|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), realization of intrinsic intelligence as a dynamical process influenced by environmental factors, and the application of 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.
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 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 is uniquely driven by electrical patterns not based on the stereotypical spike.
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 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.
|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.
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.
|Roman Poznanski at the Rockefeller University, New York City|
(ii) First to show that conduction velocities in dendrites are nonconstant . 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 .
(vi) First to propose and develop nested neural network models for fMRI .
(vii) First to debunk the assumption of isopotentiality of small compartments (under 0.2λ) as a result of significant thermal noise .
(viii) First to propose a model-based framework for the development of a cognitive brain-computer interface .
(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].
(xii) Co-authored first book on Mathematical Neuroscience [Published].
(xxii) A two-pool model for calcium source heterogeneity in starburst amacrine cells underlying speed tuning of direction-selective responses [to appear].
(xxiii) Consciousness: From Quantum to Cognition [to appear].