DOCSLIB.ORG

5. Neuromorphic Chips

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
5. Neuromorphic Chips Neuromorphic chips for the Artificial Brain Jaeseung Jeong, Ph.D Program of Brain and Cognitive Engineering, KAIST Silicon-based artificial intelligence is not efficient Prediction, expectation, and error Artificial Information processor vs. Biological information processor Core 2 Duo Brain • 65 watts • 10 watts • 291 million transistors • 100 billion neurons • >200nW/transistor • ~100pW/neuron Comparison of scales Molecules Channels Synapses Neurons CNS 0.1nm 10nm 1mm 0.1mm 1cm 1m Silicon Transistors Logic Multipliers PIII Parallel Gates Processors Motivation and Objective of neuromorphic engineering Problem • As compared to biological systems, today’s intelligent machines are less efficient by a factor of a million to a billion in complex environments. • For intelligent machines to be useful, they must compete with biological systems. Human Cortex Computer Simulation for Cerebral Cortex 20 Watts 1010 Watts 10 Objective I.4 Liter 4x 10 Liters • Develop electronic, neuromorphic machine technology that scales to biological level for efficient artificial intelligence. 9 Why Neuromorphic Engineering? Interest in exploring Interest in building neuroscience neurally inspired systems Key Advantages • The system is dynamic: adaptation • What if our primitive gates were a neuron computation? a synapse computation? a piece of dendritic cable? • Efficient implementations compute in their memory elements – more efficient than directly reading all the coefficients. Biology and Silicon Devices Similar physics of biological channels and p-n junctions • Drift and Diffusion equations form a built-in Barrier (Nernst equation for cable theory) • Exponential distribution of particles (Ions in biology and electrons/holes in silicon) Both biological channels and transistors have a gating mechanism that modulates a channel. Inspiration Biological-Scale Neuromorphic Electronic Devices Human NeoCortex Neuromorphic Electronics 1010 intersection/cm2 in crossbar ~1010 synapses/cm2 arrays w/ 100 nm pitch ~5x108 transistors/cm2 in state of the ~106 Neurons/cm2 art CMOS ~5 x 108 long range axons ~30 Gbit/sec multiplexed digital @ ~1 Hz addressing Conclusion: Gross statistics of biological neural systems might be realized in modern electronics. Approved for Public Release, Distribution 14 Unlimited Why Analog? • Much lower power than digital • Can perform many computations faster and more efficiently than digital in a more complicated way. • Follows the same physical laws as biological systems Analog: Power Savings Gene's Law 100W DSP Power CADSP Power 1W Gene’s Law • Power consumption of integrated circuits 10mW decreases exponentially over time Programmable • Follows Moore’s Law Analog Power 0.1mW Savings • Analog computation yields tremendous power savings equal to a >20 year leap in >20 Year Leap 1mW in Technology technology Power Dissipated / MMAC 10nW 1980 1990 2000 2010 2020 2030 Year e.g., Analog Cochlear Model • 32 subbands at 44.1kHz • Analog consumes <5μW • cf. Digital CM consumes ~5mW (audio-streamlined DSP) • Analog power savings of >1000 times Bio-Inspired Systems using analog - digital Smart Embedded Low-Power Analog Sensors •Consumer Electronics •Implantable Devices •Hearing Aids •Subthreshold Design •Cochlear Implants Analog Programmability Powerful Mixed-Signal Systems Provides digital features to the analog domain •Programmability •Accuracy •Reconfigurability •“Silicon Simulation” Analog alleviates the burden of the digital Synapse: The memristor (memory resistor) • The memristor (memory resistor) is a missing non-linear passive two- terminal electrical component relating electric charge and magnetic flux linkage (Circuit theorist Leon Chua, 1971). • The memristor would hypothetically operate in the following way: The memristor's electrical resistance is not constant but depends on the history of current that had previously flowed through the device, i.e., its present resistance depends on how much electric charge has flowed in what direction through it in the past. • The device remembers its history—the so-called non-volatility property. When the electric power supply is turned off, the memristor remembers its most recent resistance until it is turned on again. This is a basic element for neuromorphic chips. Neuromorphic engineering • A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. • In recent times the term neuromorphic has been used to describe analog, digital, and mixed-mode analog/digital VLSI and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). • The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, threshold switches and transistors. Neuromorphic/Bio-Mimetic Engineering Neuromorphic/Bio-Mimetic Engineering – Using biology to inspire better engineering • High-quality processing • Low power consumption Sensorimotor Systems •Intelligent robotics •Intelligent controls •Locomotive systems Neurons Silicon Retina •Systems that learn •CMOS imagers •Systems that adapt •Intelligent imagers •Neural networks •Retinal implants •Understanding biology Electronic Nose Audio Systems •“Sniff out” odors •Audio front ends •Chemical sensors •Signal processing systems •Drug traffic control •Hearing aids •Bio terror detection •Cochlear implants Analog VLSI • Carver Mead and his students pioneered the development aVLSI technology for use in neural circuits • They developed a silicon retina which electronically emulated the first 3 layers of the retina Very-large-scale integration (VLSI) • Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. VLSI began in the 1970s when complex semiconductor and communication technologies were being developed. • The microprocessor is a VLSI device. Before the introduction of VLSI technology, most ICs had a limited set of functions they could perform. • An electronic circuit might consist of a CPU, ROM, RAM and other logic gates. VLSI lets IC designers add all of these into one chip. Why VLSI? • Cheaper (and easier to mass produce) • Smaller • Reduces power • Keeps everything contained • Reduces noise • Reduces coupling from the environment • Need a large number of transistors to perform real-world computations/tasks • Allows a high density or circuit elements (therefore, VLSI reduces costs) Artificial Neural Network Chips • Early neuromorphic architectures were artificial neural network chips • Examples: • ETANN : (1989) Entirely analog chip that was designed for feed forward artificial neural network operation. • Ni1000 : (1996) Significantly more powerful than ETANN, however has narrower functionality SYNAPSE-1 System Architecture SYNAPSE-1 is a modular system arranged as a 2D array of MA16s, weight memories, data units, and a control unit • As early as 2006, researchers at Georgia Tech published a field programmable neural array. This chip was the first in a line of many increasingly complex arrays of floating gate transistors that allows programmability of charge on the gates of MOSFETs to model the channel-ion characteristics of neurons in the brain and was one of the first cases of a silicon programmable array of neurons. • In November 2011, a group of MIT researchers created a computer chip that mimics the analog, ion-based communication in a synapse between two neurons using 400 transistors and standard CMOS manufacturing techniques. • In June 2012, Spintronic Researchers at Purdue presented a paper on design for a neuromorphic chip using lateral spin valves and memristors. • They argue that the architecture they have designed works in a similar way to neurons and can therefore be used to test various ways of reproducing the brain’s processing ability. In addition, they are significantly more energy efficient than conventional chips. Neurogrid • (2005) Neurogrid is a multi-chip system developed by Kwabena Boahen and his group at Stanford University • Objective is to emulate neurons • Composed of a 4x4 array of Neurocores • Each Neurocore contains a 256x256 array of neuron circuits with up to 6,000 synapse connections • Mead succeeded in mimicking ion-flow across a neuron's membrane with electron-flow through a transistor's channel—this should not have come as a surprise—the same physical forces are at work in both cases! • Stanford researchers made these chips programmable by coming up with a versatile ion-channel analog and reconfigurable synaptic connections. This tiny chip—packaged in black plastic and mounted on a printed circuit board—models 1,024 excitatory pyramidal cells and 256 inhibitory basket cells. Their cellular properties and synaptic organization are downloaded to the chip over a USB link, which also allows their activity to be visualized in real-time. The FACETS Project • (2005) Fast Analog Computing with Emergent Transient States (FACETS) • A project designed by an international collective of scientists and engineers funded by the European Union • Recently developed a chip containing 200,000 neuron circuits connected by 50 million synapses. Large Scale Simulations IBM: Blue Brain Project: IBM & EPFL (Switzerland) IBM Almaden Research Center Los Alamos National
Load more

Recommended publications
  • Behavioral Plasticity Through the Modulation of Switch Neurons
    Behavioral Plasticity Through the Modulation of Switch Neurons Vassilis Vassiliades, Chris Christodoulou Department of Computer Science, University of Cyprus, 1678 Nicosia, Cyprus Abstract A central question in artificial intelligence is how to design agents ca- pable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mecha- nisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction of a type of artificial neuron we call \switch neuron". A switch neuron regulates the flow of information in NNs by se- lectively gating all but one of its incoming synaptic connections, effectively allowing only one signal to propagate forward. The allowed connection is determined by the switch neuron's level of modulatory activation which is affected by modulatory signals, such as signals that encode some informa- tion about the reward received by the agent. An important aspect of the switch neuron is that it can be used in appropriate \switch modules" in or- der to modulate other switch neurons. As we show, the introduction of the switch modules enables the creation of sequences of gating events. This is achieved through the design of a modulatory pathway capable of exploring in a principled manner all permutations of the connections arriving on the switch neurons. We test the model by presenting appropriate architectures in nonstationary binary association problems and T-maze tasks. The results show that for all tasks, the switch neuron architectures generate optimal adaptive behaviors, providing evidence that the switch neuron model could be a valuable tool in simulations where behavioral plasticity is required.
    [Show full text]
  • The Question of Algorithmic Personhood and Being
    Article The Question of Algorithmic Personhood and Being (Or: On the Tenuous Nature of Human Status and Humanity Tests in Virtual Spaces—Why All Souls Are ‘Necessarily’ Equal When Considered as Energy) Tyler Lance Jaynes Alden March Bioethics Institute, Albany Medical College, Albany, NY 12208, USA; [email protected] Abstract: What separates the unique nature of human consciousness and that of an entity that can only perceive the world via strict logic-based structures? Rather than assume that there is some potential way in which logic-only existence is non-feasible, our species would be better served by assuming that such sentient existence is feasible. Under this assumption, artificial intelligence systems (AIS), which are creations that run solely upon logic to process data, even with self-learning architectures, should therefore not face the opposition they have to gaining some legal duties and protections insofar as they are sophisticated enough to display consciousness akin to humans. Should our species enable AIS to gain a digital body to inhabit (if we have not already done so), it is more pressing than ever that solid arguments be made as to how humanity can accept AIS as being cognizant of the same degree as we ourselves claim to be. By accepting the notion that AIS can and will be able to fool our senses into believing in their claim to possessing a will or ego, we may yet Citation: Jaynes, T.L. The Question have a chance to address them as equals before some unforgivable travesty occurs betwixt ourselves of Algorithmic Personhood and Being and these super-computing beings.
    [Show full text]
  • A General Principle of Dendritic Constancy – a Neuron's Size And
    bioRxiv preprint doi: https://doi.org/10.1101/787911; this version posted October 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Dendritic constancy Cuntz et al. A general principle of dendritic constancy – a neuron’s size and shape invariant excitability *Hermann Cuntza,b, Alexander D Birda,b, Marcel Beininga,b,c,d, Marius Schneidera,b, Laura Mediavillaa,b, Felix Z Hoffmanna,b, Thomas Dellerc,1, Peter Jedlickab,c,e,1 a Ernst Strungmann¨ Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, 60528 Frankfurt am Main, Germany b Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany c Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, 60590 Frankfurt am Main, Germany d Max Planck Insitute for Brain Research, 60438 Frankfurt am Main, Germany e ICAR3R – Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, 35390 Giessen, Germany 1 Joint senior authors *[email protected] Keywords Electrotonic analysis, Compartmental model, Morphological model, Excitability, Neuronal scaling, Passive normalisation, Cable theory 1/57 bioRxiv preprint doi: https://doi.org/10.1101/787911; this version posted October 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.
    [Show full text]
  • Simulation and Analysis of Neuro-Memristive Hybrid Circuits
    Simulation and analysis of neuro-memristive hybrid circuits João Alexandre da Silva Pereira Reis Mestrado em Física Departamento de Física e Astronomia 2016 Orientador Paulo de Castro Aguiar, Investigador Auxiliar do Instituto de Investigação e Inovação em Saúde da Universidade do Porto Coorientador João Oliveira Ventura, Investigador Auxiliar do Departamento de Física e Astronomia da Universidade do Porto Todas as correções determinadas pelo júri, e só essas, foram efetuadas. O Presidente do Júri, Porto, ______/______/_________ U P M’ P Simulation and analysis of neuro-memristive hybrid circuits Advisor: Author: Dr. Paulo A João Alexandre R Co-Advisor: Dr. João V A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science A at A Department of Physics and Astronomy Faculty of Science of University of Porto II FCUP II Simulation and analysis of neuro-memristive hybrid circuits FCUP III Simulation and analysis of neuro-memristive hybrid circuits III Acknowledgments First and foremost, I need to thank my dissertation advisors Dr. Paulo Aguiar and Dr. João Ven- tura for their constant counsel, however basic my doubts were or which wall I ran into. Regardless of my stubbornness to stick to my way to research and write, they were always there for me. Of great importance, because of our shared goals, Catarina Dias and Mónica Cerquido helped me have a fixed and practical outlook to my research. During the this dissertation, I attended MemoCIS, a training school of memristors, which helped me have a more concrete perspective on state of the art research on technical details, modeling considerations and concrete proposed and realized applications.
    [Show full text]
  • Physiological Role of AMPAR Nanoscale Organization at Basal State and During Synaptic Plasticities Benjamin Compans
    Physiological role of AMPAR nanoscale organization at basal state and during synaptic plasticities Benjamin Compans To cite this version: Benjamin Compans. Physiological role of AMPAR nanoscale organization at basal state and during synaptic plasticities. Human health and pathology. Université de Bordeaux, 2017. English. NNT : 2017BORD0700. tel-01753429 HAL Id: tel-01753429 https://tel.archives-ouvertes.fr/tel-01753429 Submitted on 29 Mar 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE PRÉSENTÉE POUR OBTENIR LE GRADE DE DOCTEUR DE L’UNIVERSITÉ DE BORDEAUX ÉCOLE DOCTORALE DES SCIENCES DE LA VIE ET DE LA SANTE SPÉCIALITÉ NEUROSCIENCES Par Benjamin COMPANS Rôle physiologique de l’organisation des récepteurs AMPA à l’échelle nanométrique à l’état basal et lors des plasticités synaptiques Sous la direction de : Eric Hosy Soutenue le 19 Octobre 2017 Membres du jury Stéphane Oliet Directeur de Recherche CNRS Président Jean-Louis Bessereau PU/PH Université de Lyon Rapporteur Sabine Levi Directeur de Recherche CNRS Rapporteur Ryohei Yasuda Directeur de Recherche Max Planck Florida Institute Examinateur Yukiko Goda Directeur de Recherche Riken Brain Science Institute Examinateur Daniel Choquet Directeur de Recherche CNRS Invité 1 Interdisciplinary Institute for NeuroSciences (IINS) CNRS UMR 5297 Université de Bordeaux Centre Broca Nouvelle-Aquitaine 146 Rue Léo Saignat 33076 Bordeaux (France) 2 Résumé Le cerveau est formé d’un réseau complexe de neurones responsables de nos fonctions cognitives et de nos comportements.
    [Show full text]
  • Draft Nstac Report to the President On
    THE PRESIDENT’S NATIONAL SECURITY TELECOMMUNICATIONS ADVISORY COMMITTEE DRAFT NSTAC REPORT TO THE PRESIDENT DRAFTon Communications Resiliency TBD Table of Contents Executive Summary .......................................................................................................ES-1 Introduction ........................................................................................................................1 Scoping and Charge.............................................................................................................2 Subcommittee Process ........................................................................................................3 Summary of Report Structure ...............................................................................................3 The Future State of ICT .......................................................................................................4 ICT Vision ...........................................................................................................................4 Wireline Segment ............................................................................................................5 Satellite Segment............................................................................................................6 Wireless 5G/6G ..............................................................................................................7 Public Safety Communications ..........................................................................................8
    [Show full text]
  • What Are Computational Neuroscience and Neuroinformatics? Computational Neuroscience
    Department of Mathematical Sciences B12412: Computational Neuroscience and Neuroinformatics What are Computational Neuroscience and Neuroinformatics? Computational Neuroscience Computational Neuroscience1 is an interdisciplinary science that links the diverse fields of neu- roscience, computer science, physics and applied mathematics together. It serves as the primary theoretical method for investigating the function and mechanism of the nervous system. Com- putational neuroscience traces its historical roots to the the work of people such as Andrew Huxley, Alan Hodgkin, and David Marr. Hodgkin and Huxley's developed the voltage clamp and created the first mathematical model of the action potential. David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus and neocortex interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory. Computational neuroscience is distinct from psychological connectionism and theories of learning from disciplines such as machine learning,neural networks and statistical learning theory in that it emphasizes descriptions of functional and biologically realistic neurons and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein and chemical coupling to network os- cillations and learning and memory. These computational models are used to test hypotheses that can be directly verified by current or future biological experiments. Currently, the field is undergoing a rapid expansion. There are many software packages, such as NEURON, that allow rapid and systematic in silico modeling of realistic neurons.
    [Show full text]
  • The Electrotonic Transformation
    Carnevale et al.: The Electrotonic Transformation Published as: Carnevale, N.T., Tsai, K.Y., Claiborne, B.J., and Brown, T.H.. The electrotonic transformation: a tool for relating neuronal form to function. In: Advances in Neural Information Processing Systems, vol. 7, eds. Tesauro, G., Touretzky, D.S., and Leen, T.K.. MIT Press, Cambridge, MA, 1995, pp. 69–76. The Electrotonic Transformation: a Tool for Relating Neuronal Form to Function Nicholas T. Carnevale Kenneth Y. Tsai Department of Psychology Department of Psychology Yale University Yale University New Haven, CT 06520 New Haven, CT 06520 Brenda J. Claiborne Thomas H. Brown Division of Life Sciences Department of Psychology University of Texas Yale University San Antonio, TX 79285 New Haven, CT 06520 Abstract The spatial distribution and time course of electrical signals in neurons have important theoretical and practical consequences. Because it is difficult to infer how neuronal form affects electrical signaling, we have developed a quantitative yet intuitive approach to the analysis of electrotonus. This approach transforms the architecture of the cell from anatomical to electrotonic space, using the logarithm of voltage attenuation as the distance metric. We describe the theory behind this approach and illustrate its use. Page 1 Carnevale et al.: The Electrotonic Transformation 1 INTRODUCTION The fields of computational neuroscience and artificial neural nets have enjoyed a mutually beneficial exchange of ideas. This has been most evident at the network level, where concepts such as massive parallelism, lateral inhibition, and recurrent excitation have inspired both the analysis of brain circuits and the design of artificial neural net architectures.
    [Show full text]
  • Drawing Inspiration from Biological Dendrites to Empower Artificial
    DRAWING INSPIRATION FROM BIOLOGICAL DENDRITES TO EMPOWER ARTIFICIAL NEURAL NETWORKS OPINION ARTICLE Spyridon Chavlis Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology Hellas (FORTH) Plastira Avenue 100, Heraklion, 70013, Greece [email protected] Panayiota Poirazi Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology Hellas (FORTH) Plastira Avenue 100, Heraklion, 70013, Greece [email protected] ABSTRACT This article highlights specific features of biological neurons and their dendritic trees, whose adop- tion may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compart- mentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning. Keywords Artificial Neural Networks · Biological dendrites · Plasticity 1 Introduction Artificial Neural Networks (ANN) implemented in Deep Learning (DL) architectures have been extremely successful in solving challenging machine learning (ML) problems such as image [1], speech [2], and face [3] recognition, arXiv:2106.07490v1 [q-bio.NC] 14 Jun 2021 playing online games [4], autonomous driving [5], etc. However, despite their huge successes, state-of-the-art DL architectures suffer from problems that seem rudimentary to real brains. For example, while becoming true experts in solving a specific task, they typically fail to transfer these skills to new problems without extensive retraining – a property known as “transfer learning”.
    [Show full text]
  • Physics of the Extended Neuron
    PHYSICS OF THE EXTENDED NEURON¤ P C BRESSLOFFy and S COOMBESz Nonlinear and Complex Systems Group, Department of Mathematical Sciences, Loughborough University, Loughborough, Leicestershire, LE12 8DB, UK Received 27 March 1997 We review recent work concerning the e®ects of dendritic structure on single neuron response and the dynamics of neural populations. We highlight a number of concepts and techniques from physics useful in studying the behaviour of the spatially extended neuron. First we show how the single neuron Green's function, which incorporates de- tails concerning the geometry of the dendritic tree, can be determined using the theory of random walks. We then exploit the formal analogy between a neuron with dendritic structure and the tight{binding model of excitations on a disordered lattice to analyse various Dyson{like equations arising from the modelling of synaptic inputs and random synaptic background activity. Finally, we formulate the dynamics of interacting pop- ulations of spatially extended neurons in terms of a set of Volterra integro{di®erential equations whose kernels are the single neuron Green's functions. Linear stability analysis and bifurcation theory are then used to investigate two particular aspects of population dynamics (i) pattern formation in a strongly coupled network of analog neurons and (ii) phase{synchronization in a weakly coupled network of integrate{and{¯re neurons. 1. Introduction The identi¯cation of the main levels of organization in synaptic neural circuits may provide the framework for understanding the dynamics of the brain. Some of these levels have already been identi¯ed1. Above the level of molecules and ions, the synapse and local patterns of synaptic connection and interaction de¯ne a micro- circuit.
    [Show full text]
  • Face/Off: “Deepfake” Face Swaps and Privacy Laws
    Face/Off: “DeepFake” Face Swaps and Privacy Laws By: Erik Gerstner Erik Gerstner is an associate at David, Kamp & Frank, L.L.C. in Newport News, Virginia. Erik received his JD from William & Mary Law School in 2018. He focuses his practice on civil litigation, including business litigation, real estate, personal injury, and reputational injury. This article has been expanded from one the author published in For The Defense. N 2018, a curious trend spread making it appear as though Cage had rapidly across the Internet: always portrayed those characters.2 I people posting videos of Nicholas Nicholas Cage’s central role in Cage’s performances in various DeepFake videos is fitting, given his Hollywood films.1 To the uninitiated starring role alongside John viewer, these videos might appear Travolta in 1997’s Face/Off, a film in to be nothing special, just various which his and Travolta’s characters facets of Cage’s prolific career. both end up wearing the other’s However, closer inspection would faces throughout the film. Although reveal a subtler thread running it was only a fanciful Hollywood throughout these clips: none of invention in 1997, face swapping these performances actually technology entered the mainstream involved Cage. Rather, thanks to in 2017. In August of that year, relatively new artificial intelligence University of Washington (AI)-powered software programs researchers released a video, colloquially known as DeepFakes, seemingly of Barack Obama, Internet users had seamlessly discussing topics such as terrorism, inserted Cage’s face over the faces of fatherhood, and job creation, which the original actors in these scenes, had been created using machine 1 John Maher, This was the year of the https://www.dailydot.com/unclick/nicolas deepfake Nicolas Cage meme, THE DAILY DOT -cage-memes-deepfakes-2018.
    [Show full text]
  • Arxiv:1702.05339V2
    TIME FRACTIONAL CABLE EQUATION AND APPLICATIONS IN NEUROPHYSIOLOGY SILVIA VITALI1, GASTONE CASTELLANI2, AND FRANCESCO MAINARDI3 Abstract. We propose an extension of the cable equation by in- troducing a Caputo time fractional derivative. The fundamental solutions of the most common boundary problems are derived ana- litically via Laplace Transform, and result be written in terms of known special functions. This generalization could be useful to de- scribe anomalous diffusion phenomena with leakage as signal con- duction in spiny dendrites. The presented solutions are computed in Matlab and plotted. Chaos, Solitons and Fractals (2017): Special Issue on Fu- ture Directions in Fractional Calculus. Guest Editors: Mark M. Meerschaert, Bruce J. West, Yong Zhou. Pub- lished on line 29 April 2017; http://dx.doi.org/10.1016/j.chaos.2017.04.043 Introduction The one dimensional cable model is treated in neurophysiology to model the electrical conduction of non-isopotential excitable cells, we remind as example to the textbooks of Johnston and Wu (1994) [8], Weiss (1996) [29] and Tuckwell (1988) [27], and for some mathematical details to Magin (2006) [12]. In particular it describes the spatial and the temporal dependence of transmembrane potential Vm(x, t) along the axial x direction of a cylindrical nerve cell segment. The membrane behaviour is summarized by an electrical circuit with an axial internal resistance ri, and a transmembrane capacitance cm and a transmem- brane resistance rm in parallel, connecting the inner part to the outside [8]. External axial resistance could be eventually included. Transmem- brane potential is generated by ionic concentration gradient across the arXiv:1702.05339v2 [physics.bio-ph] 18 May 2017 membrane, and is maintained non null at rest (no current) by a com- bination of passive and active cell mechanisms.
    [Show full text]