Brief Biography

Roman R. Poznanski  is a biomathematician* who pioneered the  idea  of consciousness as fundamentally nonlocal  quantum dynamic effect originating in the cerebral networks of interfacial water, whose supramolecular properties were first investigated by  Nobel laureate Albert Szent-Györgyi.  He posits that Gerald Pollack's fourth phase of water, known as 'structured' interfacial water, necessitates for consciousness to emerge in the brain. A world  authority on modeling in the neurosciences, he develops theories in neuroscience with mathematics. He has written papers in high impact ISI-indexed neuroscience journals and serves as Chief Editor of the prestigious  Journal of Integrative Neuroscience: a multidisciplinary science journal at the interface of theory and empirical brain research. 
His main contribution to research began on the modeling of retinal neurons in visual perception with W.R.Levick, FRS  from the John Curtin School of Medicine, Australian National University in Canberra. His modeling work was first to predict the locus of retinal direction selectivity in individual dendritic branches of starburst amacrine cells in the retina in 1990 (and published in 1992). He subsequently developed a more accurate model of a starburst amacrine cell in order to show how direction-selectivity is produced by a network of these cells. The final unification of the yet unknown subcellular model of retinal direction selectivity with starburst network topology remains one of his current research themes in collaboration with Amane Koizumi of the National Institute for Natural Sciences, Tokyo, Japan.  
His other research direction has been on the establishment of a new generation of neural networks, in particular, the biophysical foundations of neural network theory (as embodied in his book, Biophysical Neural Networks, Mary Ann Liebert, 2001). He was the first to reveal how microscopic-level biophysical properties (e.g., endogenous structures, ion channels; neuronal geometries) may be explicitly incorporated into an analytical formalism that predicts mesoscopic-level functionality. His "ionic cable theory" approach has two major advantages: (1) avoids entirely the mathematical errors and uncertainties inevitable in iterative computational models that necessarily discretise time and space; (2) provides a framework for generating complete and exact solutions for network output enabling dynamical continuity to be reflected through spatiotemporal patterns as a field of influence for dynamic cognitive processes. In collaboration with George N. Reeke (Rockefeller University), this led to consider more sophisticated artificial systems, like  the 'cognitive' brain-computer interface (embodied in their book, Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics, CRC Press, 2005) .

He has spent years in research at the interface of neuroscience and mathematical  modeling with particular emphasis on developing neuronal models across scale. His recent collaboration with Jalil Ali (Department of Physics, Laser Center, UTM) focuses on the development of a new cable theory for understanding the precise effect of protein polarization on membrane potential and on the excitability process in general. In particular, non-stereotypical action potentials resulting from discrete distribution of ionic channels along the dendritic arbours of neurons and the influence of cytoskeletal structures on these dendritic spikes. The new theory finds its true foundations in Maxwell's theory of the electromagnetic field. In classical Nernst-Planck theory, the membrane has no structure and therefore, any attempt at combining the dynamics described by time-dependent Nernst-Planck equation for the spatial distribution of ionic concentration with cable theory is fortuitous. Ionic current flow based on Maxwellian approach is not physico-chemical and currents caused by concentration gradients are neglected. However, this new cable theory of protein polarization approximates electrodiffusion in physico-chemical systems, and is comparable with, yet resilient to the epistemological limitations inherent in the classical Hodgkin-Huxley system.  It was Hodgkin who said: “electrodiffusion is like a flea hopping in the storm” meaning  that ionic current flow in the presence of an electric field has no coherency compared with an action potential, but Hodgkin never envisaged ionic current flow as a propagating 'shock wave' or soliton-like wave.  It was Huxley who in 1959 suggested that a subthreshold disturbance can be initiated by numerically solving Hodgkin-Huxley equations, but these  subthreshold oscillations as envisaged by Andrew F. Huxley in the 1950s are not the only subthreshold responses. Subthreshold oscillations do not have a rapid leading-edge reminiscent of a 'shock wave' for decoding memory nor do they reflect upon collision. 

Roman Poznanski in Rockefeller University's Weiss Research Building

Dr. Poznanski has embarked on the physics of memory and how it depends on: (i) changes in protein metabolism accompanying learning; (ii) memory trace formation (encoding) and storage (consolidation) in assemblies at the subcellular level through phosphorylation; and (iii) decoding through dendritic protein polarization by subthreshold signals at the subcellular level. The prevalent hypothesis is that memory is sub-served by modulation in gene expression.

He has advised several graduate students: (1) Hiroshi Yamamoto 1996 M.Sc. “Computer Simulation of Bipolar Cell Coupling in the Teleost Retina,” Faculty of Information Sciences, Toho University, Japan; (2) Tirad Almalahmeh 2009 Ph.D. "Directional Selectivity by Network of Starburst Amacrine Cells in Retina", Faculty of Computer Science and Information Technology, University of Malaya, Malaysia; (3) Seyed Maysam Torabi 2010 M.Sc. "Noisy Neuronal Cables", Faculty of Computer Science and Information Technology, University of Malaya, Malaysia; (4) Chan Siow Cheng 2013 Ph.D "Neural Activity in a  Morris-Lecar Population Density Model", Faculty of Engineering and Science, UTAR, Malaysia; (5) Nur Shafika Abel Binti Razali 2014 Ph.D "Solitons in Neurons", School of Mathematical Sciences, USM, Malaysia;(6)Yaseen Al-Wesabi 2016 Ph.D "Modeling the collision of action potentials with Pseudo Parabolic Hodgkin-Huxley Systems, Faculty of Science, UTM, Malaysia.


 Roman R. Poznanski was awarded a certificate for teaching excellence from the University of Malaya where he was a visiting professor in 2009-2010.

Roman R. Poznanski  (center) with  postgraduate students taking the course “Research Foundations”, University of Malaya 2009.

He has also embarked on a research collaboration with Stanislaw Brzychczy (AGH University of Science and Technology) on the development of a nonlinear analysis methods to better understand the intricate fallacies of methodological reductionism in neuroscience. One recent example of this research is the application of nonlinear functional analysis to the cable equation proving that discrete models of neurons like  'multi-compartmental models and spiking neuron models'  are both dynamically implausible representations of real neurons. This research has implications to  'multi-scale' modeling that are supposed to be ultra 'realistic' attempts at modeling the brain. One limitation is that such 'multi-scale' models are incapable of harnessing 'bridges' across scale without producing a false sense of biological reality. This is because compartmentalization and/or discretization is subject to dynamical misalignment. All research based on compartmental models should be treated with skepticism. The results of compartmental modeling to explain complex dynamical phenomena must be taken with a grain of salt.    Infinite systems  represent hierarchical levels in a way that will not delude the continuous dynamics of neuronal systems across spatiotemporal scales. 

In computational neuroscience, reductionist approaches  span multiple levels of neural organization; however in integrative neuroscience, each level is seamlessly sculptured  as part of a continuum of levels.  Reductionism assumes a direct causal relationship between a molecular/cellular mechanism and a behavioural phenomenon, ignoring the constraints that higher-level properties exert on the possible functions of that mechanism. One of these constraints is dynamic continuity which is intrinsically difficult to harness computationally  because compartmentalization and/or discretization is subject to dynamical misalignment, producing a false sense of biological reality. Integration depends on dynamic continuity, which is manifested  through the electric field inside neurons and across synapses that results in a field of  influence  for augmenting mental processes in assemblies of networks. Current approximations to neural dynamics   rely on multi-scale models through the doctrine of biological computation .  An alternative approach is through relational biology where functional relations between neurobiological processes depend on hierarchical and functional integration in the brain.  From this perspective, hierarchical integration is structural involving dynamic continuity in spatiotemporal patterns, bringing about functional organization, while functional integration is relational enabling a relational organization to be mapped from  the functional organization.

Computational neuroscience defines loosely 'computations' as the mantra associated with functioning of the brain. What these 'computations' signify and portray are often mysterious mechanisms that are yet to be elucidated with precision. The central dogma of computational neuroscience is the assumption that information processing and Shannon information theory are the modus operandi.  As a result, the field is often entwined with how a computer operates.  Yet there are no representations in the brain,  rather selectionism manoeuvring dynamic continuity  as  an adaptive pressure from which cognitive processes spontaneously emerge. Furthermore, the cortex has no information processing capabilities,  only physical interactions  handled by  an electric field inside neurons and across synapses resulting from a match between intrinsic (non-Shannonian) Gödel  information and the environmental cues. Not all parts of the brain possess intrinsic information. For instance, the retina acts as one kind of Turing Machine when it solves the algorithm for directional selectivity. The algorithmic properties of the retina are assigned to it and are not intrinsic. The information to the retina depends on interpretation from outside the system based on Shannon information only. Thus one can conclude that the retina does not offer insight as how the mind operates. 

MATHEMATICAL NEUROSCIENCE is the first book on the development of a nonlinear functional analysis to better understand the intricate fallacies of  methodological reductionism in neuroscience
Roman Poznanski is an advocate of the Bohmian (David Bohm) interpretation of quantum mechanics and the understanding of brain science through a dichotomy of implicate order and explicate order.  This approach is based on realists attempt at interpreting quantum mechanics by distinguishing  the epistemological aspect from the ontological aspect of Heisenberg's uncertainty principle. Shannon information theory as a foundational basis of computational neuroscience corresponds to the explicate order. Intrinsic Gödelian information reflects upon the neurophenomenological aspect of consciousness corresponding to the implicate order.  If consciousness is a rudimentary effect found in bats, birds, and other animals, dominated by cognition in humans that it [sic] is perceived to be consciousness then one begins to move beyond principles of Shannon information theory to explore how the result of unpredictable, non-local quantum interactions within billions of neurons depends on what Karl Popper defined as interactionism replaced with multi-aspect monism. Multi-aspect monism (MAM) is highly relevant for capturing the multitude of biological functions across vastly different scales in an integrative way.  In this sense, cybernetics, computational neuroscience, information theory and information processing without intrinsic Gödel quantum information cannot explain how quantum brain dynamics generates consciousness.

BIOPHYSICS OF CONSCIOUSNESS is the first book to elucidate the biophysical  basis of consciousness for the development of conscious artifacts.

Physical laws are compounded by functional interactions to produce biological laws that can only apply to animate matter. Unlike physical laws, almost all biological laws are time-dependent and thus seem to appear as too accidental or transient to be named as 'laws' in the explicate order, but in the implicate order, the emergence of biological laws can differentiate between complex adaptive systems that evolved consciousness from machines with simpler mechanisms. For this reason, approaching biological organisms with reductionism sacrifices the whole in order to study the parts. What makes living matter profoundly different from ordinary inorganic matter is the way in which each chemical reaction is co-ordinated with all the other for the good of the whole. This transcends explanatory physical laws and requires biological laws. As we know it, consciousness is invariably associated with life, so the notion of conscious artifacts is possible once the mechanization of consciousness becomes a reality. Artificial life reproduced as a brain in a supercomputer suffers from methodological reductionist issues that falsely reproduce the true workings of biological organization and causality.

Visual cortex is connected with the claustrum that plays a role in sensory integration, relaying visual information to most parts of the neocortex, but it is not the loci in the brain for consciousness. If it were then hydranencephalic children would not be self-aware. Decorticate animals groom and feed quite well, in fact they are difficult to distinguish from intact ones. This is less the case in humans who are more dependent on the cortex for the execution of bipedal locomotion and fine motor control, of course not to mention language and communicable conscious awareness. Zapping deep in the brain with high frequency electric shocks would immediately shut down the corticoclaustral axons. Also the patient's brain stem and subcortical structures would be compromised. In hindsight, the claustrum is located in the cerebral cortex is not where consciousness resides in the brain, but rather it plays a vital role in sensory integration. A genuine case of abolition of consciousness is not loss of brain function through cortical sensory integration, but in the brain's total loss of energy.

Consciousness comes in several stages: elemental, phenomenal and access. The hard problem is how the subjective aspect of consciousness or phenomenal consciousness facilitates 'qualia'. The neuroscience of consciousness is the easy problem how access consciousness interrelates with cognition. The astonishing hypothesis is not that we are just a pack of neurons, but rather a molecular system impacted through a quantum dynamic effect occurring within the pack of neurons in the frequency domain. When you are in the frequency domain you are in the platonic world in the sense of Sir Roger Penrose. Enactivism through periodicities of discrete energies is undoubtedly a precursor for self-awareness as 'elemental' consciousness with quantum nonlocality providing the impetus for phenomenal consciousness to emerge and how brain dynamics generates access consciousness or Damasio's core consciousness. At the quantum realm, where 'elemental' consciousness facilitates qualia one needs quantum EM potentials. Consciousness is fundamentally nonlocal and integrated information in the brain reflects upon this nonlocality. This contrasts with local  EM fields integrating information through a global endogenous EM field proposed in the CEMI theory.

'Physical integration' and not 'neuronal integration' is what drives biological nonlocality in the brain, as Alfred J. Lotka described "Consciousness embroidered as upon a canvas" in his treatise on Elements of  Physical Biology, 1925.  Yet 'physical integration' in animate matter differs from 'physical integration' in inanimate matter. There are two aspects to consider: physical aspect because of structure and 'biological' aspect because of function. Consciousness is fundamentally nonlocal yet  emerges within cerebral networks of interfacial water where nonlocal functional interactions associated with functional integration  enable quantum information to take on a functional role in the brain.  Biological nonlocality  in the brain defines integrated information. 
Roman Poznanski at the Rockefeller University, New York City 

His most significant achievements include: 

(i) First to pinpoint the locus underlying retinal direction selectivity in mammals, circa 1992. Through modeling starburst amacrine cells he was first  to predict that direction selectivity is linked to their individual dendritic branches in a way that is still unknown with precision [5,6,13, 32].
(ii) First to show that conduction velocities in dendrites are nonconstant [20]. This theoretical result showed that sparse distribution of ionic channels will determine how information is processed differently in the dendrites of neurons as opposed to those in axons [21, 22].

(iii) First to find approximate analytical solutions to the Frankenhaeuser-Huxely equations [14, 38].

(iv) First to construct synaptically and gap-junctionally connected neural networks with ionic channels discretely juxtaposed in dendritic cable structures. Such ionic cable models have been applied to brain function through the development of large-scale brain cell assemblies [17, 23, 24, 27, 39, 40].

(v) First to introduce the conceptual idea that cognition is determined by how the distribution of endogenous proteins (e.g., ion channels) and synaptic inputs along the dendrites of neurons is integrated with the collective behaviour of a large population of neurons grouped together as assemblies [15].
(vi) First to propose and develop nested neural network models for fMRI [12].

(vii) First to debunk the assumption of isopotentiality of small compartments (under 0.2λ) as a result of significant thermal noise [10].

(viii) First to propose a model-based framework for the development of a cognitive  brain-computer interface [11].
(ix)  First to use functional  analysis  to prove how neural responses differ when continuous space is discretised in computational models  [2,9].
(x)  New theories of long-term memory away from synapses and dependent on ionic charge configurations [3,4].  

(xi) First to elucidate the precise effect of protein polarization on membrane potential and  on the excitability process through propagating subthreshold  threshold  shock  waves  that  are   conducive of rich-logic requirements in dendrites underlying memory decoding [1].

(xii) Co-authored first book on Mathematical Neuroscience  [Published]. 

(xiii) Genomic instantiation of consciousness in neurons through a biophoton field  theory [Published].

(xiv) The two-brain hypothesis: towards a guide for brain-brain and brain-machine interfaces  [ Published]. 

(xv) Genetic algorithms based feature selection for cognitive state classification using ensemble of decision tree  [Published]. 

(xvi) Does heterogeneity of intracellular calcium dynamics underlie speed tuning of direction-selective responses in dendrites of starburst amacrine cells?  [Published]. 

(xvii) Co-edited  the first book to reveal the biophysical basis of consciousness [to appear].

(xviii) Consciousness as a quantum dynamic effect [ to appear].

(xix)  Quantum nonlocality  within the cerebral networks of interfacial water [to appear]. 

(xx) The physical condition for consciousness: interfacial water in pyramidal neurons across the mammalian cerebral cortex [to appear]. 

(xxi)  The endoplasmic reticulum's role in the emergence of speed tuning of direction-selective responses in starburst amacrine cells [ to appear]. 
(xxii) Chaotic solitons in a passive neuron model with intracellular capacitive effects [to appear]. 

(xxiii) Dynamics of long-term memory consolidation in the cerebral cortex [to appear]. 

*Biomathematics is the use of mathematical models to help understand phenomena in biology.