14 – Mathematical Models for Computational Neuroscience

摘要: We present the main aspects of mathematical models for computational neuroscience, with emphasis on basic principles that can drive construction biologically inspired neural networks oriented to cognitive neuroscience problems. This chapter is subdivided into two distinct parts. In first, principal individual units (Hodgkin–Huxley, integrate and fire, rate models) are described, together a brief portrayal synapse formalism. second, assuming simplicity, we summarize peculiarities important network typologies: associative (both hetero- auto-association), self-organized networks, error-correction (within paradigm reinforcement learning). For each network, simulation exempla displayed connections physiological pathological conditions relevance discussed.