Artificial cognitive memory—changing from density driven to functionality driven

Increasing density based on bit size reduction is currently a main driving force for the development of data storage technologies. However, it is expected that all of the current available storage technologies might approach their physical limits in around 15 to 20 years due to miniaturization. To further advance the storage technologies, it is required to explore a new development trend that is different from density driven. One possible direction is to derive insights from biological counterparts. Unlike physical memories that have a single function of data storage, human memory is versatile. It contributes to functions of data storage, information processing, and most importantly, cognitive functions such as adaptation, learning, perception, knowledge generation, etc. In this paper, a brief review of current data storage technologies are presented, followed by discussions of future storage technology development trend. We expect that the driving force will evolve from density to functionality, and new memory modules associated with additional functions other than only data storage will appear. As an initial step toward building a future generation memory technology, we propose Artificial Cognitive Memory (ACM), a memory based intelligent system. We also present the characteristics of ACM, new technologies that can be used to develop ACM components such as bioinspired element cells (silicon, memristor, phase change, etc.), and possible methodologies to construct a biologically inspired hierarchical system.

[1]  Chi-Keong Goh,et al.  Perspectives of Magnetic Recording System at 10 Tb/in$^{2}$ , 2009, IEEE Transactions on Magnetics.

[2]  Zhi Yang,et al.  Circuit and Coil Design for In-Vitro Magnetic Neural Stimulation Systems , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[3]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[4]  Bruce D. Terris,et al.  Near‐field optical data storage using a solid immersion lens , 1994 .

[5]  Takuma Yanagisawa,et al.  Multilayer 500 Gbyte Optical Disk , 2009 .

[6]  Warren Robinett,et al.  Memristor-CMOS hybrid integrated circuits for reconfigurable logic. , 2009, Nano letters.

[7]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[8]  Jens Timmer,et al.  On identification of Na+ channel gating schemes using moving-average filtered hidden Markov models , 1999, European Biophysics Journal.

[9]  Hai Helen Li,et al.  Spintronic Memristor Through Spin-Torque-Induced Magnetization Motion , 2009, IEEE Electron Device Letters.

[10]  Bernabé Linares-Barranco,et al.  Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses , 2009 .

[11]  A. Aertsen,et al.  Neuronal assemblies , 1989, IEEE Transactions on Biomedical Engineering.

[12]  H. Tuckwell Introduction to Theoretical Neurobiology: Linear Cable Theory and Dendritic Structure , 1988 .

[13]  Tomasz Bilski,et al.  Digital and biological storage systems — a quantitative comparison , 2007, 2007 2nd Bio-Inspired Models of Network, Information and Computing Systems.

[14]  Giulio Tononi,et al.  Qualia: The Geometry of Integrated Information , 2009, PLoS Comput. Biol..

[15]  K. Gopalakrishnan,et al.  Phase change memory technology , 2010, 1001.1164.

[16]  R. Douglas,et al.  A silicon neuron , 1991, Nature.

[17]  V. Mountcastle Modality and topographic properties of single neurons of cat's somatic sensory cortex. , 1957, Journal of neurophysiology.

[18]  James L. McClelland,et al.  The parallel distributed processing approach to semantic cognition , 2003, Nature Reviews Neuroscience.

[19]  R. FitzHugh Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[20]  Y. Pershin,et al.  Spin Memristive Systems: Spin Memory Effects in Semiconductor Spintronics , 2008, 0806.2151.

[21]  T. Hasegawa,et al.  Learning Abilities Achieved by a Single Solid‐State Atomic Switch , 2010, Advanced materials.

[22]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[23]  Paul E. Hasler,et al.  A bio-physically inspired silicon neuron , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[24]  Linh Hoang,et al.  An Integrated 256-Channel Epiretinal Prosthesis , 2010, IEEE Journal of Solid-State Circuits.

[25]  L Hesselink,et al.  Volume Holographic Storage and Retrieval of Digital Data , 1994, Science.

[26]  M. Fatih Erden,et al.  Heat Assisted Magnetic Recording , 2008, Proceedings of the IEEE.

[27]  Y. Huai,et al.  Observation of spin-transfer switching in deep submicron-sized and low-resistance magnetic tunnel junctions , 2004, cond-mat/0504486.

[28]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[29]  Bruce Alexander Wilson,et al.  Perpendicular recording: the promise and the problems , 2001 .

[30]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.

[31]  R. Douglas,et al.  A Quantitative Map of the Circuit of Cat Primary Visual Cortex , 2004, The Journal of Neuroscience.

[32]  D. Ielmini,et al.  Phase change materials and their application to nonvolatile memories. , 2010, Chemical reviews.

[33]  E. Kandel The Molecular Biology of Memory Storage: A Dialogue Between Genes and Synapses , 2001, Science.

[34]  Zhiyong Li,et al.  Ionic/Electronic Hybrid Materials Integrated in a Synaptic Transistor with Signal Processing and Learning Functions , 2010, Advanced materials.

[35]  Mircea R. Stan,et al.  Advances and Future Prospects of Spin-Transfer Torque Random Access Memory , 2010, IEEE Transactions on Magnetics.

[36]  D H HUBEL,et al.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.

[37]  H. Wilson Simplified dynamics of human and mammalian neocortical neurons. , 1999, Journal of theoretical biology.

[38]  Crystalline amorphous semiconductor superlattice. , 2008, Physical review letters.

[39]  M. Wuttig,et al.  Phase-change materials for rewriteable data storage. , 2007, Nature materials.

[40]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[41]  Qi Zhao,et al.  Neural signal classification using a simplified feature set with nonparametric clustering , 2009, Neurocomputing.

[42]  Chiara Bartolozzi,et al.  Synaptic Dynamics in Analog VLSI , 2007, Neural Computation.

[43]  C. Koch The quest for consciousness : a neurobiological approach , 2004 .

[44]  Heidi Kloos,et al.  PSYCHOLOGICAL SCIENCE Research Article When Looks Are Everything Appearance Similarity Versus Kind Information in Early Induction , 2022 .

[45]  R. Douglas,et al.  Recurrent neuronal circuits in the neocortex , 2007, Current Biology.

[46]  I. Karpov,et al.  Nucleation switching in phase change memory , 2007 .

[47]  Henry C. Tuckwell,et al.  Introduction to theoretical neurobiology , 1988 .

[48]  Tow Chong Chong,et al.  Phase change random access memory cell with superlattice-like structure , 2006 .

[49]  Eugene M. Izhikevich,et al.  Resonate-and-fire neurons , 2001, Neural Networks.

[50]  G. Oxford Some kinetic and steady-state properties of sodium channels after removal of inactivation , 1981, The Journal of general physiology.

[51]  E. Betzig,et al.  Near-Field Optics: Microscopy, Spectroscopy, and Surface Modification Beyond the Diffraction Limit , 1992, Science.

[52]  Dileep George,et al.  Towards a Mathematical Theory of Cortical Micro-circuits , 2009, PLoS Comput. Biol..

[53]  William J. Gallagher,et al.  Development of the magnetic tunnel junction MRAM at IBM: From first junctions to a 16-Mb MRAM demonstrator chip , 2006, IBM J. Res. Dev..

[54]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[55]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[56]  Guido Bugmann,et al.  Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset , 1997, Neural Computation.

[57]  K. Harris Neural signatures of cell assembly organization , 2005, Nature Reviews Neuroscience.

[58]  J. Tominaga,et al.  Understanding the phase-change mechanism of rewritable optical media , 2004, Nature materials.

[59]  Piotr Dudek,et al.  Compact silicon neuron circuit with spiking and bursting behaviour , 2008, Neural Networks.

[60]  L. F Abbott,et al.  Lapicque’s introduction of the integrate-and-fire model neuron (1907) , 1999, Brain Research Bulletin.

[61]  Gregory S. Snider,et al.  ‘Memristive’ switches enable ‘stateful’ logic operations via material implication , 2010, Nature.

[62]  Terrence J. Sejnowski,et al.  The Computational Brain , 1996, Artif. Intell..

[63]  G. Tononi Consciousness as Integrated Information: a Provisional Manifesto , 2008, The Biological Bulletin.

[64]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[65]  E. Harth,et al.  Cooperativity in brain function: Assemblies of approximately 30 neurons , 1982, Experimental Neurology.

[66]  Charles R. Szmanda,et al.  Programmable polymer thin film and non-volatile memory device , 2004, Nature materials.

[67]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[68]  S. Ovshinsky Optical Cognitive Information Processing – A New Field , 2004 .

[69]  C. Mead,et al.  Neuromorphic analogue VLSI. , 1995, Annual review of neuroscience.

[70]  K. Nagy Evidence for multiple open states of sodium channels in neuroblastoma cells , 2005, The Journal of Membrane Biology.

[71]  Wentai Liu,et al.  Improving spike separation using waveform derivatives , 2009, Journal of neural engineering.

[72]  Rodney J. Douglas,et al.  An Improved Silicon Neuron , 2000 .

[73]  Idan Segev,et al.  Methods in Neuronal Modeling , 1988 .

[74]  Vladimir M. Sloutsky,et al.  Similarity, Induction, Naming and Categorization: A Bottom-up Approach , 2010 .

[75]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[76]  Stoddart,et al.  Electronically configurable molecular-based logic gates , 1999, Science.

[77]  Bruce W. Knight,et al.  Dynamics of Encoding in a Population of Neurons , 1972, The Journal of general physiology.

[78]  R. Williams,et al.  The control of neuron number. , 1988, Annual review of neuroscience.

[79]  Jian-Gang Zhu,et al.  Magnetoresistive Random Access Memory: The Path to Competitiveness and Scalability , 2008, Proceedings of the IEEE.

[80]  J F Scott,et al.  Nanoferroelectrics: statics and dynamics , 2006, Journal of physics. Condensed matter : an Institute of Physics journal.

[81]  Christof Koch,et al.  Cortical Cells Should Fire Regularly, But Do Not , 1999, Neural Computation.

[82]  Zvonimir Z. Bandic,et al.  Advances in Magnetic Data Storage Technologies , 2008 .

[83]  P. Asselin,et al.  Recording on bit-patterned media at densities of 1Tb/in2 and beyond , 2006, INTERMAG 2006 - IEEE International Magnetics Conference.

[84]  Kenneth D. Miller,et al.  Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell , 1997, Neural Computation.

[85]  Chris Eliasmith,et al.  Integrating behavioral and neural data in a model of zebrafish network interaction , 2005, Biological Cybernetics.

[86]  Chris Eliasmith,et al.  A general framework for neurobiological modeling: an application to the vestibular system , 2002, Neurocomputing.

[87]  Simone Raoux,et al.  Crystallization properties of ultrathin phase change films , 2008 .

[88]  Lorenzo Rosasco,et al.  Publisher Accessed Terms of Use Detailed Terms Mathematics of the Neural Response , 2022 .

[89]  Matthias Wuttig,et al.  Resonant bonding in crystalline phase-change materials. , 2008, Nature materials.