17-08-2012, 03:12 PM
Artificial Neural Networks
Artificial Neural Networks.ppt (Size: 399 KB / Downloads: 68)
Biological inspiration
Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours.
An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems.
The nervous system is build by relatively simple units, the neurons, so copying their behavior and functionality should be the solution.
Artificial neurons
The McCullogh-Pitts model:
spikes are interpreted as spike rates;
synaptic strength are translated as synaptic weights;
excitation means positive product between the incoming spike rate and the corresponding synaptic weight;
inhibition means negative product between the incoming spike rate and the corresponding synaptic weight;
Learning in biological systems
The young animal learns that the green fruits are sour, while the yellowish/reddish ones are sweet. The learning happens by adapting the fruit picking behavior.
At the neural level the learning happens by changing of the synaptic strengths, eliminating some synapses, and building new ones.
Learning in biological neural networks
The learning rules of Hebb:
synchronous activation increases the synaptic strength;
asynchronous activation decreases the synaptic strength.
These rules fit with energy minimization principles.
Maintaining synaptic strength needs energy, it should be maintained at those places where it is needed, and it shouldn’t be maintained at places where it’s not needed.
Artificial Neural Networks.ppt (Size: 399 KB / Downloads: 68)
Biological inspiration
Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours.
An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems.
The nervous system is build by relatively simple units, the neurons, so copying their behavior and functionality should be the solution.
Artificial neurons
The McCullogh-Pitts model:
spikes are interpreted as spike rates;
synaptic strength are translated as synaptic weights;
excitation means positive product between the incoming spike rate and the corresponding synaptic weight;
inhibition means negative product between the incoming spike rate and the corresponding synaptic weight;
Learning in biological systems
The young animal learns that the green fruits are sour, while the yellowish/reddish ones are sweet. The learning happens by adapting the fruit picking behavior.
At the neural level the learning happens by changing of the synaptic strengths, eliminating some synapses, and building new ones.
Learning in biological neural networks
The learning rules of Hebb:
synchronous activation increases the synaptic strength;
asynchronous activation decreases the synaptic strength.
These rules fit with energy minimization principles.
Maintaining synaptic strength needs energy, it should be maintained at those places where it is needed, and it shouldn’t be maintained at places where it’s not needed.