Biomedical subjects

Robert Kozma

Publications and source records attributed to Robert Kozma.

6 recordsLinked to original sources

PubMed2005

Phase transitions in the neuropercolation model of neural populations with mixed local and non-local interactions.

We model the dynamical behavior of the neuropil, the densely interconnected neural tissue in the cortex, using neuropercolation approach. Neuropercolation generalizes phase transitions modeled by percolation theory of random graphs, motivated by properties of neurons and neural populations. The generalization includes (1) a noisy component in the percolation rule, (2) a novel depression function in addition to the usual arousal function, (3) non-local interactions among nodes arranged on a multi-dimensional lattice. This paper investigates the role of non-local (axonal) connections in generating and modulating phase transitions of collective activity in the neuropil. We derived a relationship between critical values of the noise level and non-locality parameter to control the onset of phase transitions. Finally, we propose a potential interpretation of ontogenetic development of the neuropil maintaining a dynamical state at the edge of criticality.

Algorithms↗

PubMed2005

Chaotic neurodynamics for autonomous agents.

Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limit-cycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multilayer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.

Action Potentials↗

PubMed2005

Outgrowth of neurites from NIE-115 neuroblastoma cells is prevented on repulsive substrates through the action of PAK.

In the central nervous system (CNS), damaged axons are inhibited from regeneration by glial scars, where secreted chondroitin sulfate proteoglycan (CSPG) and tenascin repulse outgrowth of neurites, the forerunners of axons and dendrites. During differentiation, these molecules are thought to form boundaries for guiding neurons to their correct targets. In neuroblastoma NIE-115 cells, outgrowth of neurites on laminin could be induced by serum starvation or inhibition of RhoA by Clostridium botulinum C3 toxin. The outgrowing neurites avoided crossing onto the repulsive substrate CSPG or tenascin. This avoidance response was partially overcome on expression of membrane-targeted and kinase-inactive forms of PAK. In these cells, the endogenous PAK isoforms colocalized with actin in distinctive sites, alphaPAK in the cell center as small clusters and along the neurite shaft and betaPAK and gammaPAK in areas with membrane ruffles and filopodia, respectively. When isoform-specific N-terminal PAK sequences were introduced to interfere with PAK function, substantially more neurites crossed onto CSPG when cells contained a gammaPAK-derived peptide but not the corresponding alphaPAK- or betaPAK-derived peptide. Thus, while neurite outgrowth can be promoted by RhoA inhibition, overcoming the accompanying repulsive guidance response will require modulation of PAK activity. These results have therapeutic implications for CNS repair processes.

ADP Ribose Transferases↗

PubMed2003

On the constructive role of noise in stabilizing itinerant trajectories in chaotic dynamical systems.

This work aims at studying dynamical models of neural networks, which exhibit phase transitions between states of various complexities. We use the biologically motivated KIII model, which has demonstrated excellent performance as a robust dynamical memory device. KIII is a high-dimensional dynamical system with extremely fragmented boundaries between limit cycles, tori, fixed points, and chaotic attractors. We study the role of additive noise in the development of itinerant trajectories. Noise not only stabilizes aperiodic trajectories, but there is an optimum noise level with highly itinerant behavior. We speculate that the previously found optimum classification performance of KIII as a function of the noise level, also identified as chaotic resonance, is related to chaotic itinerant oscillations among various ordered states.

Animals↗

PubMed2003

Basic principles of the KIV model and its application to the navigation problem.

EEG measurements indicate the presence of common-mode, coherent oscillations shared by multiple cortical areas. In previous studies the KIII model has been introduced, which interprets the experimental observations as nonlinear, spatially distributed dynamical oscillations of locally coupled neural populations. KIII can account for the fast and robust classification and pattern recognition in sensory cortices. In order to describe selection of action, planning, and spatial orientation functions, in this paper we expand KIII into the KIV model. KIV approximates the operation of the corticostriatal-hippocampal system. KIV consists of three KI, eight KII and three KIII components, including sensory and cortical systems, as well as the hippocampus, amygdala, and the septum. KIV implements various types of dynamic neural activities. The neural activity patterns determine the emergence of global spatial encoding to implement the orientation function of a simulated animal. Our results indicate the mechanisms, which we believe support the generation of cognitive maps in the hippocampus based on the sensory input-based destabilization of cortical spatio-temporal patterns. In this paper, we describe the conceptual design of the KIV model. We outline the biological background and motivation of the basic principles that are applied to design the KIV computational model. We use the KIV model to explain how the hippocampal neural circuitry functions are constructed and controlled by the corticostriatal-hippocampal loops, supplemented with specific subcortical units. In the second part, we implement these principles using the example of the hippocampal formation as a KIII unit. We demonstrate the learning and navigation principles using the Evolving Multi-module Mobile Agent (EMMA) in 2D software environment.

Animals↗

PubMed2002

Vimentin intermediate filament reorganization by Cdc42: involvement of PAK and p70 S6 kinase.

Rho family GTPases play a major role in actin cytoskeleton reorganization. Recent studies have shown that the activation of Rho family GTPases also induces collapse of the vimentin intermediate filament (IF) network in fibroblasts. Here, we report that Cdc42V12 induces the reorganization of vimentin IFs in Hela cells, and such reorganization is independent of actin and microtubule status. We analyzed the involvement of three serine/threonine kinase effectors, MRCK, PAK and p70 S6K in the Cdc42-induced vimentin reorganization. Surprisingly, the ROK-related MRCK is not involved in this IF reorganization. We detected phosphorylation of vimentin Ser72, a site phosphorylated by PAK, after Cdc42 activation. PAK inhibition partially blocked Cdc42-induced vimentin IF collapse suggesting the involvement of other effectors. We report that p70 S6 kinase (S6K)1 participates in this IF rearrangement since the inhibitor rapamycin or a dominant inhibitory S6K could reduce the Cdc42V12 or bradykinin-induced vimentin collapse. Further, inhibition of PAK and S6K in combination very effectively prevents Cdc42-induced vimentin IF collapse. Conversely, only in combination active PAK and S6K could induce a vimentin IF rearrangement that mimics the Cdc42 effect. Thus, Cdc42-induced vimentin reorganization involves PAK and, in a novel cytoskeletal role, p70 S6K.

3T3 Cells↗