1. The Perceptron

2. 0T Afferents V thr V rest t max 0 What does a neuron do? spike no spike

3. Afferents 0T t max - V thr Null We consider a simplified case: input is synchronous

4. Afferents Alternatively, input is constant

5. The perceptron

6. Geometrical interpretation

7. The perceptron The Perceptron categorizes the space of inputs into inputs that should evoke a response and inputs that should not evoke a response

8. Constraints on possible categorizations

12. Solution: change of coordination

13. More complicated rules can be realized if an additional non-linear layer is added

15. Deerinck 2002

17. Llinas 1975 Ramon Y Cajal

18. Llinas 1975 Increasing network capacity?

19. The perceptron Learning algorithm

20. Algorithm starts with an arbitrary set of weights Examples are presented one by one If the Perceptron correctly classifies the example  no change in synaptic weights If the Perceptron does not correctly classify the example then make a Hebbian change in weights:

21. The perceptron Learning algorithm If the example is to be classifies as ‘1’: If the example is to be classifies as ‘0’:

22. Perceptron.m

23. Hebbian plasticity and unsupervised learning

24. Unsupervised learning in linear neurons

25. Hebbian plasticity W i (n+1) = efficacy of synapse i after n updates  is the plasticity rate

26. Geometrical interpretation