optic nerve

1. optic nerve

2. 1 deg Striate Cortex (V1) Hubel & Wiesel

3. 1 deg Striate Cortex (V1) Hubel & Wiesel

4. S1 S2 S3 Spikes from an LGN Neuron: 62 Repeats of each stimulus trial # 1 . . . . 62 time Firing Rate (Hz) Butts et al. 2010

5. -25º 0º +25º 80 60 response (spikes/s) 40 20 0 orientation Sclar & Freeman 1982

8. Odorant Receptors amines lactones acids sulfur terpenes aldehydes ketones aromatics alcohols esters Hallem & Carlson 2006

9. 1 deg IT face cell Striate Cortex (V1) Tsao et al. 2006

10. x x x x x x x x x x x x + + + Striate Cortex LGN X = excitation = inhibition Hubel & Wiesel 1962

11. 80% contrast 40% contrast 80 R1 0 -25º 0º +25º response (spikes/s) orientation Sclar & Freeman 1982

12. 1.0 attend in attend out 0.5 0.0 -90º -60º -30º 0º 30º 60º 90º V4 response orientation McAdams & Maunsell 1999

13. adapting direction 100 spikes/s 50 0 0 180 -180 direction of motion Kohn & Movshon 2004

14. waterfall illusion

15. Late: >150 ms 140 spikes/s Early: 65 to 85 ms (2 or 3 spikes) 140 spikes/s  = 45°  = 90°  = 135° Pack & Born 2001

16. Lorençeau et al. 1993

17. sp/sec Spikes from an MT Neuron: Identical Stimulus, 210 Repeats trial # time (ms) Shadlen & Newsome 1994

18. Outline: neural coding lecture, pt 2 Population coding: a case study Problems in understanding decoding A cheat sheet for your homework assignment

19. Population coding: a case study the cricket wind direction sensing system (first-order neurons) Bacon & Murphey J. Physiol. 1984 352:601-623

20. Population coding: a case study the cricket wind direction sensing system (second-order neurons) First-order neuron projections to the terminal ganglion are organized according to preferred wind direction. There are four second-order neurons, and their dendrites are organized along the same divisions. Bacon & Murphey J. Physiol. 1984 352:601-623 see http://www.biol.sc.edu/~vogt/courses/neuro/neurolabs.html

21. cell 1 cell 2 cell 3 cell 4 r / rmax v wind direction (degrees) Population coding: a case study P. Dayan & L.F. Abbott Theoretical Neuroscience MIT Press

22. v Population coding: a case study P. Dayan & L.F. Abbott Theoretical Neuroscience MIT Press

23. Outline: neural coding lecture, pt 2 Population coding: a case study Problems in understanding decoding A cheat sheet for your homework assignment

24. Problems in understanding decoding Which spike trains are being decoded to produce a percept? Some criteria: Stimuli that produce different percepts should produce discernable changes in the spiking of the candidate neurons. Differences in the spiking of candidate neurons should be sufficiently reliable to account for the acuity of the percept. Noise in the activity of the candidate neurons should predict noise in the percept. Artificially stimulating the candidate neurons should affect the percept. Silencing or removing the candidate neurons should affect the percept. adapted from Parker & Newsome, Annu. Rev. Neurosci. 1998. 21:227–77.

25. Problems in understanding decoding Is information encoded in spike timing or spike rate? In principle, either spike timing or spike rate can carry information about a stimulus. adapted from Gollisch & Meister Science 2008 319:1108-11

26. Problems in understanding decoding How much of a spike train should we consider? Behavioral performance can help tell us what portion of a spike train we should consider. Cury & Uchida Neuron 2010 68:570-585

27. Problems in understanding decoding Is the optimal decoding algorithm always used by the organism? rapidly adapting slowly adapting psychophysical rapidly adapting type 2 rapidly adapting type 1 The “lower envelope model”: Sensory thresholds are specified by the neuron that has the lowest threshold for stimulus in question. Johansson & Vallbo, J. Physiol. 1979 297:405-422

28. Problems in understanding decoding Is the optimal decoding algorithm always used by the organism? rapidly adapting slowly adapting … but single neurons can exhibit better acuity than the organism as a whole! Johansson & Vallbo, J. Physiol. 1979 297:405-422

29. Problems in understanding decoding Does each neuron provide independent information to the decoder? The “pooling model”: Sensory thresholds can be improved by pooling independent information from many neurons.

30. Problems in understanding decoding Does each neuron provide independent information to the decoder?

31. Problems in understanding decoding Does each neuron provide independent information to the decoder? There is lots of evidence that activity in nearby neurons is often not independent.

32. Outline: neural coding lecture, pt 2 Population coding: a case study Problems in understanding decoding A cheat sheet for your homework assignment

33. Principal component analysis: a method for reducing the dimensionality of a data set by defining a reduced set of axes which account for much of the variance in the data. principal component 1 accounts for a large part of the variance (“body size”) principal component 2 accounts for a smaller part of the variance

34. Linear discriminant analysis: a method for classifying samples within a data set based on drawing a linear boundary (a line or plane) which best separates different categories of samples. discriminant