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While at the [[University of Oxford]], Professor Howard turned his focus to [[neurology]], [[neurosurgery]], [[Nanoscopic scale|nanoscale]] [[Medical device|medical devices]] and biological coprocessors.<ref>{{Cite web|url = http://biocoprocessors.com/|title = Biological Coprocessors|date = |accessdate = |website = Biological Coprocessors|publisher = Biological Coprocessors, Inc.|last = |first = }}</ref> In 2012, he 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 [[Unary coding|unitary mathematics]] (ON/OFF +/-) 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<ref name=":3" />.
While at the [[University of Oxford]], Professor Howard turned his focus to [[neurology]], [[neurosurgery]], [[Nanoscopic scale|nanoscale]] [[Medical device|medical devices]] and biological coprocessors.<ref>{{Cite web|url = http://biocoprocessors.com/|title = Biological Coprocessors|date = |accessdate = |website = Biological Coprocessors|publisher = Biological Coprocessors, Inc.|last = |first = }}</ref> In 2012, he 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 [[Unary coding|unitary mathematics]] (ON/OFF +/-) 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<ref name=":3" />.


Professor Howard has worked on various nanoscale [[Deep brain stimulation|Deep Brain Stimulation]] and [[Optogenetics|optogenetic]] technologies in search of this most Fundamental Code Unit of Thought. In 2014, he discovered a functional endogenous optical 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.
Professor Howard has worked on various nanoscale [[Deep brain stimulation|Deep Brain Stimulation]] and [[Optogenetics|optogenetic]] technologies and in 2014 discovered a functional endogenous optical network within the brain, mediated by [[OPN5|neuropsin (OPN5)]]. Within this sub-neural 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), allowing 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 brain operation 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 name=":3">{{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>
Several of these theories have been recently brought to clinical trial and use, beginning with a wearable neurodiagnostic system that 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 [[Biomarker|disease biomarkers]]. The system has demonstrated the ability to diagnose a number of formerly undiagnosable [[Neurodegeneration|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|Alzheimer's Disease]] (AD) and [[Parkinson's disease|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 name=":3">{{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==

Latest revision as of 17:35, 28 December 2015

Prof. Newton Howard is a brain and cognitive scientist and former Director of the MIT Mind Machine Project[1][2] at the Massachusetts Institute of Technology (MIT). He is currently the Professor of Computational Neuroscience and Functional Neurosurgery[3] at the University of Oxford, where he directs the Oxford Computational Neuroscience Laboratory[4]. He is also the Director of the Synthetic Intelligence Lab at MIT[5], the founder of the Center for Advanced Defense Studies[6] and the Chairman of the Brain Sciences Foundation[7].

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

Education

[edit]

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)[8], 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

[edit]

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)[7], 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

[edit]

Prof. Howard's initial work focused on intentionality within the individual and various formal and informal command structures. His Theory of Intention Awareness (IA)[9] 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)[10] 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)[10] 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[10].

While at the University of Oxford, Professor Howard turned his focus to neurology, neurosurgery, nanoscale medical devices and biological coprocessors.[11] In 2012, he published and patented[12] the Fundamental Code Unit (FCU)[13] theory, which uses unitary mathematics (ON/OFF +/-) 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[14].

Professor Howard has worked on various nanoscale Deep Brain Stimulation and optogenetic technologies and in 2014 discovered a functional endogenous optical network within the brain, mediated by neuropsin (OPN5). Within this sub-neural 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), allowing 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 brain operation and provides a solid neurophysiological basis for the FCU[13] to build from.

Several of these theories have been recently brought to clinical trial and use, beginning with a wearable neurodiagnostic system that 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[15][16], including Alzheimer's Disease (AD) and Parkinson's Disease (PD).[17][14][18]

Selected Works

[edit]

Patents (US)

[edit]

Patents (International)

[edit]
[edit]

References

[edit]
  1. ^ "MIT Mind Machine Project". Mind Machine Project. Massachusetts Institute of Technology.
  2. ^ Chandler, David (December 7, 2009). "Rethinking artificial intelligence". MIT News. Massachusetts Institute of Technology.
  3. ^ "Nuffield Department of Surgical Sciences". Nuffield Department of Surgical Sciences. University of Oxford.
  4. ^ "Oxford Computational Neuroscience Laboratory". Oxford Computational Neuroscience Laboratory. University of Oxford.
  5. ^ "Synthetic Intelligence Lab". Synthetic Intelligence Lab. Massachusetts Institute of Technology.
  6. ^ "Center for Advanced Defense Studies". Center for Advanced Defense Studies. Center for Advanced Defense Studies.
  7. ^ a b "Brain Sciences Foundation". Brain Sciences Foundation. Brain Sciences Foundation.
  8. ^ 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.
  9. ^ 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.
  10. ^ a b c Howard, Newton; Guidere, Mathieu (January 2012). "LXIO: The Mood Detection Robopsych". Brain Sciences Journal.
  11. ^ "Biological Coprocessors". Biological Coprocessors. Biological Coprocessors, Inc.
  12. ^ Howard, Newton. "Patent US20130338526-A1". Google Patent DB. US Patent & Trademark Office.
  13. ^ a b Howard, Newton (2012). "Brain Language: The Fundamental Code Unit" (PDF). Brain Sciences Journal. Brain Sciences Foundation.
  14. ^ a b Howard, Newton (2015). The Brain Language. London, UK: Cambridge Scientific Publishing. ISBN 978-1-908106-50-6.
  15. ^ 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.
  16. ^ Howard, Newton; Stein, J. (2011). "Computational Methods for Clinical Applications: An Introduction". Functional Neurology, Rehabilitation and Ergonomics.
  17. ^ 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.
  18. ^ Howard, Newton; Stein, J. F. (2015). "Mathematical Review for Cortical Computing Proposition for Brain Code Hypothesis". Frontiers Systems Neuroscience.
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