Short (expert) version powerpoint summarizing research paper file 'Paper EGR 183 Modeling Neural Networks in silico'.

1. EGR 183: Modeling Neural Networks in silico Dr. Needham - Fall 2007 Daniel Calrin B.S.E. Daniel Cook Joshua Mendoza-Elias

2. Background: Neurons

5. Short-term and Long-term Effects

6. The Basis of Hebbian Learning

7. Foundation for our Computer Model

8. Types of Neuron Inhibitory Neurons Input Neuron Output Neuron Neuron is voltage-clamped, presynaptic to all neurons in model Inhibitory neurons depress post-synaptic neurons Excitatory Neurons Average firing rates solved at each time step Learning rule determines change in synaptic strength inhibitory synapse excitatory synapse Key: 1 2 3 4 α = - 1 4

9. Synaptic strengths 1.0 1.0 t=t 0 t=t 0+1 + α In phase Out of phase + α Two neurons are firing full-speed: Strengths increase by factor of alpha 1.0 0.0 - β One neuron v i is firing but v j is not: Strengths decrease by factor of beta - β v i v j v i v j v i v j v i v j

10. Inhibitory Neurons t=t 0 t=t 0+1 Excitatory Inhibitory = ...+ w i,j v i +... = ...- w i,j v i +... An inhibitory neuron v i is firing, depressing the post-synaptic neuron Weighted v i is summed negatively into v j Weighted v i is summed positively into v j An excitatory neuron v i is firing, potentiating the post-synaptic neuron v i v j v j v i v i v j v i v j

13. Output Neuron Maxima & Minima Local max near minimum Local min near maximum Maximum at maximum time firing rate Input Neuron

15. Biological parallel with In Silico Starvoytov et al. 2005 Light-drected stimulation of neurons on silicon wafers. J Neurophysiol 93 : 1090-1098.