The process of learning in neural net models with Poisson and Gauss connectivities

In this study we examined the dynamic behavior of isolated and non-isolated neural networks with chemical markers that follow a Poisson or Gauss distribution of connectivity. The Poisson distribution shows higher activity in comparison to the Gauss distribution although the latter has more connections that obliterated due to randomness.We examined 57 hematoxylin and eosin stained sections from an equal number of autopsy specimens with a diagnosis of “cerebral matter within normal limits”. Neural counting was carried out in 5 continuous optic fields, with the use of a simple optical microscope connected to a computer (software programmer Nikon Act-1 vers-2). The number of neurons that corresponded to a surface was equal to 0.15 mm2. There was a gradual reduction in the number of neurons as age increased. A mean value of 45.8 neurons /0.15 mm2 was observed within the age range 21–25, 33 neurons /0.15 mm2 within the age range 41–45, 19.3 neurons /0.15 mm2 within the age range 56–60 years. After the age of 60 it was observed that the number of neurons per unit area stopped decreasing.A correlation was observed between these experimental findings and the theoretical neural model developed by professor Anninos and his colleagues. Equivalence between the mean numbers of neurons of the above mentioned age groups and the highest possible number of synaptic connections per neuron (highest number of synaptic connections corresponded to the age group 21–25) was created. We then used both inhibitory and excitatory post-synaptic potentials and applied these values to the Poisson and Gauss distributions, whereas the neuron threshold was varied between 3 and 5. According to the obtained phase diagrams, the hysteresis loops decrease as age increases. These findings were significant as the hysteresis loops can be regarded as the basis for short-term memory.