Informatica

Mammalian brains consisting of up to 10¹¹ neurons belong to group of the most complex systems in the Universe. For years they have been one of the hardest objects of simulation. There are many different approaches to modelling of neurons, but one of the most biologically correct is Hodgkin-Huxley (HH) model. Simulations that require solving a large number of nonlinear differential equations (fundamental in HH model) are always time and power consuming. The structures discussed in this article simulate a part of the rat somatosensory cortex. We use a modular architecture of the network divided into layers and sub-regions. Because of a high degree of complexity effective parallelisation of algorithms is required. We propose method of parallelisation for the network and the results of simulations using GENESIS parallelised for MPI environment are presented. An occurrence of nonlinear behaviour is demonstrated. Most notably, in large biological neural networks consisting of the HH neurons, nonlinearity is shown to manifest itself in the Poincaré sections generated for the varying value of neural membrane's potential.

Neural networks built of Hodgkin–Huxley neurons were examined. These structures behaved like Liquid State Machines (LSM). They could effectively process different input signals (i.e., Morse alphabet) into precisely defined output. It is also shown that there is a possibility of logical gates creation with use of Hodgkin–Huxley neurons and simple LSMs.