1. CSE 153Modeling Neurons Chapter 2: Neurons A “typical” neuron… How does such a thing support cognition???

2. CSE 153Modeling Neurons Neurons: detectors? The simplest idea is that a neuron is a “detector” for a pattern. When it is stimulated by its pattern, it fires. The pattern could be thought of as in the “input” (but what is “input” to a neuron?) Or as the output of other neurons - patterns in the patterns…

3. CSE 153Modeling Neurons Neurons as detectors

4. CSE 153Modeling Neurons Neurons: Pandemonium? Oliver G. Selfridge 1958 idea: The mind is made up of “demons”, who shout based on other demons shouting to them. The loudest is heard…

5. CSE 153Modeling Neurons Neurons: Pandemonium? What model does this remind you of???

6. CSE 153Modeling Neurons Neurons: Pandemonium? Each neuron has a simple job, but together... Layers of more and more complicated detectors. “B” is simple! What kind of detectors would be needed for language, face recognition, driving, algebra, problem solving, etc.?

7. CSE 153Modeling Neurons How to simulate this? Neural activity (and learning) can be characterized by mathematical equations. We use these equations to specify the behavior of artificial neurons. The artificial neurons can then be put together to explore behaviors of networks of neurons. Simulation.

8. CSE 153Modeling Neurons Basic Properties of a Neuron It’s a cell: has a cell body, membrane, nucleus, DNA, RNA, proteins, etc. The cell membrane has channels (“holes”), passing ions (salt water). The cell has electrical potential (voltage), integrated in cell body, which activates an action potential output in axon, which releases neurotransmitter. The neurotransmitter activates potential via dendritic synaptic input channels. Excitation and inhibition are transmitted by different neurons!

9. CSE 153Modeling Neurons The Synapse

10. CSE 153Modeling Neurons The Synapse Synaptic efficacy = the amount of activity of the presynaptic (sending) neuron communicated to the postsynaptic (receiving) neuron. Presynaptic: # of vesicles released, NT per vesicle, efficacy of reuptake mechanism. Postsynaptic: # of receptors, alignment & proximity of release site & receptors, efficacy of channels, geometry of dendrite/spine.

11. CSE 153Modeling Neurons Abstract Model neurons 1.Compute weighted, summed net input:  i =  w ij *x j + bias i 2. Pass this through a sigmoidal function to get output: y j =1/(1+e -  i )

12. CSE 153Modeling Neurons “Biologically Plausible” Neural Nets 1. Compute weighted, summed net input (actually averages): η j ≈  a i w ij ≈ g e 2. Compute V m : 3. Compute output as: Spikes Or, rate code via a sigmoidal function: a j =1/[1 + (γ[V m (t) − Θ]+) −1 ]

13. CSE 153Modeling Neurons Neurophysiology The neuron is a miniature electro-chemical system: 1. Balance of electric and diffusion forces. 2. Principal ions. 3. Putting it all together.

14. CSE 153Modeling Neurons Switch to pdf slides