The aim of this paper is to present a novel algorithm for learning and forgetting within a very simplified, biologically derived model of the neuron, called firing cell (FC). FC includes the properties: (a) delay and decay of postsynaptic potentials, (b) modification of internal weights due to propagation of postsynaptic potentials through the dendrite, (c) modification of properties of the analog weight memory for each input due to a pattern of long-term synaptic potentiation. The FC model could be used in one of the three forms: excitatory, inhibitory, or receptory (ganglion cell). The computer simulations showed that FC precisely performs the time integration and coincidence detection for incoming spike trains on all inputs. Any modification of the initial values (internal parameters) or inputs patterns caused the following changes of the interspike intervals time series on the output, even for the 10 s or 20 s real time course simulations. It is the basic evidence that the FC model has chaotic dynamical properties. The second goal is the presentation of various nonlinear methods for analysis of a biological time series. (Folia Morphol 2018; 77, 2: 221–233)
An experimental study of computational model of the CA3 region presents cognitive and behavioural functions the hippocampus. The main property of the CA3 region is plastic recurrent connectivity, where the connections allow it to behave as an auto-associative memory. The computer simulations showed that CA3 model performs efficient long-term synaptic potentiation (LTP) induction and high rate of sub-millisecond coincidence detection. Average frequency of the CA3 pyramidal cells model was substantially higher in simulations with LTP induction protocol than without the LTP. The entropy of pyramidal cells with LTP seemed to be significantly higher than without LTP induction protocol (p = 0.0001). There was depression of entropy, which was caused by an increase of forgetting coefficient in pyramidal cells simulations without LTP (R = –0.88, p = 0.0008), whereas such correlation did not appear in LTP simulation (p = 0.4458). Our model of CA3 hippocampal formation microcircuit biologically inspired lets you understand neurophysiologic data. (Folia Morphol 2018; 77, 2: 210–220)
Congenital abnormalities of the aortic arch arise due to a defect in the unilateral disappearance of arteries of the IVth and exceptionally of the IIIrd primary branchial arches and also of the appropriate sections of paired dorsal aortas. Apart from the cases of complete “situs inversus” and a double aortic arch, the following anatomical possibilities can be distinguished: A — a left-sided aortic arch with a properly established system of branches, B — a left-sided aortic arch with an aberrant right subclavian artery, C — a left-sided aortic arch with a retroesophageal course and right-sided descending aorta or retro-esophageal course of the brachiocephalic trunk onto the right side, D — a right-sided aortic arch of the „symmetric” type usually coexisting with cyanotic congenital heart lesions, E — a right-sided aortic arch with a retro-esophageal bulge and an aberrant left subclavian artery, and F — a right-sided aortic arch with an aorta descending left-sidedly or brachiocephalic trunk going left-sidedly behind the esophagus. At the Department of Anatomy from 1945 to 1998, 1700 adult cadavers were examined. Throughout this time, one case of each of the types E and C and two cases of the type B were noted in the material. Regardless of the rare occurrence among adults (about 0.01%), the abnormal course of the aortic arch can be the reason for atypical clinical symptoms such as esophageal compression and dysphagia or insufficient cerebral blood supply.