The functional structure of our new network is not preset; instead, it
comes into existence in a random, stochastic manner.
The anatomical structure of our model consists of two input “neurons”, hundreds up to five thousands of hidden-layer “neurons” and one output “neuron”.
The proper process is based on iteration, i.e., mathematical operation governed by a set of rules, in which repetition helps to approximate the desired result.
Each iteration begins with data being introduced into the input layer to be processed in accordance with a particular algorithm in the hidden layer; it then continues with the computation of certain as yet very crude configurations of images regulated by a genetic code, and ends up with the selection of 10% of the most accomplished “offspring”. The next iteration begins with the application of these new, most successful variants of the results, i.é., descendants in the continued process of image perfection. The ever new variants (descendants) of the genetic algorithm are always generated randomly. The determinist rule then only requires the choice of 10% of all the variants available (in our case 20 optimal variants out of 200).
The stochastic model is marked by a number of characteristics, e.g., the initial conditions are determined by different data dispersion variance, the evolution of the network organisation is controlled by genetic rules of a purely stochastic nature; Gaussian distribution noise proved to be the best “organiser”.
Another analogy between artificial networks and neuronal structures lies in the use of time in network algorithms.
For that reason, we gave our networks organisation a kind of temporal development, i.e., rather than being instantaneous; the connection between the artificial elements and neurons consumes certain units of time per one synapse or, better to say, per one contact between the preceding and subsequent neurons.
The latency of neurons, natural and artificial alike, is very importaiit as it
enables feedback action.
Our network becomes organised under the effect of considerable noise. Then, however, the amount of noise must subside. However, if the network evolution gets stuek in the local minimum, the amount of noise has to be inereased again. While this will make the network organisation waver, it will also inerease the likelihood that the erisis in the local minimum will abate and improve substantially the state of the network in its self-organisation.
Our system allows for constant state-of-the-network reading by ineaiis of establishing the network energy level, i.e., basically ascertaining progression of the network’s rate of success in self-organisation. This is the principal parameter for the detection of any jam in the local minimum. It is a piece of input information for the formator algorithm which regulates the level of noise in the system.
The sex steroid hormones (SSHs) such as testosterone, estradiol, progesterone, and their metabolites have important organizational and activational impacts on the brain during critical periods of brain development and in adulthood. A variety of slow and rapid mechanisms mediate both organizational and activational processes via intracellular or membrane receptors for SSHs. Physiological concentrations and distribution of SSHs in the brain result in normal brain development. Nevertheless, dysregulation of hormonal equilibrium may result in several mood disorders, including depressive disorders, later in adolescence or adulthood. Gender differences in cognitive abilities, emotions as well as the 2-3 times higher prevalence of depressive disorders in females, were already described. This implies that SSHs may play a role in the development of depressive disorders. In this review, we discuss preclinical and clinical studies linked to SSHs and development of depressive disorders. Our secondary aim includes a review of up-to-date knowledge about molecular mechanisms in the pathogenesis of depressive disorders. Understanding these molecular mechanisms might lead to significant treatment adjustments for patients with depressive disorders and to an amelioration of clinical outcomes for these patients. Nevertheless, the impact of SSHs on the brain in the context of the development of depressive disorders, progression, and treatment responsiveness is complex in nature, and depends upon several factors in concert such as gender, age, comorbidities, and general health conditions.