Biomedical subjects

András Lorincz

Publications and source records attributed to András Lorincz.

4 recordsLinked to original sources

PubMed2006

Novel anti-cholesterol monoclonal immunoglobulin G antibodies as probes and potential modulators of membrane raft-dependent immune functions.

Natural autoantibodies against cholesterol are present in the sera of all healthy individuals; their function, production, and regulation, however, are still unclear. Here, we managed to produce two monoclonal anti-cholesterol antibodies (ACHAs) by immunizing mice with cholesterol-rich liposomes. The new ACHAs were specific to cholesterol and to some structurally closely related 3beta-hydroxyl sterols, and they reacted with human lipoproteins VLDL, LDL, and HDL. They bound, usually with low avidity, to live human or murine lymphocyte and monocyte-macrophage cell lines, which was enhanced substantially by a moderate papain digestion of the cell surface, removing some protruding extracellular protein domains. Cell-bound ACHAs strongly colocalized with markers of cholesterol-rich lipid rafts and caveolae at the cell surface and intracellularly with markers of the endoplasmic reticulum and Golgi complex. These data suggest that these IgG ACHAs may serve as probes of clustered cholesterol (e.g., different lipid rafts) in live cells and thus may also have immunomodulatory potential.

Antibodies, Monoclonal↗

PubMed2005

Competitive spiking and indirect entropy minimization of rate code: efficient search for hidden components.

Our motivation, which originates from the psychological and physiological evidences of component-based representations in the brain, is to find neural methods that can efficiently search for structures. Here, an architecture made of coupled parallel working reconstruction subnetworks is presented. Each subnetwork utilizes non-negativity constraint on the generative weights and on the internal representation. 'Spikes' are generated within subnetworks via winner take all mechanism. Memory components are modified in order to directly minimize the reconstruction error and to indirectly minimize the entropy of the spike rate distribution, via a combination of a stochastic gradient search and a novel tuning method. This tuning dynamically changes the learning rate: the higher the entropy of the spike rate, the higher the learning rate of the gradient search in the subnetworks. This method effectively reduces the search space and increases the escape probability from high entropy local minima. We demonstrate that one subnetwork can develop localized and oriented components. Coupled networks can discover and sort components into the subnetworks; a problem subject to combinatorial explosion. Synergy between spike code and rate code is discussed.

Action Potentials↗

PubMed2004

Kalman filter control embedded into the reinforcement learning framework.

There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-gaussian Kalman filter model allows the on-line estimation of optimal control by using reinforcement learning and overcomes this difficulty. Moreover, the emerging learning rule for value estimation exhibits a Hebbian form, which is weighted by the error of the value estimation.

Algorithms↗

PubMedDate not supplied

The mystery of structure and function of sensory processing areas of the neocortex: a resolution.

Many different neural models have been proposed to account for major characteristics of the memory phenomenon family in primates. However, in spite of the large body of neurophysiological, anatomical and behavioral data, there is no direct evidence for supporting one model while falsifying the others. And yet, we can discriminate models based on their complexity and/or their predictive power. In this paper we present a computational framework with our basic assumption that neural information processing is performed by generative networks. A complex architecture is 'derived' by using information-theoretic principles. We find that our approach seems to uncover possible relations among the functional memory units (declarative and implicit memory) and the process of information encoding in primates. The architecture can also be related to the entorhinal-hippocampal loop. An effort is made to form a prototype of this computational architecture and to map it onto the functional units of the neocortex. This mapping leads us to claim that one may gain a better understanding by considering that anatomical and functional layers of the cortex differ. Philosophical consequences regarding the homunculus fallacy are also considered.

Afferent Pathways↗