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AI Machine Learning Breakthrough Is a Twist on Brain Replay

#artificialintelligence

Recently, researchers affiliated with the Baylor College of Medicine, the University of Cambridge, the University of Massachusetts Amherst, and Rice University created a new way of adapting a neuroscience concept called "brain replay" to the digital realm of artificial neural networks to enable continuous learning. From a neuroscience perspective, the concept of brain replay is analogous to a streaming service that activates repeat showings from its vast archives of stored pre-recorded content. The brain can replay memories by reactivating the neural activity patterns that represent prior experiences, whether asleep or awake. This ability for memory replay starts in the hippocampus, then continues in the cortex. The research trio of Hava Siegelmann, Andreas Tolias, and Gido van de Ven published a study in Nature Communications on August 13, 2020, that shows state-of-the-art performance from neural networks by deploying a new twist on mimicking brain replay.


Yale researchers develop AI technology for adults with autism

#artificialintelligence

Researchers from several American universities are collaborating to develop artificial intelligence based software to help people on the autism spectrum find and hold meaningful employment. The project is a collaboration between experts at Vanderbilt, Yale, Cornell and the Georgia Institute of Technology. It consists of developing multiple pieces of technology, each one aimed at a different aspect of supporting people with Autism Spectrum Disorder (ASD) in the workplace, according to Nilanjan Sarkar, professor of engineering at Vanderbilt University and the leader of the project. "We realized together that there are some support systems for children with autism in this society, but as soon as they become 18 years old and more, there is a support cliff and the social services are not as much," Sarkar said. The project began a year ago with preliminary funding from the National Science Foundation. The NSF initially invested in around 40 projects, but only four -- including this one -- were chosen to be funded for a longer term of two years.


Deep learning for next-generation sleep diagnostics

#artificialintelligence

Currently, the diagnosis of sleep disorders relies on polysomnographic recordings with a time-consuming manual analysis with low reliability between different manual scorers. Throughout the night, sleep stages are identified manually in non-overlapping 30-second epochs starting from the onset of the recording based on electroencephalography (EEG), electro-oculography (EOG), and chin electromyography (EMG) signals which require meticulous placement of electrodes. Moreover, the diagnosis of many sleep disorders relies on outdated guidelines. When assessing the severity of obstructive sleep apnea (OSA), the patients are classified based on thresholds of the apnea-hypopnea index (AHI), i.e. the number of respiratory disruptions during sleep. These thresholds are not fully based on solid scientific evidence and remain the same across different measurement techniques.


Magnetic microbots can hook up brain cells to make a neural network

New Scientist

Tiny robots that can transport individual neurons and connect them to form active neural circuits could help us study brain disorders such as Alzheimer's disease. The robots, which were developed by Hongsoo Choi at the Daegu Gyeongbuk Institute of Science and Technology in South Korea and his colleagues, are 300 micrometres long and 95 micrometre wide. They are made from a polymer coated with nickel and titanium and their movement can be controlled with external magnetic fields.


Deep learning helps explore the structural and strategic bases of autism?

#artificialintelligence

Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person's behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the'bible' of mental health diagnosis. However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult. But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability.


Monash University researchers speed up epilepsy diagnosis with machine learning

ZDNet

A new study by Monash University, together with Alfred Health and The Royal Melbourne Hospital, has uncovered how machine learning technology could be used to automate epilepsy diagnosis. As part of the study, Monash University researchers applied over 400 electroencephalogram (EEG) recordings of patients with and without epilepsy from Alfred Health and The Royal Melbourne hospital to a machine learning model. Training the model with the various datasets enabled it to automatically detect signs of epilepsy -- or abnormal activities known as "spikes" in EEG recordings. "The objective of the first stage is to evaluate existing patterns involved in the detection of abnormal electrical recordings among neurons in the brain, called epileptiform activity. These abnormalities are often sharp spikes which stand out from the rhythmic patterns of a patient's EEG scan," explained Levin Kuhlmann, Monash University senior lecturer at the Faculty of IT Department of Data Science and AI.


Voice-based applications for E-Health – H2020 COMPRISE

#artificialintelligence

Healthcare has been one of the countless beneficiaries of the revolutionary advances that widespread computing has brought. Fast, efficient data organisation, storage and access that have greatly sped up the medical enterprise, yet many low hanging fruits remain hanging. Chief among those is the increased application of technologies that can process speech. In this post, we'll share with you how speech technology can improve healthcare in the three following ways. Finally, (3) voice signal analysis can be used for earlier diagnosis and to help track the changes of medical condition over time.


A cortex-like canonical circuit in the avian forebrain

Science

Mammals can be very smart. They also have a brain with a cortex. It has thus often been assumed that the advanced cognitive skills of mammals are closely related to the evolution of the cerebral cortex. However, birds can also be very smart, and several bird species show amazing cognitive abilities. Although birds lack a cerebral cortex, they do have pallium, and this is considered to be analogous, if not homologous, to the cerebral cortex. An outstanding feature of the mammalian cortex is its layered architecture. In a detailed anatomical study of the bird pallium, Stacho et al. describe a similarly layered architecture. Despite the nuclear organization of the bird pallium, it has a cyto-architectonic organization that is reminiscent of the mammalian cortex. Science , this issue p. [eabc5534][1] ### INTRODUCTION For more than a century, the avian forebrain has been a riddle for neuroscientists. Birds demonstrate exceptional cognitive abilities comparable to those of mammals, but their forebrain organization is radically different. Whereas mammalian cognition emerges from the canonical circuits of the six-layered neocortex, the avian forebrain seems to display a simple nuclear organization. Only one of these nuclei, the Wulst, has been generally accepted to be homologous to the neocortex. Most of the remaining pallium is constituted by a multinuclear structure called the dorsal ventricular ridge (DVR), which has no direct counterpart in mammals. Nevertheless, one long-standing theory, along with recent scientific evidence, supports the idea that some parts of the sensory DVR could display connectivity patterns, physiological signatures, and cell type–specific markers that are reminiscent of the neocortex. However, it remains unknown if the entire Wulst and sensory DVR harbor a canonical circuit that structurally resembles mammalian cortical organization. ### RATIONALE The mammalian neocortex comprises a columnar and laminar organization with orthogonally organized fibers that run in radial and tangential directions. These fibers constitute repetitive canonical circuits as computational units that process information along the radial domain and associate it tangentially. In this study, we first analyzed the pallial fiber architecture with three-dimensional polarized light imaging (3D-PLI) in pigeons and subsequently reconstructed local sensory circuits of the Wulst and the sensory DVR in pigeons and barn owls by means of in vivo or in vitro applications of neuronal tracers. We focused on two distantly related bird species to prove the hypothesis that a canonical circuit comparable to the neocortex is a genuine feature of the avian sensory forebrain. ### RESULTS The 3D-PLI fiber analysis showed that both the Wulst and the sensory DVR display an orthogonal organization of radially and tangentially organized fibers along their entire extent. In contrast, nonsensory components of the DVR displayed a complex mosaic-like arrangement with patches of fibers with different orientations. Fiber tracing revealed an iterative circuit motif that was present across modalities (somatosensory, visual, and auditory), brain regions (sensory DVR and Wulst), and species (pigeon and barn owl). Although both species showed a comparable column- and lamina-like circuit organization, small species differences were discernible, particularly for the Wulst, which was more subdifferentiated in barn owls, which fits well with the processing of stereopsis, combined with high visual acuity in the Wulst of this species. The primary sensory zones of the DVR were tightly interconnected with the intercalated nidopallial layers and the overlying mesopallium. In addition, nidopallial and some hyperpallial lamina-like areas gave rise to long-range tangential projections connecting sensory, associative, and motor structures. ### CONCLUSION Our study reveals a hitherto unknown neuroarchitecture of the avian sensory forebrain that is composed of iteratively organized canonical circuits within tangentially organized lamina-like and orthogonally positioned column-like entities. Our findings suggest that it is likely that an ancient microcircuit that already existed in the last common stem amniote might have been evolutionarily conserved and partly modified in birds and mammals. The avian version of this connectivity blueprint could conceivably generate computational properties reminiscent of the neocortex and would thus provide a neurobiological explanation for the comparable and outstanding perceptual and cognitive feats that occur in both taxa. ![Figure][2] Fiber architectures of mammalian and avian forebrains. Schematic drawings of a rat brain (left) and a pigeon brain (right) depict their overall pallial organization. The mammalian dorsal pallium harbors the six-layered neocortex with a granular input layer IV (purple) and supra- and infragranular layers II/III and V/VI, respectively (blue). The avian pallium comprises the Wulst and the DVR, which both, at first glance, display a nuclear organization. Their primary sensory input zones are shown in purple, comparable to layer IV. According to this study, both mammals and birds show an orthogonal fiber architecture constituted by radially (dark blue) and tangentially (white) oriented fibers. Tangential fibers associate distant pallial territories. Whereas this pattern dominates the whole mammalian neocortex, in birds, only the sensory DVR and the Wulst (light green) display such an architecture, and the associative and motor areas (dark green), as in the caudal DVR, are devoid of this cortex-like fiber architecture. NC, caudal nidopallium. 3D RAT BRAIN (LEFT): SCALABLE BRAIN ATLAS, RESEARCH RESOURCE IDENTIFIER (RRID) SCR_006934 Although the avian pallium seems to lack an organization akin to that of the cerebral cortex, birds exhibit extraordinary cognitive skills that are comparable to those of mammals. We analyzed the fiber architecture of the avian pallium with three-dimensional polarized light imaging and subsequently reconstructed local and associative pallial circuits with tracing techniques. We discovered an iteratively repeated, column-like neuronal circuitry across the layer-like nuclear boundaries of the hyperpallium and the sensory dorsal ventricular ridge. These circuits are connected to neighboring columns and, via tangential layer-like connections, to higher associative and motor areas. Our findings indicate that this avian canonical circuitry is similar to its mammalian counterpart and might constitute the structural basis of neuronal computation. [1]: /lookup/doi/10.1126/science.abc5534 [2]: pending:yes


A neural correlate of sensory consciousness in a corvid bird

Science

Humans have tended to believe that we are the only species to possess certain traits, behaviors, or abilities, especially with regard to cognition. Occasionally, we extend such traits to primates or other mammals—species with which we share fundamental brain similarities. Over time, more and more of these supposed pillars of human exceptionalism have fallen. Nieder et al. now argue that the relationship between consciousness and a standard cerebral cortex is another fallen pillar (see the Perspective by Herculano-Houzel). Specifically, carrion crows show a neuronal response in the palliative end brain during the performance of a task that correlates with their perception of a stimulus. Such activity might be a broad marker for consciousness. Science , this issue p. [1626][1]; see also p. [1567][2] Subjective experiences that can be consciously accessed and reported are associated with the cerebral cortex. Whether sensory consciousness can also arise from differently organized brains that lack a layered cerebral cortex, such as the bird brain, remains unknown. We show that single-neuron responses in the pallial endbrain of crows performing a visual detection task correlate with the birds’ perception about stimulus presence or absence and argue that this is an empirical marker of avian consciousness. Neuronal activity follows a temporal two-stage process in which the first activity component mainly reflects physical stimulus intensity, whereas the later component predicts the crows’ perceptual reports. These results suggest that the neural foundations that allow sensory consciousness arose either before the emergence of mammals or independently in at least the avian lineage and do not necessarily require a cerebral cortex. [1]: /lookup/doi/10.1126/science.abb1447 [2]: /lookup/doi/10.1126/science.abe0536


Birds do have a brain cortex--and think

Science

The term “birdbrain” used to be derogatory. But humans, with their limited brain size, should have known better than to use the meager proportions of the bird brain as an insult. Part of the cause for derision is that the mantle, or pallium, of the bird brain lacks the obvious layering that earned the mammalian pallium its “cerebral cortex” label. However, birds, and particularly corvids (such as ravens), are as cognitively capable as monkeys ([ 1 ][1]) and even great apes ([ 2 ][2]). Because their neurons are smaller, the pallium of songbirds and parrots actually comprises many more information-processing neuronal units than the equivalent-sized mammalian cortices ([ 3 ][3]). On page 1626 of this issue, Nieder et al. ([ 4 ][4]) show that the bird pallium has neurons that represent what it perceives—a hallmark of consciousness. And on page 1585 of this issue, Stacho et al. ([ 5 ][5]) establish that the bird pallium has similar organization to the mammalian cortex. The studies of Nieder et al. and Stacho et al. are noteworthy in their own ways, but not because either is the first demonstration of close parallels between mammalian and bird pallia. That neuroscientists still refer to how bird cognition happens “without a cerebral cortex” ([ 6 ][6]), as Nieder et al. have done themselves ([ 4 ][4]), is a testament to how neuroscience has grown so much that specialists in different subfields often are not familiar with each other's findings, even when groundbreaking. Stating that birds do not have a cerebral cortex has been doubly wrong for several years. Birds do have a cerebral cortex, in the sense that both their pallium and the mammalian counterpart are enormous neuronal populations derived from the same dorsal half of the second neuromere in neural tube development ([ 7 ][7]). The second neuromere is important: The pallium of birds and mammals lies posterior to the hypothalamus, the true front part of the brain, which is then saddled in development by the rapidly bulging pallium. Owing to the painstaking, systematic comparative analyses of expression patterns of multiple homeobox (Hox) genes that compartmentalize embryonic development, it is now understood that in both birds and mammals, the pallium rests on top of all the neuronal loops formed between spinal cord, hindbrain, midbrain, thalamus, and hypothalamus. In both birds and mammals, the pallium is the population of neurons that are not a necessary part of the most fundamental circuits that operate the body. But because the pallium receives copies, through the thalamus, of all that goes on elsewhere, these pallial neurons create new associations that endow animal behavior with flexibility and complexity. So far, it appears that the more neurons there are in the pallium as a whole, regardless of pallial, brain, or body size, the more cognitive capacity is exhibited by the animal ([ 8 ][8]). Humans remain satisfyingly on top: Despite having only half the mass of an elephant pallium, the human version still has three times its number of neurons, averaging 16 billion ([ 9 ][9]). Corvids and parrots have upwards of half a billion neurons in their pallia and can have as many as 1 or 2 billion—like monkeys ([ 3 ][3]). Additionally, it has been known since 2013 that the circuits formed by the pallial neurons are functionally organized in a similar manner in birds as they are in mammals ([ 10 ][10]). Using resting-state neuroimaging to infer functional connectivity, the pigeon pallium was shown to be functionally organized and internally connected just like a mouse, monkey, or human pallium, with sensory areas, effector areas, richly interconnected hubs, and highly associative areas in the hippocampus and nidopallium caudolaterale. The nidopallium caudolaterale is the equivalent of the monkey prefrontal cortex ([ 10 ][10]), the portion of the pallium that is the seat of the ability to act on thoughts, feelings, and decisions, according to the current reality informed by the senses. Now, adding to their resting-state neuroimaging tool set the power and high resolution of polarized light microscopy to examine anatomical connectivity, Stacho et al. show that the pallia of pigeons and owls, like that of mice, monkeys, and humans, is criss-crossed by fibers that run in orthogonal planes. Repeated imaging of the brain with light shone at different orientations revealed that fibers within and across bird pallial areas are mostly (although not exclusively) organized at right angles, reminiscent of the orthogonal tangential and radial organization of cortical fibers in mammals ([ 11 ][11]). The broadminded neuroscientist with some knowledge of developmental biology might not find this surprising; what would be the alternative, a spaghetti-like disorganized jumble of fibers? But then again, the mantra that “birds do not have a cortex” even though they share pallial development and organization with mammals has been repeated so exhaustively that recognizing that columns and layers are actually observed—visible under polarized light if not to the naked eye—brings new hope that this mantra will join the ranks of myth. If the bird pallium as a whole is organized just like the mammalian pallium, then it follows that the part of the bird pallium that is demonstrably functionally connected like the mammalian prefrontal pallium (the nidopallium caudolaterale) should also function like it. Nieder et al. , who established previously that corvids, like macaques, have sensory neurons that represent numeric quantities ([ 12 ][12]), now move on to this associative part of the bird pallium. They find that, like the macaque prefrontal cortex, the associative pallium of crows is rich in neurons that represent what the animals next report to have seen—whether or not that is what they were shown. This representation develops over the time lapse of 1 to 2 s between the stimulus disappearing and the animal reporting what it perceived by pecking at a screen either for “yes, there was a stimulus” or for “no, there was no stimulus,” depending on a variable contingency rule. The early activity of these neurons still reflects the physical stimulus presented to the animal, which indicates that they receive secondhand sensory signals. However, as time elapses and (presumably) recurrent, associative cortical circuits progressively shape neuronal activity, the later component of the responses of the same neurons predicts instead what the animal then reports: Did it see a stimulus that indeed was there, or did it think the stimulus was there enough to report it—even if it was not? Future studies will certainly delve into more complex mental content than simply “Was it there or not?”, but concluding that birds do have what it takes to display consciousness—patterns of neuronal activity that represent mental content that drives behavior—now appears inevitable. Because the common ancestor to birds (and non-avian reptiles) and mammals lived 320 million years ago, Nieder et al. infer that consciousness might already have been present then—or might have appeared independently in birds and mammals through convergent evolution. Those hypotheses miss an important point: how fundamental properties of life present themselves at different scales. The widespread occurrence of large mammalian bodies today does not mean that ancestral mammals were large (they were not), nor do the nearly ubiquitous folded cortices of most large mammals today imply that the ancestral cortex was folded [it was not ([ 13 ][13])]. The physical properties that make self-avoiding surfaces buckle and fold as they expand under unequal forces apply equally to tiny and enormous cortices, but folds only present themselves past a certain size ([ 14 ][14]). Expansion of the cortical surface relative to its thickness is required for folds to appear. But that does not imply that folding evolved, because the physical principles that cause it to emerge were always there. Perhaps the same is true of consciousness: The underpinnings are there whenever there is a pallium, or something connected like a pallium, with associative orthogonal short- and long-range loops on top of the rest of the brain that add flexibility and complexity to behavior. But the level of that complexity, and the extent to which new meanings and possibilities arise, should still scale with the number of units in the system. This would be analogous to the combined achievements of the human species when it consisted of just a few thousand individuals, versus the considerable achievements of 7 billion today. 1. [↵][15]1. E. L. MacLean et al ., Proc. Natl. Acad. Sci. U.S.A. 111, 2140 (2014). [OpenUrl][16][Abstract/FREE Full Text][17] 2. [↵][18]1. C. Kabadayi, 2. L. A. Taylor, 3. A. M. P. von Bayern, 4. M. Osvath , R. Soc. Open Sci. 3, 160104 (2016). [OpenUrl][19][CrossRef][20] 3. [↵][21]1. S. Olkowicz et al ., Proc. Natl. Acad. Sci. U.S.A. 113, 7255 (2016). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. A. Nieder et al ., Science 369, 1626 (2020). [OpenUrl][25][Abstract/FREE Full Text][26] 5. [↵][27]1. M. Stacho et al ., Science 369, eabc5534 (2020). [OpenUrl][28][Abstract/FREE Full Text][29] 6. [↵][30]1. O. Güntürkün, 2. T. Bugnyar , Trends Cogn. Sci. 20, 291 (2016). [OpenUrl][31][CrossRef][32][PubMed][33] 7. [↵][34]1. L. Puelles, 2. M. Harrison, 3. G. Paxinos, 4. C. Watson , Trends Neurosci. 36, 570 (2013). [OpenUrl][35][CrossRef][36][PubMed][37][Web of Science][38] 8. [↵][39]1. S. Herculano-Houzel , Curr. Opin. Behav. Sci. 16, 1 (2017). [OpenUrl][40] 9. [↵][41]1. S. Herculano-Houzel , The Human Advantage (MIT Press, 2016). 10. [↵][42]1. M. Shanahan, 2. V. P. Bingman, 3. T. Shimizu, 4. M. Wild, 5. O. Güntürkün , Front. Comput. Neurosci. 7, 89 (2013). [OpenUrl][43][CrossRef][44][PubMed][45] 11. [↵][46]1. V. J. Wedeen et al ., Science 335, 1628 (2012). [OpenUrl][47][Abstract/FREE Full Text][48] 12. [↵][49]1. A. Nieder , Curr. Opin. Behav. Sci. 16, 8 (2017). [OpenUrl][50] 13. [↵][51]1. T. B. Rowe, 2. T. E. Macrini, 3. Z.-X. Luo , Science 332, 955 (2011). [OpenUrl][52][Abstract/FREE Full Text][53] 14. [↵][54]1. B. Mota, 2. S. Herculano-Houzel , Science 349, 74 (2015). 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