EGR 183 Final Presentation
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Transcript of EGR 183 Final Presentation
EGR 183: Modeling Neural Networks in silicoDr. Needham - Fall 2007
Daniel Calrin B.S.E. Daniel Cook
Joshua Mendoza-Elias
Background: Neurons
Background: LTP and LTDThe mechanisms of Long-term Potentiation
Background continued:The mechanisms of Long-term Depression
Short-term and Long-term Effects
The Basis of Hebbian Learning
Foundation for our Computer Model
Types of NeuronTypes of Neuron
1
2
3
4
Inhibitory NeuronsInhibitory Neurons
Input NeuronInput Neuron
Output NeuronOutput Neuron
α = -1 4
Neuron is voltage-clamped,
presynaptic to all neurons in model
Inhibitory neurons depress post-
synaptic neurons
Excitatory NeuronsExcitatory NeuronsAverage firing rates solved at each time
step
Learning rule determines change in synaptic strength
inhibitory synapseexcitatory synapse
Key:
Synaptic strengths
vvii
1.0 1.0
t=t0 t=t0+1
+αvvjj
In phase
vvii vvjj
Out of phase
+α
Two neurons are firing full-speed:
Strengths increase by factor of alpha
vvii
1.0 0.0
-βvvjj vvii vvjj
One neuron vi is firing but vj
is not:Strengths decrease by factor
of beta
-β
Inhibitory Neurons
t=t0 t=t0+1
Excitatory
vvii vvjj vvjj
Inhibitory
vvii
= ...+ wi,jvi
+...
= ...- wi,jvi +...
An inhibitory neuron vi is firing, depressing the post-synaptic neuron
Weighted vi is summed negatively into vj
Weighted vi is summed positively into vj
vvii vvjj vvii vvjj
An excitatory neuron vi is firing, potentiating the post-synaptic
neuron
• In-phase & out-of-phase components, but we could not teach the model complete phasic inversion
• Need further development to do this: one-way connections (i.e. some strengths are 0)
Results
Phase components• Learning rule: α = | v1 - vN |
Input NeuronInput Neuron
Output NeuronOutput Neuron
ConvergenceConvergence
In phasecomponentIn phase
componentOut of phasecomponent
Out of phasecomponent
time
firi
ng
rate
Output NeuronOutput Neuron
Maxima & Minima
Local max near minimum
Local min near maximum
Maximumat maximum
time
firi
ng
rate
Input NeuronInput Neuron
Further developmentsShort-term
• Sparse synapse matrix (i.e. some synapses are strength 0)
• Asynchronous firing
• Multi-dimensional training (i.e. for character recognition, sound recognition, etc.)
Long-term
• Ca+2 Modeling
• Gene Expression Profile (DNA microarray data to reflect changes in synaptic efficacy)
Biological parallel with In Silico
Starvoytov et al. 2005Light-drected stimulation of neurons on silicon wafers. J Neurophysiol 93: 1090-1098.
LDS in concert with Computer Simulation
•MEAs vs. LDS •More real-time data•More quickly•Scans: Works on variably connected neural networks