|Dr. Roman R. Poznanski|
Caspary Auditorium at The Rockefeller University
Roman R. Poznanski
is the Founder/Owner and Chief Editor of the Journal of Integrative Neuroscience: a transdisciplinary journal at the interface of theory and empirical brain research.
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 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 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 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.
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 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.
The dogma of super 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. 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 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.|
Recently Professor Poznanski's 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. 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 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 physicalist version of neutral monism of the Russellian-type is the metaphysics of choice.
BIOPHYSICS OF CONSCIOUSNESS (2017) is the first book to elucidate the biophysical basis of phenomenological consciousness.
The final integration to consciousness is the ultimate goal of brain science. Integrative brain function that is based on neuroimaging and statistical averaging whitewashes the functional interactions which are nonlocal in the brain. Consciousness is fundamentally nonlocal quantum dynamic effect emerging as qualia 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. 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. In fact 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 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 the 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 to understanding the computational properties of the brain. Unfortunately most of these neural nets are unrealistic in important respects."
(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 the fallacy of computationalism in neuroscience Mathematical Neuroscience [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) Subneuronal integration of information in the endogenous electromagnetic field of the brain [to appear].
(xxiii) Calcium source heterogeneity in starburst amacrine cells underlying speed tuning of direction-selective responses [to appear ].
2.Elemental consciousness in machines
3. Foundations of intrinsic intelligence
4. Consciousness as an endogenous electromagnetic field
5. Conscious recall of memory engrams
6. Consciousness in wetware: Brain-inspired intelligence technology
7. Nonlocal interactions of brain functions in hierarchical systems
8. Sentience in machines based on two-brains hypothesis
9. Foundations for mechanization of consciousness
*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”.