Brief Biography

 

Email: rpoznanski@rockefeller.edu    



Dr. Roman R. Poznanski
Caspary Auditorium at The Rockefeller University

Roman R. Poznanski  is an internationally acclaimed scientist  known for his work on  retinal direction selectivity.  He is the Chief Editor of the  Journal of Integrative Neuroscience®. Journal of Integrative Neuroscience® is a registered trademark of Roman R. Poznanski.

His contribution to research began in elucidating visual perception mechanisms with G.A.Horridge, FRS from the Research School of Biological Sciences and   W.R.Levick, FRS from the John Curtin School of Medicine, Australian National University in Canberra. His 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 mechanism involved in  retinal direction selectivity within the microstructure of each starburst amacrine cell 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 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) .


His more recent work focuses on electrophysiological applications of  cable theory with microstructure to elucidate how polarization-induced capacitive currents affect the excitability process in general. The new models find their foundations in Maxwell's theory of the electromagnetic field. In classical Nernst-Planck theory, the neuronal 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 a Maxwellian approach is not physicochemical and currents caused by concentration gradients are neglected. However, this new cable theory of protein polarization approximates electrodiffusion in physicochemical 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 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 lead to nonlinear superposition necessary for carrying semantic information in long-term memory.



Electrotonic potentials are classically non-propagating local potentials in dendrites. They are sub-threshold waveforms and have no refractory period and propagate passively over short distances and for short time periods. We know that there is a transition from sub-threshold to supra-threshold when membrane potential exceeds a threshold (usually in the axon hillock) than conformational changes result in action potentials. There are also g-pulses occurring in dendrites reflecting this transition. Solitonic conduction of electrotonic signals in neuronal branchlets does not fall into any of the classical electrical signaling modes and was shown to be a novel signaling mode, interdependent on polarized microstructure and independent of synaptic potentials. From: Solitonic conduction of electrotonic signals in neuronal branchlets with polarized microstructure, Scientific Reports, vol. 7: 2746 (2017).        

  



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 spanning multiple brain regions and over multiple timescales, but not across spatiotemporal scales. How long-term procedural memories in the striatum are retrieved is different from semantic memories in the cortex. The neural dynamics are uniquely driven  by electrical patterns not based on the stereotypical spikes.

 

 

 

 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.

In the 2010s he had collaborated with the Polish mathematician 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 computational 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 for  '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 because compartmentalization and discretization are subject to dynamical misalignment.

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 behavioral phenomenon, ignoring the constraints that higher-level properties exert on the possible brain functions of that mechanism. One of these constraints is the continuity of brain functions, which is intrinsically difficult to harness computationally.  The integration of brain functions depends on nonlocal interactions of brain functions.

Computational neuroscience defines loosely 'computations' as the mantra associated with brain functioning. 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 computation is discovered in physics. For example, computational properties are physical properties; that is, that computation is "intrinsic to physics". In reality, computation is not discovered in 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. Neural computations merely describe observer-relative intelligence and not observer-independent intelligence, i.e., biological intrinsic intelligence.

The dogma of superintelligence: 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. Indeed most if not all artificial neural networks are based on connectionism and work by changing their synaptic connections.  At best they can mimic, for example, sequential learning.  The decoding of memories is mediated by conscious introspection without learning or changes to synaptic connections. Alternative models based on Biophysical Neural Networks must be used to address  the interrelationship between consciousness and nonsynaptic plasticity mechanisms of memory.   

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.

He is currently advancing the quantum foundations of biological consciousness. 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 corresponding to the implicate order.  If qualia in bats, birds, and other animals, is 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 intrinsic information is harnessed  in unpredictable, non-local quantum interactions within billions of neurons. Non-reductive physicalism in the sense of integrative neuroscience together with panexperiential materialism is the metaphysics of choice.

In the book Biophysics of Consciousness (2017), Poznanski and  colleagues pioneered the idea of  qualia  as the content of consciousness in  protein pockets, whose supramolecular properties were first investigated by Nobel laureate Albert Szent-Györgyi.  He posits that qualia are integrated at the picoscale independently of cognition, thus undermining the prevailing dogma that neural, cognitive information underwrites our consciousness.
 

 

The Nonlocal Mind in the Brain

How the brain codes consciousness at the very small scale 

1.  Molecular Bohmian mechanics  

2. Self-referential amplification of simultaneity 

 

3. Interconnectedness of active-intrinsic information and functional continuance

4. The transformation capacity of information from quantum to classical

5. Conscious recall of  memory engrams

6. Consciousness in wetware: Brain-inspired intelligence technology

7. Nonlocal  cortical effects based on pilot-wave theory

8.  Sentience in machines based on two-brains hypothesis

9.  Towards artificially conscious polaritonic devices

10. Spontaneous potentiality as a precursor to cognitive binding

11.  Free will,  Qualia, and Feelings in vivo

12.  Holism as a property of self-reference

13.   Holonomic brain theory

14.  Self-referential identity theory  and  nonreductive physicalism* 

*The American philosopher Jaegwon Kim defines “nonreductive physicalism” as: “Mental phenomena cannot be reduced to physical phenomena”.  Our definition is different: “Physical phenomena that are integrated across levels cannot be reduced to multiple numbers of single level physical phenomena”.

 



















 




BIOPHYSICS OF CONSCIOUSNESS (2017) is the first book to elucidate the biophysical  basis of brain-based 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 how each chemical reaction is coordinated 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.

Th 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 the 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 the abolition of consciousness is not a loss of brain function through cortical sensory integration but in the brain's total loss of energy.
 

The hard problem is how the subjective aspect of consciousness or phenomenal consciousness facilitates 'qualia'. The neuroscience of consciousness is the easy problem of  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 consciousness 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 intrinsic 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.

The final integration of consciousness is the ultimate goal of brain science. Integrative brain function that is based on neuroimaging and statistical averaging whitewash  the functional interactions which are nonlocal in the brain. Consciousness is fundamentally nonlocal  quantum dynamic effect within cerebral networks. To integrate across scale, the development of a field theory for hierarchical and functional integration in the brain is needed at the quantum and classical realms of reality. To understand the Hard Problem is the attempt to explain consciousness at the junction/edge of the quantum and classical realms of reality. The anomaly placed upon the notion of integrated information whence intrinsic and Shannon information cannot be integrated because physical laws do not exist that allow spanning across the classical and the quantum realms of reality is based on reductionism.

Dr. Poznanski has written papers in high impact ISI-indexed neuroscience journals focusing exclusively on data-rich integrative modeling (not multi-scale, but across scale), realization of intrinsic intelligence as a dynamical process that can be influenced by environmental factors, and his most recent work is  on the nonlocal functional interactions across brain regions below the molecular level as the bedrock of consciousness. This research aims to forge forward in the development of truly brain-inspired intelligence technology leading towards the final frontier of conscious artefacts which are man-made constructs that have the capability to fathom the quantum-classical transition in an integrative way. Subjective experiences or sentience arise at the quantum-classical junction which we define to be "qualia". These constructs are built on principles that are far different to cognitive computing inscribed on silicon chips and deep learning algorithms based on optimization techniques (steepest-decent)  where retrograde changes to synaptic weights computationally assign meaning.


As Francis Crick (see below)  remarked these are gimmicks of neural computation and not how neural networks function in the brain. Neural computation relies on discrete symbolic processing while non-reductive physicalism is based on machinery that is non-computational in a sense it conjures continuance across scale (in a teleofunctionalist epistemology).

"The remarkable properties of some recent computer algorithms for neural networks seemed to promise a fresh approach... Unfortunately most of these neural nets are unrealistic in important respects."

     Francis Crick (1989) The recent excitement about neural networks. Nature 337, 129-132.

 

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  potentials  that  are   conducive of rich-logic requirements in dendrites underlying memory decoding [1].

(xii) Co-authored first book on the fallacy of computationalism in neuroscience   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) Solitonic conduction of electrotonic signals in neuronal branchlets with polarized microstructure [Published ]. 

(xx) Nonsynaptic plasticity model of long-term memory engrams [Published ]. 

(xxi) Induced mitochondrial membrane potential for modeling solitonic conduction of electrotonic signals [Published ].  

 

(xxii) Spontaneous potentiality as formative cause of thermo-quantum consciousness [Published]. 

(xxiii) On intrinsic information content of the physical mind in quantized space: against externalism [Published]. 

 

(xxiv) Theorizing how the brain encodes consciousness based on negentropic entanglement [Published].
 

(xxv)  Molecular orbitals of delocalized  electron clouds in neuronal domains [Published].
  

(xxvi) Panexperiential materialism: a physical exploration of qualitativeness  in the brain [Published].

(xxvii) On the nature of raw feelings  [to appear].

(xxviii) Calcium source heterogeneity in starburst amacrine cells underlying speed tuning of direction-selective responses [to appear ].

If every neuron in a human brain was accurately simulated in a computer, would it result in human consciousness?

 

No. Simulations or models do not carry first person capabilities, so a simulation will  be a road map or a blue print to move from metaphysics to reality. The model if implemented in a robotic devise (hardware: spintronics, wetware: quantum fluid) will only exhibit consciousness if the "accuracy" of the model incorporates an understanding of the roots of consciousness based on biomolecular intraneuronal energy processing as suggested in the two-brains hypothesis and panexperiential materialism.

 

 Forthcoming Book

 

Everyone believes consciousness "emerges" from neural activity. A few others say it is universal in the cosmos and interacts with the brain in some way, but we say the brain is a conjugate of mind. A revolutionary idea that will not only change our understanding of the relationship between mind and brain, but holds great promise for the development of artificial consciousness in future Strong AI technologies beyond deep learning gimmicks used today.