Brief Biography

Roman R. Poznanski, B.Sc (Hons), M.Sc (Monash), Ph.D (ANU)

Roman R. Poznanski is a full professor with extensive background in teaching and research. He was recently awarded a certificate for teaching excellence. He is the founder and currently the chief editor of the Journal of Integrative Neuroscience, and is also on several editorial boards such as: Algorithms and the International Journal of Neural Systems.

He has spent years in research at the interface of visual neuroscience and mathematical modeling. His main research began on the modeling of retinal mechanisms in visual perception in close collaboration with W.R. Levick from the John Curtin School of Medicine, Australian National University in Canberra. He subsequently developed a more accurate model of a starburst amacrine cell in order to show how direction-selectivity is processed by a network of these cells. His modeling work was the first to rigorously predict a locus of retinal direction selectivity; this prediction has been validated experimentally.

His other focus 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). The modeling allows for integration across spatial scales by imposing a distributed structure upon the neuronal circuitry with neuromodulatory effects enabling the relationship between brain structure, function and behavior. He was the first to reveal how cell-level biophysical properties (e.g., ion channels; neuronal geometries) may be explicitly incorporated into an analytical formalism which predicts network-level functionality. This approach has two major advantages: (1) avoids entirely the mathematical errors and uncertainties inevitable in iterative computational models which necessarily discretize time and space; (2) provides an analytical framework for generating complete and exact solutions for network output. This led him to consider more sophisticated artificial systems (embodied in the book, Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics, CRC Press, 2005).

Recently with Dorian Aur (Stanford University) his main research focus has been on unifying electrodiffusion theory with cable theory, and identifying calcium wave propagation with soliton-like behavior. He has also embarked with Stanislaw Brzychczy (AGH University of Science and Technology) on the development of a new mathematics in neuronal modeling. Their research invokes the application of functional analysis to nonlinear problems arising in neuroscience. The impact of such collaboration will result in mathematically proving that ‘compartmental models of spiking neurons’ are dynamically implausible representations of real neurons.

He has advised several graduate students: (1) Hiroshi Yamamoto 1996 M.Sci. “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.Comp.Sci “Noisy Cables”, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia;(4) Chan Siow Cheng 2011 Ph.D “Population Density Approach for Modeling Systems of Morris-Lecar Neurons”, Faculty of Engineering and Science, UTAR, Malaysia.

His most significant achievements include:

(i) First to pinpoint the neural circuitry underlying retinal motion perception in mammals. [27,1,8]

(ii) First to show that conduction velocities in dendrites are non-constant [15]. 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. Solitary traveling waves in axons as envisaged by the Hodgkin-Huxley model are replaced by dissipative waves that are conducive for the rich-logic requirements in cognitive information processing. [16, 17]

(iii) First to solve the most difficult partial differential equations in classical neurophysiology-the Frankenhaeuser-Huxely equations [9]

(iv) First to construct synaptically and gap-junctionally connected neural networks with ionic channels in dendritic cable structures. Such models have been applied to brain function through the development of large-scale brain cell assemblies [12, 19, 22]

(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 behavior of a large population of neurons grouped together as assemblies [10]

(vi) First to propose and develop nested neural network models for fMRI [7]

(vii) First to debunk the assumption of isopotentiality of small compartments (under 0.2λ) as a result of significant thermal noise [5]

(viii) First to propose a model-based framework for the development of a cognitive brain-computer interface [6]

(ix) First to show that the calcium-induced-calcium-release in the starburst amacrine cells is not responsible for the mechanism of directional selectivity in the retina [2]

(x) Together with Stanislaw Brzycyhczy first to apply functional analysis to nonlinear problems in neuroscience to reveal solutions differ when space is discretized in most computational models [4]