The possibility of direct interfacing between biological and technological information devices could result in a merger of mind and machine — Ultimate Computing. This book, a thorough consideration of this idea, involves a number of disciplines, including biochemistry, cognitive science, computer science, engineering, mathematics, microbiology, molecular biology, pharmacology, philosophy, physics, physiology, and psychology.
Silicon-based complementary metal-oxide semiconductors or negative differential resistance device circuits can emulate neural features, yet are complicated to fabricate and not biocompatible. Here, the authors report an ion-modulated antiambipolarity in mixed ion–electron conducting polymers demonstrating capability of sensing, spiking, emulating the most critical biological neural features, and stimulating biological nerves in vivo.
Born tail first, bottlenose dolphin calves are initially adorned with two delicate rows of whiskers along their snout, resembling the tactile whiskers of seals. However, these whiskers are shed shortly after birth, leaving behind a pattern of indentations called vibrissal pits. Recently, Tim Hüttner and Guido Dehnhardt, researchers from the University of Rostock in Germany, began to suspect that these pits might serve a purpose beyond being mere remnants.
Could they allow adult bottlenose dolphins to sense weak electric fields? Taking an initial close look, they realized that the remnant pits resemble the structures that allow sharks to detect electric fields, and when they checked whether captive bottlenose dolphins could sense an electric field in water, all of the animals felt the field.
‘It was very impressive to see,’ says Dehnhardt, who recently published the extraordinary discovery and how the animals could use their electric sense in the Journal of Experimental Biology.
When large stars or celestial bodies explode near Earth, their debris can reach our solar system. Evidence of these cosmic events is found on Earth and the Moon, detectable through accelerator mass spectrometry (AMS). An overview of this exciting research was recently published in the scientific journal Annual Review of Nuclear and Particle Science by Prof. Anton Wallner of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), who soon plans to decisively advance this promising branch of research with the new, ultrasensitive AMS facility “HAMSTER.”
In their paper, HZDR physicist Anton Wallner and colleague Prof. Brian D. Fields from the University of Illinois in Urbana, USA, provide an overview of near-Earth cosmic explosions with a particular focus on events that occurred three and, respectively, seven million years ago.
“Fortunately, these events were still far enough away, so they probably did not significantly impact the Earth’s climate or have major effects on the biosphere. However, things get really uncomfortable when cosmic explosions occur at a distance of 30 light-years or less,” Wallner explains. Converted into the astrophysical unit parsec, this corresponds to less than eight to ten parsecs.
Summary: A groundbreaking study by physicists and neuroscientists reveals that the connectivity among neurons stems from universal networking principles, not just biological specifics.
Analyzing various model organisms, researchers found a consistent “heavy-tailed” distribution of neural connections, guided by Hebbian dynamics, indicating that neuron connectivity relies on general network organization.
This discovery, transcending biology, potentially applies to non-biological networks like social interactions, offering insights into the fundamental nature of networking.
The incredible explosion in the power of artificial intelligence is evident in daily headlines proclaiming big breakthroughs. What are the remaining differences between machine and human intelligence? Could we simulate a brain on current computer hardware if we could write the software? What are the latest advancements in the world’s largest brain model? Participate in the discussion about what AI has done and how far it has yet to go, while discovering new technologies that might allow it to get there.
ABOUT THE SPEAKERS
CHRIS ELIASMITH is the Director of the Centre for Theoretical Neuroscience (CTN) at the University of Waterloo. The CTN brings together researchers across many faculties who are interested in computational and theoretical models of neural systems. Dr Eliasmith was recently elected to the new Royal Society of Canada College of New Scholars, Artists and Scientists, one of only 90 Canadian academics to receive this honour. He is also a Canada Research Chair in Theoretical Neuroscience. His book, ‘How to build a brain’ (Oxford, 2013), describes the Semantic Pointer Architecture for constructing large-scale brain models. His team built what is currently the world’s largest functional brain model, ‘Spaun’, and the first to demonstrate realistic behaviour under biological constraints. This ground-breaking work was published in Science (November, 2012) and has been featured by CNN, BBC, Der Spiegel, Popular Science, National Geographic and CBC among many other media outlets, and was awarded the NSERC Polayni Prize for 2015.
PAUL THAGARD is a philosopher, cognitive scientist, and author of many interdisciplinary books. He is Distinguished Professor Emeritus of Philosophy at the University of Waterloo, where he founded and directed the Cognitive Science Program. He is a graduate of the Universities of Saskatchewan, Cambridge, Toronto (PhD in philosophy) and Michigan (MS in computer science). He is a Fellow of the Royal Society of Canada, the Cognitive Science Society, and the Association for Psychological Science. The Canada Council has awarded him a Molson Prize (2007) and a Killam Prize (2013). His books include: The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change (MIT Press, 2012); The Brain and the Meaning of Life (Princeton University Press, 2010); Hot Thought: Mechanisms and Applications of Emotional Cognition (MIT Press, 2006); and Mind: Introduction to Cognitive Science (MIT Press, 1996; second edition, 2005). Oxford University Press will publish his 3-book Treatise on Mind and Society in early 2019.
Date/Time:
Wednesday, October 17, 2018 — 7:30pm.
Location:
Vanstone Lecture Hall, St. Jerome’s University Academic Centre.
Some technologies are so cool they make you do a double take. Case in point: robots being controlled by rat brains. Kevin Warwick, once a cyborg and still a researcher in cybernetics at the University of Reading, has been working on creating neural networks that can control machines. He and his team have taken the brain cells from rats, cultured them, and used them as the guidance control circuit for simple wheeled robots. Electrical impulses from the bot enter the batch of neurons, and responses from the cells are turned into commands for the device. The cells can form new connections, making the system a true learning machine. Warwick hasn’t released any new videos of the rat brain robot for the past few years, but the three older clips we have for you below are still awesome. He and his competitors continue to move this technology forward – animal cyborgs are real.
The skills of these rat-robot hybrids are very basic at this point. Mainly the neuron control helps the robot to avoid walls. Yet that obstacle avoidance often shows clear improvement over time, demonstrating how networks of neurons can grant simple learning to the machines. Whenever I watch the robots in the videos below I have to do a quick reality check – these machines are being controlled by biological cells! It’s simply amazing.
In Neuromorphic Computing Part 2, we dive deeper into mapping neuromorphic concepts into chips built from silicon. With the state of modern neuroscience and chip design, the tools the industry is working with we’re working with are simply too different from biology. Mike Davies, Senior Principal Engineer and Director of Intel’s Neuromorphic Computing Lab, explains the process and challenge of creating a chip that can replicate some of the form and functions in biological neural networks.
Mike’s leadership in this specialized field allows him to share the latest insights from the promising future in neuromorphic computing here at Intel. Let’s explore nature’s circuit design of over a billion years of evolution and today’s CMOS semiconductor manufacturing technology supporting incredible computing efficiency, speed and intelligence.
Architecture All Access Season 2 is a master class technology series, featuring Senior Intel Technical Leaders taking an educational approach in explaining the historical impact and future innovations in their technical domains. Here at Intel, our mission is to create world-changing technology that improves the life of every person on earth. If you would like to learn more about AI, Wi-Fi, Ethernet and Neuromorphic Computing, subscribe and hit the bell to get instant notifications of new episodes.
Jump to Chapters:
0:00 Welcome to Neuromorphic Computing.
0:30 How to architect a chip that behaves like a brain.
1:29 Advantages of CMOS semiconductor manufacturing technology.
2:18 Objectives in our design toolbox.
2:36 Sparse distributed asynchronous communication.
4:51 Reaching the level of efficiency and density of the brain.
6:34 Loihi 2 a fully digital chip implemented in a standard CMOS process.
6:57 Asynchronous vs Synchronous.
7:54 Function of the core’s memory.
8:13 Spikes and Table Lookups.
9:24 Loihi learning process.
9:45 Learning rules, input and the network.
10:12 The challenge of architecture and programming today.
10:45 Recent publications to read.
Architecture all access season 2 playlist — • architecture all access season 2
Intel Wireless Technology — https://intel.com/wireless.
Computer design has always been inspired by biology, especially the brain. In this episode of Architecture All Access — Mike Davies, Senior Principal Engineer and Director of Intel’s Neuromorphic Computing Lab — explains the relationship of Neuromorphic Computing and understanding the principals of brain computations at the circuit level that are enabling next-generation intelligent devices and autonomous systems.
Mike’s leadership in this specialized field allows him to share the latest insights from the promising future in neuromorphic computing here at Intel. Discover the history and influence of the secrets that nature has evolved over a billion years supporting incredible computing efficiency, speed and intelligence.
Architecture All Access Season 2 is a master class technology series, featuring Senior Intel Technical Leaders taking an educational approach in explaining the historical impact and future innovations in their technical domains. Here at Intel, our mission is to create world-changing technology that improves the life of every person on earth. If you would like to learn more about AI, Wi-Fi, Ethernet and Neuromorphic Computing, subscribe and hit the bell to get instant notifications of new episodes.
Chapters:
0:00 Welcome to Neuromorphic Computing.
1:16 Introduction to Mike Davies.
1:34 The pioneers of modern computing.
1:48 A 2 GR. brain running on 50 mW of power.
2:19 The vision of Neuromorphic Computing.
2:31 Biological Neural Networks.
4:03 Patterns of Connectivity explained.
4:36 How neural networks achieve great energy efficiency and low latency.
6:20 Inhibitory Networks of Neurons.
7:42 Conventional Architecture.
8:01 Neuromorphic Architecture.
9:51 Conventional processors vs Neuromorphic chips.
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Computer simulations of complex systems provide an opportunity to study their time evolution under user control. Simulations of neural circuits are an established tool in computational neuroscience. Through systematic simplification on spatial and temporal scales they provide important insights in the time evolution of networks which in turn leads to an improved understanding of brain functions like learning, memory or behavior. Simulations of large networks are exploiting the concept of weak scaling where the massively parallel biological network structure is naturally mapped on computers with very large numbers of compute nodes. However, this approach is suffering from fundamental limitations. The power consumption is approaching prohibitive levels and, more seriously, the bridging of time-scales from millisecond to years, present in the neurobiology of plasticity, learning and development is inaccessible to classical computers. In the keynote I will argue that these limitations can be overcome by extreme approaches to weak and strong scaling based on brain-inspired computing architectures.
Bio: Karlheinz Meier received his PhD in physics in 1984 from Hamburg University in Germany. He has more than 25years of experience in experimental particle physics with contributions to 4 major experiments at particle colliders at DESY in Hamburg and CERN in Geneva. For the ATLAS experiment at the Large Hadron Collider (LHC) he led a 15 year effort to design, build and operate an electronics data processing system providing on-the-fly data reduction by 3 orders of magnitude enabling among other achievements the discovery of the Higgs Boson. Following scientific staff positions at DESY and CERN he was appointed full professor of physics at Heidelberg university in 1992. In Heidelberg he co-founded the Kirchhoff-Institute for Physics and a laboratory for the development of microelectronic circuits for science experiments. In particle physics he took a leading international role in shaping the future of the field as president of the European Committee for Future Accelerators (ECFA). Around 2005 he gradually shifted his scientific interests towards large-scale electronic implementations of brain-inspired computer architectures. His group pioneered several innovations in the field like the conception of a description language for neural circuits (PyNN), time-compressed mixed-signal neuromorphic computing systems and wafer-scale integration for their implementation. He led 2 major European initiatives, FACETS and BrainScaleS, that both demonstrated the rewarding interdisciplinary collaboration of neuroscience and information science. In 2009 he was one of the initiators of the European Human Brain Project (HBP) that was approved in 2013. In the HBP he leads the subproject on neuromorphic computing with the goal of establishing brain-inspired computing paradigms as tools for neuroscience and generic methods for inference from large data volumes.
