|Dr. Roman R. Poznanski|
Caspary Auditorium at The Rockefeller University
Roman R. Poznanski is a mathematician and neurobiologist known for his defining theory of consciousness. He is currently Director of the Artificial Consciousness Lab and serves as the 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.
to research began on the modeling of retinal neurons in visual perception 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 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 more recent work focuses on a new cable theory for understanding the how polarization effects membrane ions and on the excitability process in general. 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.
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 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. 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. 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. However, systems consolidation requires the imprinting of memories mediated by conscious introspection without learning or changes to synaptic connections. Alternative models based on Biophysical Neural Networks must be used to address fundamental problem relating to the interrelationship between consciousness and 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.|
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.
Functional interactions are nonlocal in the brain. Yet 'functional interaction' in animate matter differs from 'physical interaction' 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 the brain's functional hierarchy enable quantum information to take on a functional role in the brain.
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 a quantum biological field theory of consciousness where he is researching on the nonlocal functional interactions of memory to conscious introspection. This research aims to forge forward in the development of truly brain-inspired intelligence technology beyond Google's "DeepMind".
|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].
(xx) Solitons as carriers of enduring memories[Published ].
(xxi) Polarization current in fissured subcellular domains of neuronal branchlets [to appear ].
(xxii) A two-pool model for calcium source heterogeneity in starburst amacrine cells underlying speed tuning of direction-selective responses [to appear ].
(xxiii) The rise of quantum biological field theory and the fall of integrated information as a reliable theory of consciousness[to appear].
(xxiv) Quantum functionality of elemental consciousness in wetware [to appear].
(xxv) The unity of consciousness: a quantum biological field theory of phenomenal binding [to appear].
1. Bohmian mechanics
3. Foundations of intrinsic
4. Brain-inspired intelligence technology
5. Conscious recall of memories
6. Biophysical neural networks
7. Phenomenology as functionality
8. Quantum biological field theory of phenomenal binding