This work presents concepts of the use of algorithms inspired by the functions and properties of the nervous system in dense wireless networks. In particular, selected features of the brain consisting of a large number of nerve connections were analyzed, which is why they are a good model for a dense network. In addition, the action of a selected cells from the nervous system (such as neuron, microglia or astrocyte) as well as phenomena observed in it (e.g. neuroplasticity) are presented.
The way brain networks maintain high transmission efficiency is believed to be fundamental in understanding brain activity. Brains consisting of more cells render information transmission more reliable and robust to noise. On the other hand, processing information in larger networks requires additional energy. Recent studies suggest that it is complexity, connectivity, and function diversity, rather than just size and the number of neurons, that could favour the evolution of memory, learning, and higher cognition. In this paper, we use Shannon information theory to address transmission efficiency quantitatively. We describe neural networks as communication channels, and then we measure information as mutual information between stimuli and network responses. We employ a probabilistic neuron model based on the approach proposed by Levy and Baxter, which comprises essential qualitative information transfer mechanisms. In this paper, we overview and discuss our previous quantitative results regarding brain-inspired networks, addressing their qualitative consequences in the context of broader literature. It is shown that mutual information is often maximized in a very noisy environment e.g., where only one-third of all input spikes are allowed to pass through noisy synapses and farther into the network. Moreover, we show that inhibitory connections as well as properly displaced long-range connections often significantly improve transmission efficiency. A deep understanding of brain processes in terms of advanced mathematical science plays an important role in the explanation of the nature of brain efficiency. Our results confirm that basic brain components that appear during the evolution process arise to optimise transmission performance.
The present study investigated the expression of androgen receptor (AR) in neurons of the anterior pelvic ganglion (APG) and celiac-superior mesenteric ganglion (CSMG; ganglion not involved in the innervation of reproductive organs) in the male pig with quantitative real-time PCR (qPCR) and immunohistochemistry. qPCR investigations revealed that the level of AR gene expression in the APG tissue was approximately 2.5 times higher in the adult (180-day-old) than in the juvenile (7-day-old) boars. Furthermore, in both the adult and juvenile animals it was sig- nificantly higher in the APG than in CSMG tissue (42 and 85 times higher, respectively). Immu- nofluorescence results fully confirmed those obtained with qPCR. In the adult boars, nearly all adrenergic (DβH-positive) and the majority of non-adrenergic neurons in APG stained for AR. In the juvenile animals, about half of the adrenergic and non-adrenergic neurons were AR-posi- tive. In both the adult and juvenile animals, only solitary CSMG neurons stained for AR. The present results suggest that in the male pig, pelvic neurons should be considered as an element of highly testosterone-dependent autonomic circuits involved in the regulation of urogenital func- tion, and that their sensitization to androgens is a dynamic process, increasing during the prepu- bertal period.
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