Principles of Sensory Neuroscience

1. Principles of Sensory Neuroscience Systems Biology Doctoral Training Program Physiology course Prof. Jan Schnupp jan.schnupp@dpag.ox.ac.uk HowYourBrainWorks.net

2. Transduction Fig 2 of http://www.masseyeandear.org/research/ent/ent-investigators/eatock/

3. Labelled Line Codes Already before the specific tuning properties of sensory receptors could be demonstrated, Rene Descartes hypothesized that sensory afferents carry modality specific information to the brain. Microstimulation of single cutaneous afferents in human volunteers links specific nerve fibres to specific sensations (e.g. RA-II fibre activation causes the sensation of “flutter”)

4. Place Codes • Ramon y Cajal speculated in the 1930s that the optic chiasm may have evolved to allow an uninterrupted topographic representation of the visual scene on the surface the optic tectum. (A, B) • We now know that topographic maps in the tectum are discontinuous. (C). • The notion that topographic maps contribute to neural representations remains widespread.

5. The Receptive Field Concept Fig 22.3 of Kandel et al “Principles of Neural Science”

6. A Better Receptive Field Concept… … describes the behaviour of a sensory neuron quantitatively in terms of a “transfer function” y=f(x) which maps a mathematical description of the stimulus x (location, intensity, frequency, colour, temperature, recent history …) onto a measure of the neuron’s “output” y (depolarization, firing probability, response latency).

7. Rate Codes • Classic experiments performed by Adrian in the 1920s on frog muscle stretch receptors established that sensory afferents use changes in spike rateto signal the intensity of a stimulus. • Adrian also found that many sensory neurons “adapt”, i.e. they do not maintain very high firing rates for long if stimuli are held constant.

8. Quantifying Rate Codes: The Post Stimulus Time Histogram Source: http://www.frontiersin.org/Journal/10.3389/fnsyn.2010.00017/full

9. Eye and Retina

10. Centre –Surround Receptive Fields Photo- receptors Horizontal Cell Bipolar Cell RetinalGanglion Cell

11. Difference of Gaussians Model of Retinal Ganglion Cells • The centre-surround structure of Retinal Ganglion Cells turns them into “spatial frequency filters”. Larger RGC receptive fields are tuned to “coarsely grained” structure in the visual scene, while smaller RFs are tuned to fine grain structure.

12. Convolving a Penny with DoGs • The picture of an American cent (left) seen through large (middle) or small (right) difference of Gaussian receptive fields.

13. Seeing Lines

14. The Gabor Filter Model of V1 Simple Cells Retina->LGN->V1 simple cell: linear

15. Linear Filters in Visual Cortex? Movshon, Tolhurst and Thompson 1978 • The “F0/F1” ratio is often used to distinguish simple (approximately linear) V1neurons from complex (nonlinear) ones. • Responses are recorded to sinusoidal contrast gratings. If the cell is linear, the output should contain only the input frequency F0. • Fourier analysis is performed on the post stimulus time histogram to measure the amplitude ratio of the fundamental (1st harmonic, F1) to the “zero frequency” (i.e. sustained, “DC” response) F0. • Some complex cells have “on” and “off” responses which manifest themselves as F2=2·F1 components - a “quadratic” (... +c·sin(x)2 +...) non-linearity.

16. Pennies as seen by V1 simple cells • American cent coin (original to the left) convolved with “Gabor” simple cell receptive field models shown above.

17. The Ear Organ of Corti Cochlea “unrolled” and sectioned

18. “Gammatone Filter Bank”

19. Auditory Nerve Fibers behave like Rectified Gammatone Filters Auditory Neuroscience Fig 2.12 Based on data collected by Goblick and Pfeiffer (JASA 1969)

20. The Auditory Pathway CN, cochlear nuclei; SOC, superior olivary complex; NLL, nuclei of the lateral lemniscus; IC, inferior colliculus; MGB, medial geniculate body.

21. Linear Neural Filters In Auditory Cortex? From work by Shihab Shamma and colleagues

22. Measuring Frequency-Time (Spectro-Temporal) Receptive Fields with Reverse Correlation Freq. channel time

23. Binaural Frequency-Time Receptive Field

24. Linear Prediction of Responses FTRF “w matrix” Input“i vector” r(t) = i1(t-1) w1(1) + i1(t-2) w1(2)+ ...+ i2(t-1) w2(1) + i2(t-2) w2(2)+ ... + i3(t-1) w3(1) + i2(t-2) w3(2)+ ... Frequency [kHz] Latency

25. Predicting Space from Spectrum a Left and Right Ear Frequency-Time Response Fields Virtual Acoustic Space Stimuli d Frequency [kHz] Elev [deg] b e c f Schnupp et al Nature 2001

26. Are Neurons “Noisy” Rate Coders? Or Precision Spike Timers? Mainen & Sejnowski, Science 1995

27. What about Spike Latency Codes? • Many nervous in the central auditory system seem to fire only short bursts of action potentials at the onset of a stimulus. • For such neurons, the response latency may vary as a function of certain stimulus parameters (e.g. intensity, sound source position … ) and could therefore encode that parameter. • Nelken et al, “Encoding stimulus information by spike numbers and mean response time in primary auditory cortex” J Comput Neurosci (2005) Sound source azimuth ()

28. How about Spike Interval Codes? The discharges of cochlear nerve fibres to low frequency sounds are not random; they occur at particular times (phase locking). The spike time intervals therefore encode temporal features of the stimulus (sound periodicity). Evans (1975)

29. Who reads the neural code, and how do they do it?