2. optic nerve

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

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

5. Butts et al. 2010 Spikes from an LGN Neuron: 62 Repeats of each stimulus S1S1 S2S2 S3S3 Firing Rate (Hz) time trial # 1.. 62

6. Sclar & Freeman 1982 response (spikes/s) orientation 80 0 -25º+25º0º0º 40 20 60

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

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

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

12. R1R1 Sclar & Freeman 1982 response (spikes/s) orientation 80% contrast 40% contrast 80 0 -25º+25º0º0º

13. McAdams & Maunsell 1999 attend in attend out 1.0 0.5 0.0 -90º-60º-30º0º0º30º60º90º V4 response orientation

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

15. waterfall illusion

16. 140 spikes/s Early: 65 to 85 ms (2 or 3 spikes)  = 45°  = 90°  = 135° Late: >150 ms 140 spikes/s Pack & Born 2001

17. 140 spikes/s Early: 65 to 85 ms  = 45°  = 90°  = 135° Late: >150 ms 140 spikes/s Pack & Born 2001

18. Lorençeau et al. 1993

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

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

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

22. see http://www.biol.sc.edu/~vogt/courses/neuro/neurolabs.html the cricket wind direction sensing system (second-order neurons) Population coding: a case study 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.

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

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

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

26. Problems in understanding decoding Which spike trains are being decoded to produce a percept? 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. Some criteria: adapted from Parker & Newsome, Annu. Rev. Neurosci. 1998. 21:227–77.

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

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

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

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

31. 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.

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

33. 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.

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

35. principal component 1 accounts for a large part of the variance (“body size”) 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 2 accounts for a smaller part of the variance

36. discriminant 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.