial muscle movements. However, producing text or synthesized speech with such devices is laborious, error-prone, and painfully slow, typically permitting a maximum of 10 words per minute, compared to the 100 to 150 words per minute of natural speech.

The new system being developed in the laboratory of Edward Chang, MD – described April 24, 2019, in Nature – demonstrates that it is possible to create a synthesized version of a person’s voice that can be controlled by the activity of their brain’s speech centers. In the future, this approach could not only restore fluent communication to individuals with severe speech disability, the authors say, but could also reproduce some of the musicality of the human voice that conveys the speaker’s emotions and personality.

“For the first time, this study demonstrates that we can generate entire spoken sentences based on an individual’s brain activity,” said Chang, a professor of neurological surgery and member of the UCSF Weill Institute for Neuroscience. “This is an exhilarating proof of principle that with technology that is already within reach, we should be able to build a device that is clinically viable in patients with speech loss.”

Scientists have used an imaging technique to reconstruct the brain architecture and neural networks of the thylacine – better known as the Tasmanian tiger – an extinct carnivorous marsupial native to Tasmania. The study, published in PLOS ONE, used magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) to scan postmortem specimens of two thylacine brain specimens, both of which were about 100 years old.

The results, when compared to the Tasmanian tiger’s closest living relative, the Tasmanian devil, suggest that the larger-brained thylacine had more cortex devoted to planning and decision-making.  

“The natural behavior of the thylacine was never scientifically documented,” says Gregory Berns, a neuroscientist at Emory University and the lead author of the study. “Our reconstruction of its white matter tracts, or neural wiring, between different regions of its brain is consistent with anecdotal evidence that the thylacine occupied a more complex, predatory ecological niche versus the scavenging niche of the Tasmanian devil.”

The comparative study also supports theories of brain evolution suggesting that as brains grow larger they become more modular, or divided into sections associated with discrete functions, Berns says.  

Kenneth Ashwell, an anatomist at the University of New South Wales School of Medical Sciences and an expert on the brain evolution of marsupials and monotremes, co-authored the study.

“The technology for imaging the preserved brains of rare, extinct and endangered species is an exciting innovation in the study of brain evolution,” Ashwell says. “It will allow us to track pathways and study functional connections that could never be analyzed through older experimental techniques.”

Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks.

Scientists now have continued their efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm C. elegans. They extended their previous work by considering the contributions made by hierarchical partitions of the connectome that form functional clusters, and they explored possible beneficial effects of inter-cluster inhibitory connections. The results showed that the self-optimization process can be applied to neural network topologies characterized by greater biological realism, and that long-range inhibitory connections can facilitate the generalization capacity of the process.

In a study of epilepsy patients, researchers at the National Institutes of Health monitored the electrical activity of thousands of individual brain cells, called neurons, as patients took memory tests. They found that the firing patterns of the cells that occurred when patients learned a word pair were replayed fractions of a second before they successfully remembered the pair. The study was part of an NIH Clinical Center trial for patients with drug-resistant epilepsy whose seizures cannot be controlled with drugs.

"Memory plays a crucial role in our lives. Just as musical notes are recorded as grooves on a record, it appears that our brains store memories in neural firing patterns that can be replayed over and over again," said Kareem Zaghloul, M.D., Ph.D., a neurosurgeon-researcher at the NIH's National Institute of Neurological Disorders and Stroke (NINDS) and senior author of the study published in Science.

Dr. Zaghloul's team has been recording electrical currents of drug-resistant epilepsy patients temporarily living with surgically implanted electrodes designed to monitor brain activity in the hopes of identifying the source of a patient's seizures. This period also provides an opportunity to study neural activity during memory. In this study, his team examined the activity used to store memories of our past experiences, which scientists call episodic memories.

In 1957, the case of an epilepsy patient H.M. provided a breakthrough in memory research. H.M could not remember new experiences after part of his brain was surgically removed to stop his seizures. Since then, research has pointed to the idea that episodic memories are stored, or encoded, as neural activity patterns that our brains replay when triggered by such things as the whiff of a familiar scent or the riff of a catchy tune. But exactly how this happens was unknown.

Over the past two decades, rodent studies have suggested that the brain may store memories in unique neuronal firing sequences. After joining Dr. Zaghloul's lab, Alex P. Vaz, B.S., an M.D., Ph.D. student at Duke University, Durham, North Carolina, and the leader of this study decided to test this idea in humans. "We thought that if we looked carefully at the data we had been collecting from patients we might be able to find a link between memory and neuronal firing patterns in humans that is similar to that seen in rodents," said Vaz, a bioengineer who specializes in deciphering the meaning of electrical signals generated by the body.

In order to do this they analyzed the firing patterns of individual neurons located in the anterior temporal lobe, a brain language center. Currents were recorded as patients sat in front of a screen and were asked to learn word pairs such as "cake" and "fox." The researchers discovered that unique firing patterns of individual neurons were associated with learning each new word pattern. Later, when a patient was shown one of the words, such as "cake," a very similar firing pattern was replayed just milliseconds before the patient correctly recalled the paired word "fox."

"These results suggest that our brains may use distinct sequences of neural spiking activity to store memories and then replay them when we remember a past experience," said Dr. Zaghloul.

A reliable set of functional brain networks is found in healthy people and thought to underlie our cognition, emotion, and behavior. Now, scientists investigated these networks by quantifying intrinsic functional connectivity in six individuals who had undergone surgical removal of one hemisphere. Hemispherectomy subjects and healthy controls were scanned with identical parameters on the same scanner and compared to a large normative sample (n = 1,482).

Surprisingly, hemispherectomy subjects and controls all showed strong and equivalent intrahemispheric connectivity between brain regions typically assigned to the same functional network. Connectivity between parts of different networks, however, was markedly increased for almost all hemispherectomy participants and across all networks.

These results support the hypothesis of a shared set of functional networks that underlie cognition and suggest that between-network interactions may characterize functional reorganization in hemispherectomy.

The phrase "positive reinforcement," is something you hear more often in an article about child rearing than one about artificial intelligence. But according to Alice Parker, Dean's Professor of Electrical Engineering in the Ming Hsieh Department of Electrical and Computer Engineering, a little positive reinforcement is just what our AI machines need. Parker has been building electronic circuits for over a decade to reverse-engineer the human brain to better understand how it works and ultimately build artificial systems that mimic it. Her most recent paper, co-authored with Ph.D. student Kun Yue and colleagues from UC Riverside, was just published in the journal Science Advances and takes an important step towards that ultimate goal.

Neuromorphic computing is an approach to efficiently solve complicated learning and cognition problems like the human brain using electronics. To efficiently implement the functionality of biological neurons, nanodevices and their implementations in circuits are exploited. Here, we describe a general-purpose spiking neuromorphic system that can solve on-the-fly learning problems, based on magnetic domain wall analog memristors (MAMs) that exhibit many different states with persistence over the lifetime of the device. The research includes micromagnetic and SPICE modeling of the MAM, CMOS neuromorphic analog circuit design of synapses incorporating the MAM, and the design of hybrid CMOS/MAM spiking neuronal networks in which the MAM provides variable synapse strength with persistence. Using this neuronal neuromorphic system, simulations show that the MAM-boosted neuromorphic system can achieve persistence, can demonstrate deterministic fast on-the-fly learning with the potential for reduced circuitry complexity, and can provide increased capabilities over an all-CMOS implementation.

Scientists who peered inside snoozing zebrafish have spotted some strikingly familiar patterns of activity. Zebrafish, like the adult seen here, seem to experience sleep cycles that are similar to REM sleep in humans, according to a new study that looked at neural activity in the tiny aquatic animals.

Scientists actually published a map of the C. elegans nervous system back in 1986. The work involved analyzing neural structures on thousands of images and manually connecting the dots, creating a web of around 5,000 connections between the structures on one image and another. This work was critical in making the worm a popular model for studying human biology, but the map wasn't overly detailed, only describing the nervous system of the female worm and leaving out large sections of the body.

Fast-forward a few decades and scientists have now filled in the blanks. Led by Scott Emmons, a professor of genetics at Albert Einstein College of Medicine, the researchers combined these older roundworm images with new ones and then employed purpose-built software to stitch them together, forming complete wiring diagrams for both sexes of C. elegans. These include all the connections between the neurons and the worm's muscles, tissues like the gut and skin, and the synapses between the muscle cells.

"It was simply digital imaging," Emmons explains to New Atlas. "A digital camera on the electron microscope, digital scanners for the old prints and the PC, which weren't available to the original mappers in the 1970s. The new connectomes are now in digital format."

The early surveys of these maps reveal some interesting insights. For example, Emmons says the synaptic pathways between the two sexes are similar, but they observed differences in the strength of some, particularly those related to reproductive functions in the female and those related to copulation in the male.

The researchers describe this achievement as a major milestone in the field of connectomics, as it is known, and say the complete diagram can serve as a starting point for further exploration of how these neural connections control the worm's behavior. And because of the molecules it shares with the human nervous system, it could help us better understand that and possibly even uncover new therapeutic treatments for some neurological conditions further down the track.

Two researchers — Robert McIntyre, an MIT graduate, and Gregory M. Fahy, PhD., 21st Century Medicine (21CM) Chief Scientific Officer, have developed a method for scanning a preserved brain’s connectome (the 150 trillion microscopic synaptic connections presumed to encode all of a person’s knowledge). That data could possibly be used, centuries later, to reconstruct a whole-brain emulation — uploading your mind into a computer or Avatar-style robotic, virtual, or synthetic body, McIntyre and others suggest.

According to MIT Technology Review, McIntyre has formed a startup company called Nectome that has won a large NIH grant for creating “technologies to enable whole-brain nanoscale preservation and imaging.” McIntyre is also collaborating with Edward Boyden, PhD., a top neuroscientist at MIT and inventor of a new “expansion microscopy” technique (to achieve super-resolution with ordinary confocal microscopes), as KurzweilAI reported. The technique also causes brain tissue to swell, making it more accessible.

Preserving brain information patterns, not biological function

Unlike cryonics (freezing people or heads for future revival), the researchers did not intend to revive a pig or pig brain (or human, in the future). Instead, the idea is to develop a bridge to future mind-uploading technology by preserving the information content of the brain, as encoded within the frozen connectome.

The first step in the ASC procedure is to perfuse the brain’s vascular system with the toxic fixative glutaraldehyde (typically used as an embalming fluid but also used by neuroscientists to prepare brain tissue for the highest resolution electron microscopic and immunofluorescent examination). That instantly halts metabolic processes by covalently crosslinking the brain’s proteins in place, leading to death (by contemporary standards). The brain is then quickly stored at -130 degrees C, stopping all further decay.

The method, tested on a pig’s brain, led to 21st Century Medicine (21CM), lead researcher McIntyre, and senior author Fahy winning the $80,000 Large Mammal Brain Preservation Prize offered by the Brain Preservation Foundation (BPF), announced March 13, 2018.

New study shows that frogs make cognitive maps.

A cognitive map is a mental representation of the external world, and our place in it, allowing us to plot the most efficient route to any destination. Humans, other mammals and many bird species use them – and now for the first time, amphibians have been shown to do so, too

Poison arrow frogs, members of the Dendrobatidae family and native to South and Central America, have a parenting style that requires sophisticated navigation and spatial recall in their complex rainforest habitat.

After their eggs hatch in damp leaf litter on the forest floor, the adults transport the tadpoles, one or two at a time, to tiny ephemeral pools of water in tree holes and epiphytes such as bromeliads. The parents spend considerable time locating and monitoring the pools to ensure their tadpoles remain in water. 

Field based studies on the frogs have suggested that parents most probably use a cognitive map to locate their tadpoles, but laboratory experiments are necessary to conclusively establish its existence.

A team of US researchers, led by Sabrina Burmeister from the University of North Carolina, Chapel Hill, trained five adult green-and-black poison dart frogs (Dendrobates auratus) to use a modified version of the Morris water maze, which they dubbed the moat maze.

The Morris water maze has been used with some success with rats in cognitive map research, with trained animals learning visual cues to locate a hidden platform under opaque water, and thus be able to stop swimming.

The brain's capacity for simultaneously learning and memorizing large amounts of information while requiring little energy has inspired an entire field to pursue brain-like - or neuromorphic - computers. Researchers at Stanford University and Sandia National Laboratories previously developed one portion of such a computer: a device that acts as an artificial synapse, mimicking the way neurons communicate in the brain.

In a paper published online by the journal Science on April 25, the team reports that a prototype array of nine of these devices performed even better than expected in processing speed, energy efficiency, reproducibility and durability.

Looking forward, the team members want to combine their artificial synapse with traditional electronics, which they hope could be a step toward supporting artificially intelligent learning on small devices.

"If you have a memory system that can learn with the energy efficiency and speed that we've presented, then you can put that in a smartphone or laptop," said Scott Keene, co-author of the paper and a graduate student in the lab of Alberto Salleo, professor of materials science and engineering at Stanford who is co-senior author. "That would open up access to the ability to train our own networks and solve problems locally on our own devices without relying on data transfer to do so."

For the first time, researchers at the Netherlands Institute for Neuroscience were able to test the theory of empathy in rats.

Researchers have found that the rat brain activates the same cells when they observe the pain of others as when they experience pain themselves. In addition, without activity of these 'mirror neurons,' the animals no longer share the pain of others. Finding the neural basis for sharing the emotions of others is an exciting step towards understanding empathy.

Why is it that we can get sad, when we see someone else crying? Why is it that we wince, when a friend cuts his finger? Researchers from the Netherlands Institute for Neuroscience have found that the rat brain activates the same cells when they observe the pain of others as when they experience pain themselves. In addition, without activity of these "mirror neurons," the animals no longer share the pain of others. As many psychiatric disorders are characterized by a lack of empathy, finding the neural basis for sharing the emotions of others, and being able to modify how much an animal shares the emotions of others, is an exciting step towards understanding empathy and these disorders. The findings will be published in the leading journal Current Biology on April 11th, 2019.

Human neuroimaging studies have shown that when we experience pain ourselves, we activate a region of the brain called "the cingulate cortex." When we see someone else in pain, we reactivate the same region. On the basis of this, researchers formulated two speculations: (a) the cingulate cortex contains mirror neurons, i.e. neurons that trigger our own feeling of pain and are reactivated when we see the pain of others, and (b) that this is the reason why we wince and feel pain while seeing the pain of others. This intuitively plausible theory of empathy however remained untested because it is not possible to record the activity of individual brain cells in humans. Moreover, it is not possible to modulate brain activity in the human cingulate cortex to determine whether this brain region is responsible for empathy.

Scientists just activated the world’s biggest “brain”: a supercomputer with a million processing cores and 1,200 interconnected circuit boards that together operate like a human brain. Ten years in the making, it is the world’s largest neuromorphic computer—a type of computer that mimics the firing of neurons—scientists announced on Nov. 2, 2018.

Dubbed Spiking Neural Network Architecture, or SpiNNaker, the computer powerhouse is located at the University of Manchester in the United Kingdom, and it “rethinks the way conventional computers work,” project member Steve Furber, a professor of computer engineering at the University of Manchester, said in a statement. But SpiNNaker doesn’t just “think” like a brain. It creates models of the neurons in human brains, and it simulates more neurons in real time than any other computer on Earth, according to the statement.

“Its primary task is to support partial brain models: for example, models of cortex, of basal ganglia, or multiple regions expressed typically as networks of spiking [or firing] neurons,” Furber told Live Science in an email.

Since April 2016, SpiNNaker has been simulating neuron activity using 500,000 core processors, but the upgraded machine has twice that capacity, Furber explained. With the support of the European Union’s Human Brain Project—an effort to construct a virtual human brain—SpiNNaker will continue to enable scientists to create detailed brain models. But now it has the capacity to perform 200 quadrillion actions simultaneously, university representatives reported in the statement.

While some other computers may rival SpiNNaker in the number of processors they contain, what sets this platform apart is the infrastructure connecting those processors. In the human brain, 100 billion neurons simultaneously fire and transmit signals to thousands of destinations. SpiNNaker’s architecture supports an exceptional level of communication among its processors, behaving much like a brain’s neural network does, Furber explained.

“Conventional supercomputers have connectivity mechanisms that are much less well suited to real-time brain modeling,” he said. “SpiNNaker is, I believe, capable of modeling larger spiking neural networks in biological real time than any other machine.”

Previously, when SpiNNaker was operating with only 500,000 processors, it modeled 80,000 neurons in the cortex, the brain region that moderates data from the senses. Another SpiNNaker simulation of the basal ganglia, a brain area affected by Parkinson’s disease, hints at the computer’s potential as a tool for studying brain disorders, according to the statement.