Neuromorphic Engineering 1 - Interaktive Studienpläne der TU Ilmenau

After the lectures and exercises, the students are able to understand and analyze the principles of neural computation methods so that they can compare various advantages and disadvantages of neuro-inspired networks.

• Biophysical background: neurons, synapses, Data processing invertebrates and vertebrates, Implicit and explicit learning, Short and long-term potentiation, Plasticity
  
• Spiking neurons models
  
• Hebbian learning theory
• Neural Networks: an overview (McCulloch-Pitts Neuron, Perceptron, Adalein/Madaline, ART, Boltzmann-Machine)
• Neuronal analog circuits: Axon Hillock Circuit, LIF-Neuron, STDP, AER
  
  

PowerPoint presentation, blackboard

• Gerstner and Kistler, Spiking Neuron Models. Single Neurons, Populations, Plasticity, Cambridge University Press, 2002
• Analog VLSI and Neural Systems, C. Mead, Addison-Wesley Pub. Comp. 1989