Brief Biography

Professor Dr. Roman R. Poznanski
Caspary Auditorium at The Rockefeller University
Roman R. Poznanski  is a biological dynamicist known for his theoretical work on consciousness as a quantum dynamic effect. Dr. Poznanski has written papers in high impact ISI-indexed neuroscience journals focusing exclusively on data-rich integrative modeling (not multiscale, but across scale) and realization of intrinsic intelligence as a dynamical process influenced by environmental factors by  applying Bohmian mechanics  to understand  biological nonlocality for the mechanization of phenomenological consciousness. He currently serves as Chief Editor of the prestigious  Journal of Integrative Neuroscience: a transdisciplinary journal at the interface of theory and empirical brain research, published by IOS Press. 

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, which resulted in the first paper to describe the functional implications of starburst amacrine cells in directional selectivity and published in the Bulletin of Mathematical Biology 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 within starburst network microstructure 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, which led to consider more sophisticated artificial systems, like  the 'cognitive' brain-computer interface (embodied in the 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 applied 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 Alan Hodgkin, FRS 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 soliton-like wave.  It was Andrew Huxley, FRS 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 to exist, especially in the submicron branchlets with endogenous proteinaceous structures. Subthreshold oscillations are unstable and collapse on interaction, while non-decremental waves with solitonic properties leads to nonlinear superposition necessary for carrying semantic information in long-term memory.

Dr. Poznanski in Rockefeller University's Weiss Research Building

Dr. Poznanski has embarked on the biophysics 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 electrical pulses. The prevalent hypothesis is that memory is sub-served by modulation in gene expression. The prevailing dogma is that synapses underwrite our long-term memories. The understanding of spike-directivity as the neural correlate of semantic information relies on the transient charge density dynamics within non-stereotypical spikes and his interest remains in the 3D visualization of such spikes in vivo for understanding how long-term semantic memories are stored in the cortex of man.

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. "Painlevé analysis of nonlinear cable equations in neuroscience, Faculty of Biosciences and Medical Engineering, UTM, Malaysia.

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

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

He has also embarked on research collaboration with Stanislaw Brzychczy (AGH University of Science and Technology) on the development of 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 scepticism. 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 cognitive processes in assemblies of networks. Current approximations to neural dynamics   rely on multi-scale modelling or synthetic modelling through the doctrine of biological computation.  An alternative approach is 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 computation is the assumption that it is discovered in the physics. For example, computational properties are physical properties, that is, that computation is "intrinsic to physics". In reality computation is not discovered in the physics, but it is assigned to it. The laws of natural processes are merely contingently computational because the mathematical language we use to express them is biased towards being computational. For example, IBM's TrueNorth® chip integrates a million neurons and 256 million synapses to mimic the architecture of the brain, but cannot replicate its neuroelectrodynamics and therefore lacks intrinsic intelligence. Consequently if there are no representations in the brain, but only selectionism (arising from the quantum realm) maneuvers dynamic continuity as an adaptive pressure from which cognitive processes spontaneously emerge, then neural computations merely describe observer-relative intelligence and not observer-independent intelligence, i.e., biological intrinsic intelligence. Just like artificial neural networks (deep or shallow) cannot simulate biophysical neural networks leading to human cognition,  artificial intelligence (super or general) will not be able to mimic phenomenological consciousness.


MATHEMATICAL NEUROSCIENCE (2013) is the first book on the development of a nonlinear functional analysis to better understand the intricate fallacies of  methodological reductionism in neuroscience.

Recently Professor Poznanski's  research interests changed to quantum neurobiophysics. As 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 he is currently advancing the quantum foundations of biological intrinsic intelligence through application of  Bohmian mechanics. It 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 the nature of subjectivity.

In the book Biophysics of Consciousness (2017) Poznanski and colleagues pioneered the idea of consciousness as a fundamental nonlocal quantum dynamic effect, originating in the cerebral network's  interfacial water protein pockets, 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. This conceptualization posits that consciousness occurs in the neuronal microstructure at the picoscale thus undermining the prevailing dogma that neural cognitive information underwrites our consciousness.

BIOPHYSICS OF CONSCIOUSNESS (2017) is the first book to elucidate the biophysical  basis of phenomenological consciousness.

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 of 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 is what 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 [Published].

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

(xix)  Propagation of electrical solitons in self-excitable neurons with microstructure [to appear]. 

(xx) Modeling electrical soliton propagation in an active branchlet with a homogenous microstructure [to appear]. 

(xxi) Are steady-state electrical pulses in neuronal branchlets a precursor of long-term memory? [to appear]. 

(xxii) A two-pool model for calcium source heterogeneity in starburst amacrine cells underlying speed tuning of direction-selective responses [to appear]. 



Professor R.R. Poznanski
Artificial Consciousness Laboratory