A monoclonal antibody against choline acetyltransferase (ChAT), the acetylcholine synthesizing enzyme, was used to determine the morphological characteristics of cholinergic neurons and axon terminals within the rat septum. Light microscopy revealed numerous large fusiform or multipolar ChAT-immunoreactive neurons in the medial septal nucleus/diagonal band complex (MSDB). In contrast, virtually no immunostained cells were found in the lateral septum (Nc. septalis dorsalis and Nc. septalis lateralis). Fine immunostained fibers were most abundant close to the midline in the MSDB mainly following an ascending course. A few thin ChAT-immunoreactive fibers and terminallike pericellular punctate structures were observed in the inner part of the dorsal septal nucleus. Electron microscopy of ChAT-immunoreactive neurons revealed large cell bodies rich in cytoplasmic organelles. The cell nuclei regularly exhibited multiple invaginations of the nuclear membrane. Only rarely were terminals found that established synaptic contacts on the cell bodies of immunostained neurons. In contrast, numerous terminals formed synaptic contacts on immunoreactive dendrites. ChAT-immunopositive terminals were studied in thin sections from the MSDB and from the dorsal septal nucleus. In both regions they appeared as heavily immunostained vesicle-filled boutons that established symmetric and asymmetric synaptic contacts. In the dorsal septal nucleus immunostained terminals often showed a basketlike arrangement around immunonegative cell bodies. Our fine structural study provides evidence that cholinergic neurons in the MSDB are similar to cholinergic neurons in the basal nucleus and neostriatum, which have been described by other investigators. The presence of cholinergic synapses in the septal complex indicates that this region not only contains cholinergic projection neurons, but receives a cholinergic input itself.
The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.
An experimental study of computational model of the CA3 region presents cog-nitive 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).
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 (gan-glion 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).
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