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Prof. Newton Howard (DM, PhD, DPhil, HDR, MA, CO) is an [[United States|American]] [[cognitive scientist]] and former Director of the [http://www.mindmachineproject.org Mind Machine Project] at the [[Massachusetts Institute of Technology]] (MIT). He is currently the [http://www.nds.ox.ac.uk/team/newton-howard Professor of Computational Neuroscience and Functional Neurosurgery] at the [[University of Oxford]], where he directs the [http://www.brainscienceresearch.org/ Oxford Computational Neuroscience Laboratory]. He is also the Director of the [http://www.synthintel.org/ Synthetic Intelligence Lab at MIT], the founder of the [http://www.c4ads.org/ Center for Advanced Defense Studies] and the Chairman of the [http://brainsciences.org/ Brain Sciences Foundation].
Prof. Newton Howard is a [[brain]] and [[cognitive scientist]] and former Director of the MIT Mind Machine Project<ref>{{Cite web|url = http://www.mindmachineproject.org|title = MIT Mind Machine Project|date = |accessdate = |website = Mind Machine Project|publisher = Massachusetts Institute of Technology|last = |first = }}</ref> at the [[Massachusetts Institute of Technology]] (MIT). He is currently the Professor of Computational Neuroscience and Functional Neurosurgery<ref>{{Cite web|url = http://www.nds.ox.ac.uk/team/newton-howard|title = Nuffield Department of Surgical Sciences|date = |accessdate = |website = Nuffield Department of Surgical Sciences|publisher = University of Oxford|last = |first = }}</ref> at the [[University of Oxford]], where he directs the Oxford Computational Neuroscience Laboratory<ref>{{Cite web|url = http://www.brainscienceresearch.org/|title = Oxford Computational Neuroscience Laboratory|date = |accessdate = |website = Oxford Computational Neuroscience Laboratory|publisher = University of Oxford|last = |first = }}</ref>. He is also the Director of the Synthetic Intelligence Lab at MIT<ref>{{Cite web|url = http://www.synthintel.org/|title = Synthetic Intelligence Lab|date = |accessdate = |website = Synthetic Intelligence Lab|publisher = Massachusetts Institute of Technology|last = |first = }}</ref>, the founder of the Center for Advanced Defense Studies<ref>{{Cite web|url = http://www.c4ads.org/|title = Center for Advanced Defense Studies|date = |accessdate = |website = Center for Advanced Defense Studies|publisher = Center for Advanced Defense Studies|last = |first = }}</ref> and the Chairman of the Brain Sciences Foundation<ref name=":0">{{Cite web|url = http://www.brainsciences.org|title = Brain Sciences Foundation|date = |accessdate = |website = Brain Sciences Foundation|publisher = Brain Sciences Foundation|last = |first = }}</ref>.


His research areas include [[Cognition]], [[Memory]], [[Trauma surgery|Trauma]], [[Machine Learning]], Comprehensive Brain Modeling, [[Natural Language Processing]], [[Nanotech]], [[Medical Devices]] and [[Artificial Intelligence]].
His research areas include [[Cognition]], [[Memory]], [[Trauma surgery|Trauma]], [[Machine Learning]], Comprehensive Brain Modeling, [[Natural Language Processing]], [[Nanotech]], [[Medical Devices]] and [[Artificial Intelligence]].
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==Education==
==Education==


Dr. Howard earned his B.A. in Behavioral and Military Science from [[Concordia University]], then obtained an M.A. in Technology & Information Security from [[Eastern Michigan University]]. He went on to continue his studies at the [[University of Paris-Sorbonne]], where he received a Doctorate in Cognitive Informatics and Mathematics and was awarded the prestigious Habilitation a Diriger des Recherches for his work on the Physics of Cognition (PoC). He then studied at the [[Massachusetts Institute of Technology]] and at [[Oxford University]], earning a Doctorate in Philosophy. While a graduate member of the Faculty of Mathematical Sciences at the [[University of Oxford]], he proposed the Theory of Intention Awareness (IA), which made a significant impact on the design of command & control systems and information exchange systems at tactical, operational and strategic levels.
Dr. Howard earned his [[Bachelor of Arts|B.A.]] from [[Concordia University]], then obtained an [[Master of Arts|M.A.]] in Technology from [[Eastern Michigan University]]. He went on to continue his studies at the [[University of Oxford]] where as a graduate member of the Faculty of Mathematical Sciences he proposed the Theory of Intention Awareness (IA)<ref name="“Newton">Newton Howard, “Theory of Intention Awareness in Tactical Military Intelligence: Reducing Uncertainty by Understanding the Cognitive Architecture of Intentions", Author House First Books Library, Bloomington, Indiana. 2002.</ref>, which made a significant impact on the design of command & control systems and information exchange systems at tactical, operational and strategic levels. He further developed this theory at the [[University of Paris-Sorbonne]], where he received a [[Doctorate]] in [[Cognitive science|Cognitive Informatics]] and [[Mathematics]] and was awarded the prestigious [[Habilitation|Habilitation a Diriger des Recherches]] for his work on the Physics of Cognition (PoC).


==Career==
==Career==


Professor Howard is an international executive, author and national security advisor to several U.S. Government organizations. He has directed leading international cooperation programs on emerging trends in global security and information assurance and has been instrumental in the creation of National Centers of Excellence in medicine, psychiatry, and computation at multiple research institutions in the US. His work has contributed to more than 30 U.S. patents and [https://www.researchgate.net/profile/Newton_Howard over 90 publications], fully half of which are in the areas of cognitive and computational theory. His work has garnered him several awards and honors, including a nomination to the White House fellowship.
Professor Howard is an author and national security advisor to several U.S. Government organizations. He has directed leading international cooperation programs on emerging trends in global security and information assurance and has been instrumental in the creation of National Centers of Excellence in medicine, psychiatry, and computation at multiple research institutions in the US. His work has contributed to more than 30 U.S. patents and [https://www.researchgate.net/profile/Newton_Howard over 90 publications], fully half of which are in the areas of cognitive and computational theory.


In 2009, Prof. Howard founded the [http://www.brainsciences.org Brain Sciences Foundation] (BSF), a nonprofit 501(c)3 organization to help fund research and promote awareness for combating neurological and neurodegenerative diseases. The foundation provides grants and scholarships to support the development and promotion of several highly promising new diagnostic and therapeutic technologies. The organization includes a multitude of fellows from various leading research institutions, including [[Oxford University]], [[Massachusetts Institute of Technology]], [[La Sorbonne University of Paris|La Sorbonne University of Paris]] and [[INSERM]]. The goal of the foundation is to improve the quality of life for those suffering from neurological disorders.
In 2009, Prof. Howard founded the Brain Sciences Foundation (BSF)<ref name=":0" />, a nonprofit 501(c)3 organization to help fund research and promote awareness for combating neurological and neurodegenerative diseases. The foundation provides grants and scholarships to support the development and promotion of several highly promising new diagnostic and therapeutic technologies. The organization includes a multitude of fellows from various leading research institutions, including [[Oxford University]], [[Massachusetts Institute of Technology]], [[La Sorbonne University of Paris|La Sorbonne University of Paris]] and [[INSERM]]. The goal of the Foundation is to improve the quality of life for those suffering from neurological disorders.


==Research==
==Research==


Prof. Howard's initial work focused on [[intentionality]] within the individual and various formal and informal command structures. His Theory of Intention Awareness (IA) is grounded in the systematic design and construction of naturalistic systems and serves as a possible model for explaining volition in human intelligence, recursively at all layers of biological organization. IA informs intelligent action planning; being both the process and product of planning and becomes the intelligent architecture to which we can credit purposeful action. His work on IA led him to expand his research into [[signal processing]], [[Linguistic description|linguistic analysis]] and [[Artificial intelligence|Artificial Intelligence]] (AI), eventually bringing him to MIT.
Prof. Howard's initial work focused on [[intentionality]] within the individual and various formal and informal command structures. His Theory of Intention Awareness (IA)<ref>{{Cite book|title = Theory of Intention Awareness in Tactical Military Intelligence: Reducing Uncertainty by Understanding the Cognitive Architecture of Intentions|last = Howard|first = Newton|publisher = Author House First Books Library|year = 2002|isbn = |location = Bloomington, IN|pages = }}</ref> is grounded in the systematic design and construction of naturalistic systems and serves as a possible model for explaining volition in human intelligence, recursively throughout all layers of biological organization. Conceptually, IA informs intelligent action planning; being both the process and product of planning and becomes the intelligent architecture to which we can credit purposeful action. This work on Intention Awareness led him to expand his research into [[signal processing]], [[Linguistic description|linguistic analysis]] and [[Artificial intelligence|Artificial Intelligence]] (AI), eventually bringing him to [[Massachusetts Institute of Technology|MIT]].


While [http://web.mit.edu/nhmit/www/ at MIT], he developed the Mood State Indicator Algorithm (MSI) a system that can model and explain the mental processes involved in human speech and writing to predict emotional states. In 2011, he collaborated on the development of the Language Axiological Input/Output system (LXIO)<ref name=“Newton Howard of Massachusetts Institute of Technology and Mathieu Guidere of University of Toulouse”>Newton Howard and Mathieu Guidere, “LXIO: The Mood Detection Robopsych”, ‘ ‘Brain Sciences Journal’ ’, January 2012</ref>. The LXIO was developed as a practical application of the Mood Sate Indicator (MSI) technology and is a system capable of detecting both sentiment and mood states based on the analysis of written or verbal discourse. The LXIO represents a patient’s mood state by the sum of values generated by a given sentence or word string. Each word's context and grammatical function is computed with the use of a mind default axiology (MDA) while a learning algorithm tracks patients’ word analysis histories to enhance accuracy. LXIO parses sentences into words that are processed through modules to evaluate cognitive states. Words are processed through time orientation, contextual-prediction and consequent modules. The significance of LXIO is its ability to incorporate conscious thought and bodily expression (linguistic or otherwise) into a uniform code schema.
While [http://web.mit.edu/nhmit/www/ at MIT], he developed the Mood State Indicator (MSI)<ref name=":1">{{Cite journal|url = |title = LXIO: The Mood Detection Robopsych|last = Howard|first = Newton|date = January 2012|journal = Brain Sciences Journal|doi = |pmid = |access-date = |last2 = Guidere|first2 = Mathieu}}</ref> a system that can model and explain the mental processes involved in human speech and writing to predict emotional states (2011). He next developed the Language Axiological Input/Output system (LXIO)<ref name=":1" /> as a practical application of the Mood Sate Indicator technology. LXIO is a system capable of detecting both sentiment and mood states based on the analysis of written or verbal discourse, representing a patient’s mood state by the sum of values generated by a given sentence or word string. LXIO parses sentences into words that are processed through modules to evaluate cognitive states. Words are then processed through time orientation, contextual-prediction and consequent modules and each word's context and grammatical function is computed with the use of a Mind Default Axiology (MDA). Within the LXIO, a learning algorithm tracks patients’ word analysis histories to further enhance accuracy. The significance of LXIO is its ability to incorporate conscious thought and bodily expression (linguistic or otherwise) into a uniform code schema<ref name=":1" />.


While at the [[University of Oxford]], Professor Howard turned his focus to [[neurology]], [[neurosurgery]] and [[Nanoscopic scale|nanoscale]] [[Medical device|medical devices]] and developed the Fundamental Code Unit (FCU) theory, which serves as a blueprint for conscious thought and may be used to map higher order function to neurophysiological process. His work on the FCU provides the foundation for his subsequent theory, the Brain Code (BC). The Fundamental Code Unit cannot be simplified to a neuron, yet the difference between our intellectual abilities lies in the combination of quantitative differences of these neurons and neural networks.
While at the [[University of Oxford]], Professor Howard turned his focus to [[neurology]], [[neurosurgery]] and [[Nanoscopic scale|nanoscale]] [[Medical device|medical devices]] and in 2012 published and patented<ref>{{Cite web|url = http://www.google.com/patents/US20130338526|title = Patent US20130338526-A1|date = |accessdate = |website = Google Patent DB|publisher = US Patent & Trademark Office|last = Howard|first = Newton}}</ref> the Fundamental Code Unit (FCU)<ref name=":2">{{Cite web|url = http://www.brainsciencesjournal.org/brain-language.pdf|title = Brain Language: The Fundamental Code Unit|date = 2012|accessdate = |website = Brain Sciences Journal|publisher = Brain Sciences Foundation|last = Howard|first = Newton}}</ref> theory, which uses on/off (UNARY +/-) mathematics to correlate networks of [[Neurophysiology|neurophysiological]] processes and map them to higher order function. The FCU provides the underlying foundation for the Brain Code (BC) theory, also proposed by Prof. Howard, and is used to map entire circuits of neurological activity to behavior and response, effectively decoding the language of the brain.

In 2014, Professor Howard discovered an optical communications network within the brain, mediated by [[OPN5|neuropsin (OPN5)]]. Within this network, [[Photonics|photonic]] activity is transduced into [[Chemical synapse|synaptic membrane]] potential changes within neocortical networks via a [[CGMP-specific phosphodiesterase type 5|cGMP]]-dependent mechanism, accompanied by a photostimulation-catalyzed G protein/[[CGMP-gated cation channel|cGMP phosphodiesterase activation]], which regulates membrane potential by closing cGMP-gated ion channels. This self-regulating cycle of photon-mediated events in the neocortex involves sequential interactions among 3 [[Mitochondrion|mitochondrial]] sources of endogenously-generated photons during periods of increased neural spiking activity: (a) near UV photons (~380 nm), a free radical reaction byproduct; (b) blue photons (~470 nm) emitted by [[Nicotinamide adenine dinucleotide|NAD(P)H]] upon absorption of near-UV photons; and (c) green photons (~530 nm) generated by [[NAD(P)H oxidase|NAD(P)H oxidases]], upon NAD(P)H-generated blue photon absorption.  Neuropsin ([[OPN5]]) has two switchable conformations (~380nm-absorbing and ~470nm-absorbing) and this bistable nature allows it to serve as an effective regulatory signaling mechanism. The bistable nature of this nanoscale quantum process provides evidence for an on/off ([[Unary coding|UNARY]] +/-) coding system existing at the most fundamental level of the brain and provides a solid neurophysiological basis for the FCU<ref name=":2" /> to build from.

Several of these theories have been recently brought to clinical trial and use, beginning in 2015 with the Brain Code Collection System (BCCS). This neurodiagnostic device uses synchronous [[Multisensory integration|multimodal]] data capture techniques in combination with the FCU to establish a highly comprehensive patient profile that can be securely analyzed against millions of other profiles to find disease biomarkers. The system has demonstrated the ability to diagnose a number of formerly undiagnosable neurodegenerative conditions<ref>{{Cite journal|url = |title = Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes|last = Howard|first = Newton|date = 2013|journal = Frontiers Special Issue INCF: Imaging the Brain at Different Scales|doi = |pmid = |access-date = |last2 = Bergmann|first2 = J.|last3 = Stein|first3 = J.}}</ref><ref>{{Cite journal|url = |title = Computational Methods for Clinical Applications: An Introduction|last = Howard|first = Newton|date = 2011|journal = Functional Neurology, Rehabilitation and Ergonomics|doi = |pmid = |access-date = |last2 = Stein|first2 = J.}}</ref>, including Alzheimer's Disease (AD) and Parkinson's Disease (PD).<ref>{{Cite book|title = Approach to Study the Brain: Towards the Early Detection of Neurodegenerative Disease|last = Howard|first = Newton|publisher = Cambridge Scientific Publishing|year = 2015|isbn = 978-1-908106-49-0|location = London, UK|pages = }}</ref><ref>{{Cite book|title = The Brain Language|last = Howard|first = Newton|publisher = Cambridge Scientific Publishing|year = 2015|isbn = 978-1-908106-50-6|location = London, UK|pages = }}</ref><ref>{{Cite journal|url = |title = Mathematical Review for Cortical Computing Proposition for Brain Code Hypothesis|last = Howard|first = Newton|date = 2015|journal = Frontiers Systems Neuroscience|doi = |pmid = |access-date = |last2 = Stein|first2 = J. F.}}</ref>


==Selected Works==
==Selected Works==
*Howard, N., Argamon, S. (Eds.) (2009). [http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-642-01140-5 Computational Methods For Counterterrorism]. Berlin: Springer-Verlag.
*Howard, N., Argamon, S. (Eds.) (2009). [http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-642-01140-5 Computational Methods For Counterterrorism]. Berlin: Springer-Verlag.
*Howard, N. (2002) Theory of Intention Awareness in Tactical Military Intelligence: Reducing Un- certainty by Understanding the Cognitive Architecture of Intentions. Bloomington, Indiana: Author House First Books Library.
*Howard, N. (2001) Seeking Peace in our Time: Global Defense System of Law. Bloomington, Indiana: Author House First Books Library.
*Howard, N. (2001) The Challenges in Weapon Systems Design. Bloomington, Indiana: Author House First Books Library.
*Howard, N. (1999) The Logic of Uncertainty and Situational Understanding. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
*Howard, N. (1999) The Logic of Uncertainty and Situational Understanding. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
*Howard, N. (1999) Multidimensional Time Understanding Combinatorial Manifold. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
*Howard, N. (1999) Multidimensional Time Understanding Combinatorial Manifold. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
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*Howard, N. (2015). The Case for Intention Awareness in Security Systems., Journal of Cyber-Security and Digital Forensics, 1(1), in press.
*Howard, N. (2015). The Case for Intention Awareness in Security Systems., Journal of Cyber-Security and Digital Forensics, 1(1), in press.
*Cambria, E., White, B., Durrani, T., Howard, N. (2014) Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1)
*Cambria, E., White, B., Durrani, T., Howard, N. (2014) Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1)
*Poria, S., Agarwal, Basant., Gelbukh, A., Hussain, A., Howard, N. (2014) Dependency-Based Semantic Parsing for Concept-Level Text Analysis. Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, 8403, 113-127
*Poria, S., Agarwal, Basant., Gelbukh, A., Hussain, A., Howard, N. (2014) [http://www.cs.stir.ac.uk/~spo/publication/resources/cicling.pdf Dependency-Based Semantic Parsing for Concept-Level Text Analysis]. Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, 8403, 113-127
*Hussain, A., Cambria, E., Schuller, B., Howard, N. (2014). Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis, Neural Networks, Special Issue, 58, 1-3.
*Hussain, A., Cambria, E., Schuller, B., Howard, N. (2014). [http://sentic.net/affective-and-cognitive-learning.pdf Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis], Neural Networks, Special Issue, 58, 1-3.
*Cambria, E., Howard, N., Song, Y. & Wang, H. (2014). Semantic Multidimeensional Scaling for Open Domain Sentiment Analysis. IEEE Intelligent Systems, 29 March/April.
*Cambria, E., Howard, N., Song, Y. & Wang, H. (2014). [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6383145 Semantic Multidimeensional Scaling for Open Domain Sentiment Analysis]. IEEE Intelligent Systems, 29 March/April.
*Bermann, J., Langdon, P., Mayagoita, R. & Howard, N. (2014). Exploring the use of sensors to measure behavioral interactions: An experimental evaluation of using hand trajectories. PLoS ONE, 9, e88080.
*Bermann, J., Langdon, P., Mayagoita, R. & Howard, N. (2014). [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3917885/ Exploring the use of sensors to measure behavioral interactions: An experimental evaluation of using hand trajectories]. PLoS ONE, 9, e88080.
*Nave, O., Neuman, Y., Perlovsky, L. & Howard, N. (2014) How Much Information Should We Drop to Become Intelligent? Applied Mathematics and Computation, 245: 261-264.
*Nave, O., Neuman, Y., Perlovsky, L. & Howard, N. (2014) [http://www.researchgate.net/publication/264899911_How_much_information_should_we_drop_to_become_intelligent How Much Information Should We Drop to Become Intelligent?] Applied Mathematics and Computation, 245: 261-264.
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*Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S. & Howard, N. (2013). [http://link.springer.com/chapter/10.1007%2F978-3-642-38989-4_26#page-1 Music Genre Classification: A Semi-supervised Approach]. Pattern Recognition. Springer Berlin Heidelberg,
*Howard, N., Leisman, G. (2013). DIME (Diplomatic Information Military and Economic) Power) Effects Modeling System: Applications for the Modeling of the Brain. Journal of Functional Neurology, Rehabilitation and Ergonomics. 3:2-3.
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*Poria, S., Gelbukh, A., Agarwal, B., Cambria, E. & Howard, N. (2013). [http://link.springer.com/chapter/10.1007%2F978-3-642-45111-9_42#page-1 Common Sense Knowledge Based Personality Recognition from Text.] Advances in Soft Computing and Its Applications,. Lecture Notes in Computer Science, Springer, 8266:2013, 484-496.
*Howard, N. (2013). Identifying and modeling language, brain and perception: A narrative approach. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2.
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*Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S. & Howard, N. (2013). Music Genre Classification: A Semi-supervised Approach. Pattern Recognition. Springer Berlin Heidelberg,
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*Poria, S., Gelbukh, A., Agarwal, B., Cambria, E. & Howard, N. (2013). Common Sense Knowledge Based Personality Recognition from Text. Advances in Soft Computing and Its Applications,. Lecture Notes in Computer Science, Springer, 8266:2013, 484-496.
*Howard, N., Cambria, E. (2013). Development of a Diplomatic, Information, Military, Health, and Economic Effects Modeling System. International Journal of Privacy and Health Information 1:1, pp. 1-1.
*Howard, N., Cambria, E. (2013). Development of a Diplomatic, Information, Military, Health, and Economic Effects Modeling System. International Journal of Privacy and Health Information 1:1, pp. 1-1.
*Cambria, E., Mazzocco, T., Hussain, A., Howard, N. (2013). Sentic Neurons: A Biologically inspired cognitive architecture for affective common sense reasoning. In Procedia Computer Science, 00, 1-6.
*Cambria, E., Mazzocco, T., Hussain, A., Howard, N. (2013). Sentic Neurons: A Biologically inspired cognitive architecture for affective common sense reasoning. In Procedia Computer Science, 00, 1-6.
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*Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. & Frieder, O. (2013). [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0062343 Metaphor identification in large texts corpora]. PLoS One, 8.
*CANT FIND PPUBLISHED
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*Howard, N., Bergmann, J. & Stein, J. (2013). [http://www.frontiersin.org/10.3389/conf.fninf.2013.10.00043/event_abstract Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes.] Frontiers Special Issue INCF Course Imaging the Brain at Different Scales.
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*Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. & Frieder, O. (2013). Metaphor identification in large texts corpora. PLoS One, 8.
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*Bergmann, J., Graham, S., Howard, N. & Mcgregor, A. (2013). [http://www.sciencedirect.com/science/article/pii/S0263224113000717 Comparison of Median Frequency Between Traditional and Functional Sensor Placements During Activity Monitoring]. Measurement, 46, 2193-2200.
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*Howard, N., Bergmann, J. & Stein, J. (2013). Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes. Frontiers Special Issue INCF Course Imaging the Brain at Different Scales.
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*Bergmann, J., Graham, S., Howard, N. & Mcgregor, A. (2013). Comparison of Median Frequency Between Traditional and Functional Sensor Placements During Activity Monitoring. Measurement, 46, 2193-2200.
*Howard, N., Stein, J. & Aziz, T. (2013). Early Detection of Parkinson's Disease from Speech and Movement Recordings. Oxford Parkinson's Disease Center Research Day 2013.
*Howard, N., Stein, J. & Aziz, T. (2013). Early Detection of Parkinson's Disease from Speech and Movement Recordings. Oxford Parkinson's Disease Center Research Day 2013.
*Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer’s Disease. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2(1), 29-38
*Howard, N. & Bergmann, J. (2012). [https://www.novapublishers.com/catalog/product_info.php?products_id=39326 Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer’s Disease]. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2(1), 29-38
*Bergmann, J. & Howard, N. (2012). Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease. BMC Neuroscience, 13(supp), 178.
*Bergmann, J. & Howard, N. (2012). [https://www.novapublishers.com/catalog/product_info.php?products_id=39326 Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease]. BMC Neuroscience, 13(supp), 178.
*Cambria, E., White, B., Durrani, T., & Howard, N. (2012). Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1), 19-63.
*Cambria, E., White, B., Durrani, T., & Howard, N. (2012). [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6710245 Computational Intelligence for Natural Language Processing]. IEEE Computational Intelligence Magazine, 9(1), 19-63.
*Howard, N. (2012). Brain Language: The Fundamental Code Unit. The Brain Sciences Journal, 1(1), 4-45.
*Howard, N. (2012). [http://www.brainsciencesjournal.org/brain-language.pdf Brain Language: The Fundamental Code Unit]. The Brain Sciences Journal, 1(1), 4-45.
*Howard, N. (2012). Energy Paradox of the Brain. The Brain Sciences Journal, 1(1), 46-61.
*Howard, N. (2012). [http://www.brainsciencesjournal.org/energy-paradox?view=document Energy Paradox of the Brain]. The Brain Sciences Journal, 1(1), 46-61.
*Howard, N., Lieberman, H. (2012). Brain Space: Automated Brain Understanding and Machine Constructed Analytics in Neuroscience. Brain Sciences Journal, 1(1), 85-97.
*Howard, N., Lieberman, H. (2012). [http://link.springer.com/article/10.1007/s12559-012-9171-2#page-1 Brain Space: Automated Brain Understanding and Machine Constructed Analytics in Neuroscience]. Brain Sciences Journal, 1(1), 85-97.
*Howard, N., Guidere, M. (2012). LXIO The Mood Detection Robopsych. The Brain Sciences Journal, 1(1), 98-109.
*Howard, N., Guidere, M. (2012). [http://www.brainsciencesjournal.org/lxio-the-mood-detection-robopsych?view=document LXIO The Mood Detection Robopsych]. The Brain Sciences Journal, 1(1), 98-109.
*Howard, N., Kanareykin, S. (2012) Transcranial Ultrasound Application Methods: Low-frequency ultrasound as a treatment for brain dysfunction. The Brain Sciences Journal, 1(1), 110-124.
*Howard, N., Kanareykin, S. (2012) [http://www.brainsciencesjournal.org/transcranial-ultrasound-application-methods-low-frequency-ultrasound-as-a-treatment-for-brain-dysfunction?view=document Transcranial Ultrasound Application Methods: Low-frequency ultrasound as a treatment for brain dysfunction]. The Brain Sciences Journal, 1(1), 110-124.
*Dunn, J., Heredia, J. B. D., Burke, M., Gandy, L., Kanareykin, S., Kapah, O., Taylor, M., Hines, D., Freieder, O., Grossman, D., Howard, N., Koppel, M., Morris, S., Ortony, A. & Argamon, S. *Howard, N., Jehel, L. & Arnal, R. (2014). Towards a Differntial Diagnostic of PTSD Using Cognitive Computing Methods. International Conference on Cognitive Informatics and Cognitive Computing ICCI CC. London, UK: IEEE.
*Dunn, J., Heredia, J. B. D., Burke, M., Gandy, L., Kanareykin, S., Kapah, O., Taylor, M., Hines, D., Freieder, O., Grossman, D., Howard, N., Koppel, M., Morris, S., Ortony, A. & Argamon, S. *Howard, N., Jehel, L. & Arnal, R. (2014). [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6921435 Towards a Differential Diagnostic of PTSD Using Cognitive Computing Methods]. International Conference on Cognitive Informatics and Cognitive Computing ICCI CC. London, UK: IEEE.
*Howard, N., Bergmann, J. & Fahlstrom, R. (2013). Exploring the Relationship Between Everyday Speech and Motor Symptoms of Parkinson's Disease as Prerequisite Analysis for Tool Development. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
*Howard, N., Bergmann, J. & Fahlstrom, R. (2013). [http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6714677&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6709835%2F6714631%2F06714677.pdf%3Farnumber%3D6714677 Exploring the Relationship Between Everyday Speech and Motor Symptoms of Parkinson's Disease as Prerequisite Analysis for Tool Development]. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
*Howard, N. (2013). Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
*Howard, N. (2013). [http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6714674&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6709835%2F6714631%2F06714674.pdf%3Farnumber%3D6714674 Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions]. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
*Howard, N. & Cambria, E. (2013). Intention awareness: improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences, 3;9, 1-17.
*Howard, N. & Cambria, E. (2013). [http://www.hcis-journal.com/content/3/1/9/abstract Intention awareness: improving upon situation awareness in human-centric environments]. Human-centric Computing and Information Sciences, 3;9, 1-17.
*Howard, N. (2013). The Twin Hypotheses: Brain Code and the Fundamental Code Unit: Towards Understanding the Computational Primitive Elements of Cortical Computing. Lecture Notes in Artificial Intelligence, MICAI, November 24-30, 2013, Mexico City, Mexico.
*Howard, N. (2013). [http://link.springer.com/chapter/10.1007/978-3-642-45114-0_35#page-1 The Twin Hypotheses: Brain Code and the Fundamental Code Unit: Towards Understanding the Computational Primitive Elements of Cortical Computing]. Lecture Notes in Artificial Intelligence, MICAI, November 24-30, 2013, Mexico City, Mexico.
*Howard, N., Pollock, R., Prinold, J., Sinha, J., Newham, D., Bergmann, J. (2013). Effect of Impairment on upper limb performance in an Ageing Population. The Human Computer Interaction International 2013 Conference (HCI) July 21-26, 2013, Las Vegas, Nevada.
*Howard, N., Pollock, R., Prinold, J., Sinha, J., Newham, D., Bergmann, J. (2013). [http://link.springer.com/chapter/10.1007%2F978-3-642-39191-0_9 Effect of Impairment on upper limb performance in an Ageing Population]. The Human Computer Interaction International 2013 Conference (HCI) July 21-26, 2013, Las Vegas, Nevada.
*Howard, N. (2013). Toward Understanding Analogical Mapping and Ideological Cataloguing in the Brain. Research Challenges in Information Science Series Conference (RCIS) May 29-31, 2013, Paris, France.
*Howard, N. (2013). [http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6577738&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6571722%2F6577671%2F06577738.pdf%3Farnumber%3D6577738 Toward Understanding Analogical Mapping and Ideological Cataloguing in the Brain]. Research Challenges in Information Science Series Conference (RCIS) May 29-31, 2013, Paris, France.
*Last, M., Assaf, D., Neuman, Y., Cohen, Y., Argamon, S., Howard, N., Frieder, O., Koppel, M. (2013) Towards Metaphor Analysis for Natural Language Understanding. The 35th Annual Conference on Information Retrieval, March 24-27, 2013, Moscow, Russia.
*Last, M., Assaf, D., Neuman, Y., Cohen, Y., Argamon, S., Howard, N., Frieder, O., Koppel, M. (2013) Towards Metaphor Analysis for Natural Language Understanding. The 35th Annual Conference on Information Retrieval, March 24-27, 2013, Moscow, Russia.
*Assaf, D., Howard, N., Neuman, Y., Last, M., Cohen, Y., Frieder, O., Argamon, S., Koppel (2013). Identifying -Noun Metaphors. 2013 IEEE Symposium Series on Computational Intelligence April 15-19, 2013, Singapore.
*Assaf, D., Howard, N., Neuman, Y., Last, M., Cohen, Y., Frieder, O., Argamon, S., Koppel (2013). Identifying -Noun Metaphors. 2013 IEEE Symposium Series on Computational Intelligence April 15-19, 2013, Singapore.
*Bergmann, J., Howard, N. (2013). Design Considerations for a Wearable Sensor Network that Measures Accelerations during Water-Ski Jumping. IEEE Body Sensor Network Conference, May 6-9, 2013, Cambridge, Massachusetts.
*Bergmann, J., Howard, N. (2013). [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6575480 Design Considerations for a Wearable Sensor Network that Measures Accelerations during Water-Ski Jumping]. IEEE Body Sensor Network Conference, May 6-9, 2013, Cambridge, Massachusetts.
*Howard, N., Rao, D., Fahlstrom, R. & Stein, J. (2013). The Fundamental Code Unit: A Framework for Biomarker Analysis. . The 2nd Neurological Biomarkers Conference at the 2013 Biomarker Summit. San Francisco, California. .
*Howard, N., Rao, D., Fahlstrom, R. & Stein, J. (2013). The Fundamental Code Unit: A Framework for Biomarker Analysis. . The 2nd Neurological Biomarkers Conference at the 2013 Biomarker Summit. San Francisco, California. .
*Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer's Disease. Organization for Computational Neurosciences, July 21-26, 2012, Atlanta/Decatur.
*Howard, N. & Bergmann, J. (2012). [http://www.biomedcentral.com/1471-2202/13/S1/P178 Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer's Disease]. Organization for Computational Neurosciences, July 21-26, 2012, Atlanta/Decatur.
*Howard, N. (2012). Intention Awareness in Human-Machine Interaction Sensemaking in Joint Cognitive Systems. International Conference on Information Sciences and Interaction Science, June 26-28, 2012, Jeju Island, Korea, 293-299.
*Howard, N. (2012). [http://ieeexplore.ieee.org/Xplore/defdeny.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%2Fstamp%2Fstamp.jsp%3Ftp%3D%26arnumber%3D6413031%26userType%3D&denyReason=-134&arnumber=6413031&productsMatched=null&userType= Intention Awareness in Human-Machine Interaction Sensemaking in Joint Cognitive Systems]. International Conference on Information Sciences and Interaction Science, June 26-28, 2012, Jeju Island, Korea, 293-299.
*Howard, N. Global Defense Policy System of Laws: Graph Theory Approach to Balance of Power Theory. European Intelligence and Security Informatics Conference (EISIC), 2011a. IEEE, 248-258.
*Howard, N. Global Defense Policy System of Laws: Graph Theory Approach to Balance of Power Theory. European Intelligence and Security Informatics Conference (EISIC), 2011a. IEEE, 248-258.


Revision as of 16:26, 28 December 2015

Prof. Newton Howard is a brain and cognitive scientist and former Director of the MIT Mind Machine Project[1] at the Massachusetts Institute of Technology (MIT). He is currently the Professor of Computational Neuroscience and Functional Neurosurgery[2] at the University of Oxford, where he directs the Oxford Computational Neuroscience Laboratory[3]. He is also the Director of the Synthetic Intelligence Lab at MIT[4], the founder of the Center for Advanced Defense Studies[5] and the Chairman of the Brain Sciences Foundation[6].

His research areas include Cognition, Memory, Trauma, Machine Learning, Comprehensive Brain Modeling, Natural Language Processing, Nanotech, Medical Devices and Artificial Intelligence.

Education

Dr. Howard earned his B.A. from Concordia University, then obtained an M.A. in Technology from Eastern Michigan University. He went on to continue his studies at the University of Oxford where as a graduate member of the Faculty of Mathematical Sciences he proposed the Theory of Intention Awareness (IA)[7], which made a significant impact on the design of command & control systems and information exchange systems at tactical, operational and strategic levels. He further developed this theory at the University of Paris-Sorbonne, where he received a Doctorate in Cognitive Informatics and Mathematics and was awarded the prestigious Habilitation a Diriger des Recherches for his work on the Physics of Cognition (PoC).

Career

Professor Howard is an author and national security advisor to several U.S. Government organizations. He has directed leading international cooperation programs on emerging trends in global security and information assurance and has been instrumental in the creation of National Centers of Excellence in medicine, psychiatry, and computation at multiple research institutions in the US. His work has contributed to more than 30 U.S. patents and over 90 publications, fully half of which are in the areas of cognitive and computational theory.

In 2009, Prof. Howard founded the Brain Sciences Foundation (BSF)[6], a nonprofit 501(c)3 organization to help fund research and promote awareness for combating neurological and neurodegenerative diseases. The foundation provides grants and scholarships to support the development and promotion of several highly promising new diagnostic and therapeutic technologies. The organization includes a multitude of fellows from various leading research institutions, including Oxford University, Massachusetts Institute of Technology, La Sorbonne University of Paris and INSERM. The goal of the Foundation is to improve the quality of life for those suffering from neurological disorders.

Research

Prof. Howard's initial work focused on intentionality within the individual and various formal and informal command structures. His Theory of Intention Awareness (IA)[8] is grounded in the systematic design and construction of naturalistic systems and serves as a possible model for explaining volition in human intelligence, recursively throughout all layers of biological organization. Conceptually, IA informs intelligent action planning; being both the process and product of planning and becomes the intelligent architecture to which we can credit purposeful action. This work on Intention Awareness led him to expand his research into signal processing, linguistic analysis and Artificial Intelligence (AI), eventually bringing him to MIT.

While at MIT, he developed the Mood State Indicator (MSI)[9] a system that can model and explain the mental processes involved in human speech and writing to predict emotional states (2011). He next developed the Language Axiological Input/Output system (LXIO)[9] as a practical application of the Mood Sate Indicator technology. LXIO is a system capable of detecting both sentiment and mood states based on the analysis of written or verbal discourse, representing a patient’s mood state by the sum of values generated by a given sentence or word string. LXIO parses sentences into words that are processed through modules to evaluate cognitive states. Words are then processed through time orientation, contextual-prediction and consequent modules and each word's context and grammatical function is computed with the use of a Mind Default Axiology (MDA). Within the LXIO, a learning algorithm tracks patients’ word analysis histories to further enhance accuracy. The significance of LXIO is its ability to incorporate conscious thought and bodily expression (linguistic or otherwise) into a uniform code schema[9].

While at the University of Oxford, Professor Howard turned his focus to neurology, neurosurgery and nanoscale medical devices and in 2012 published and patented[10] the Fundamental Code Unit (FCU)[11] theory, which uses on/off (UNARY +/-) mathematics to correlate networks of neurophysiological processes and map them to higher order function. The FCU provides the underlying foundation for the Brain Code (BC) theory, also proposed by Prof. Howard, and is used to map entire circuits of neurological activity to behavior and response, effectively decoding the language of the brain.

In 2014, Professor Howard discovered an optical communications network within the brain, mediated by neuropsin (OPN5). Within this network, photonic activity is transduced into synaptic membrane potential changes within neocortical networks via a cGMP-dependent mechanism, accompanied by a photostimulation-catalyzed G protein/cGMP phosphodiesterase activation, which regulates membrane potential by closing cGMP-gated ion channels. This self-regulating cycle of photon-mediated events in the neocortex involves sequential interactions among 3 mitochondrial sources of endogenously-generated photons during periods of increased neural spiking activity: (a) near UV photons (~380 nm), a free radical reaction byproduct; (b) blue photons (~470 nm) emitted by NAD(P)H upon absorption of near-UV photons; and (c) green photons (~530 nm) generated by NAD(P)H oxidases, upon NAD(P)H-generated blue photon absorption.  Neuropsin (OPN5) has two switchable conformations (~380nm-absorbing and ~470nm-absorbing) and this bistable nature allows it to serve as an effective regulatory signaling mechanism. The bistable nature of this nanoscale quantum process provides evidence for an on/off (UNARY +/-) coding system existing at the most fundamental level of the brain and provides a solid neurophysiological basis for the FCU[11] to build from.

Several of these theories have been recently brought to clinical trial and use, beginning in 2015 with the Brain Code Collection System (BCCS). This neurodiagnostic device uses synchronous multimodal data capture techniques in combination with the FCU to establish a highly comprehensive patient profile that can be securely analyzed against millions of other profiles to find disease biomarkers. The system has demonstrated the ability to diagnose a number of formerly undiagnosable neurodegenerative conditions[12][13], including Alzheimer's Disease (AD) and Parkinson's Disease (PD).[14][15][16]

Selected Works

Patents (US)

Patents (International)

References

  1. ^ "MIT Mind Machine Project". Mind Machine Project. Massachusetts Institute of Technology.
  2. ^ "Nuffield Department of Surgical Sciences". Nuffield Department of Surgical Sciences. University of Oxford.
  3. ^ "Oxford Computational Neuroscience Laboratory". Oxford Computational Neuroscience Laboratory. University of Oxford.
  4. ^ "Synthetic Intelligence Lab". Synthetic Intelligence Lab. Massachusetts Institute of Technology.
  5. ^ "Center for Advanced Defense Studies". Center for Advanced Defense Studies. Center for Advanced Defense Studies.
  6. ^ a b "Brain Sciences Foundation". Brain Sciences Foundation. Brain Sciences Foundation.
  7. ^ Newton Howard, “Theory of Intention Awareness in Tactical Military Intelligence: Reducing Uncertainty by Understanding the Cognitive Architecture of Intentions", Author House First Books Library, Bloomington, Indiana. 2002.
  8. ^ Howard, Newton (2002). Theory of Intention Awareness in Tactical Military Intelligence: Reducing Uncertainty by Understanding the Cognitive Architecture of Intentions. Bloomington, IN: Author House First Books Library.
  9. ^ a b c Howard, Newton; Guidere, Mathieu (January 2012). "LXIO: The Mood Detection Robopsych". Brain Sciences Journal.
  10. ^ Howard, Newton. "Patent US20130338526-A1". Google Patent DB. US Patent & Trademark Office.
  11. ^ a b Howard, Newton (2012). "Brain Language: The Fundamental Code Unit" (PDF). Brain Sciences Journal. Brain Sciences Foundation.
  12. ^ Howard, Newton; Bergmann, J.; Stein, J. (2013). "Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes". Frontiers Special Issue INCF: Imaging the Brain at Different Scales.
  13. ^ Howard, Newton; Stein, J. (2011). "Computational Methods for Clinical Applications: An Introduction". Functional Neurology, Rehabilitation and Ergonomics.
  14. ^ Howard, Newton (2015). Approach to Study the Brain: Towards the Early Detection of Neurodegenerative Disease. London, UK: Cambridge Scientific Publishing. ISBN 978-1-908106-49-0.
  15. ^ Howard, Newton (2015). The Brain Language. London, UK: Cambridge Scientific Publishing. ISBN 978-1-908106-50-6.
  16. ^ Howard, Newton; Stein, J. F. (2015). "Mathematical Review for Cortical Computing Proposition for Brain Code Hypothesis". Frontiers Systems Neuroscience.
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