Varlės vidurinių smegenų stogo (tectum) neuronų aktyvumo, sujaudinus vieną tinklainės ganglinę ląstelę, tyrimas

• Main Author: Batulevičienė, Vaida
• Other Authors: Skeberdis, Vytenis
• Format: Doctoral Thesis
• Language: Lithuanian
• Published: Lithuanian Academic Libraries Network (LABT) 2006
• Subjects: Biology; NMDA receptors; Suprathreshold excitation; Viršslenkstinis sujaudinimas; Retinotektalinis perdavimas; NMDA receptoriai; Retinotectal transmission; Biologija
• Online Access: http://vddb.library.lt/fedora/get/LT-eLABa-0001:E.02~2006~D_20060621_151049-34399/DS.005.0.01.ETD

It is considered that coincident inputs from multiple presynaptic axons are required to achieve both the suprathreshold level of excitation for the central neurons and the activation of NMDA receptors. The aim of the present study was to determine, whether a discharge of single retinal ganglion cell, which axon terminates in the tectum layer F, can evoke a suprathreshold excitation of frog tectum neurons and activate the NMDA receptors. Extracellular recordings of the neuronal activity elicited by the electrical stimulation of single optic fiber were made in frog tectum. We conclude, that: (1) a train of action potentials of single optic fiber, which terminates in the frog tectum layer F, surely elicits a suprathreshold excitation of tectal neurons at physiological conditions. The suprathreshold level is achieved due to the frequency facilitation of the fast non-NMDA retinotectal synaptic potentials. (2) The train of action potentials of higher frequency activates the NMDA receptors of the tectal neurons at physiological conditions. The activation of NMDA receptors is achieved due to the temporal summation and frequency facilitation of the fast synaptic potentials. (3) Two different kinds of optic fibers (likely darkness and moving edge detectors) exhibit different efficiency to achieve both the suprathreshold excitation of the tectal neurons and the activation of NMDA receptors. Findings of the present study improve the understanding of how the local neural network operates... [to full text]