Lex Fridman
lex.mit.edu
January
2018
MIT 6.S094: Deep Learning for Self-Driving Cars
https://selfdrivingcars.mit.edu
For the full updated list of references visit:
https://selfdrivingcars.mit.edu/references
Neuron: Biological Inspiration for Computation
• Neuron: computational building
block for the brain
[18, 143]
• (Artificial) Neuron: computational
building block for the “neural network”
Differences (among others):
• Parameters: Human brains have ~10,000,000
times synapses than artificial neural networks.
• Topology: Human brains have no “layers”.
Topology is complicated.
• Async: The human brain works
asynchronously, ANNs work synchronously.
• Learning algorithm: ANNs use gradient
descent for learning. Human brains use … (we
don’t know)
• Processing speed: Single biological neurons
are slow, while standard neurons in ANNs are
fast.
• Power consumption: Biological neural
networks use very little power compared to
artificial networks
• Stages: Biological networks usually don't stop
/ start learning. ANNs have different fitting
(train) and prediction (evaluate) phases.
Similarity (among others):
• Distributed computation on a large scale.
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