Download Cellular Neuroscience

Principles of Sensory
Neuroscience
Systems Biology Doctoral Training Program
Physiology course
Prof. Jan Schnupp
[email protected]
HowYourBrainWorks.net
Transduction
Fig 2 of
http://www.masseyeandear.org/
research/ent/ent-investigators/eatock/
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”)
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.
The Receptive Field Concept
Fig 22.3 of Kandel et al “Principles of Neural Science”
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).
Rate (Hz)
Rate
Codes
150
100
50
0
0
1
2
Weight (g)
3
• Classic experiments performed by Adrian in the 1920s on frog muscle
stretch receptors established that sensory afferents use changes in
spike rate to 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.
Quantifying Rate Codes:
The Post Stimulus Time Histogram
Source: http://www.frontiersin.org/Journal/10.3389/fnsyn.2010.00017/full
Eye and Retina
Centre –Surround Receptive Fields
Photoreceptors
++ + +
-- - -
Horizontal
Cell
-
Bipolar
Cell
Retinal
Ganglion
Cell
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.
Convolving a Penny with
DoGs
• The picture of an American cent (left)
seen through large (middle) or small
(right) difference of Gaussian receptive
fields.
Seeing Lines
The Gabor Filter Model of V1 Simple Cells
LGN
- + - + -+
-
Cortex
+ - + - + - +
Retina->LGN->V1 simple cell: linear
Linear Filters in
Visual Cortex?
Movshon, Tolhurst and Thompson 1978
• The “F0/F1” ratio is often used to distinguish simple (approximately linear) V1
neurons 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.
Pennies as seen by V1 simple cells
• American cent coin (original to the left)
convolved with “Gabor” simple cell receptive
field models shown above.
The Ear
Organ of Corti
Cochlea “unrolled” and sectioned
“Gammatone Filter Bank”
Auditory Nerve Fibers behave like Rectified
Gammatone Filters
Auditory Neuroscience Fig 2.12
Based on data collected by Goblick and Pfeiffer (JASA
x
Corte
x
Corte
MGB
Brainstem Midbrain
The
Auditory
Pathway
MGB
IC
IC
NLL
CN, cochlear nuclei;
Cochlea
SOC, superior olivary complex;
NLL, nuclei of the lateral lemniscus;
IC, inferior colliculus;
MGB, medial geniculate body.
NLL
SOC
CN
SOC
CN
Cochlea
Linear Neural Filters In
Auditory Cortex?
From work by Shihab Shamma and colleagues
Freq. channel
Measuring Frequency-Time (Spectro-Temporal)
Receptive Fields with Reverse Correlation
time
Binaural Frequency-Time
Receptive Field
Linear
Prediction
of Responses
Input
“i vector”
16
Frequency [kHz]
4
1
16
4
1
Latency
response
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)+ ...
FTRF
“w matrix”
1
0.5
0
200 ms
100
0
-5 0 5 10
dB
Predicting Space from
Spectrum
Left and Right Ear
Frequency-Time Response
Fields
a
Virtual Acoustic
Space Stimuli
16
Frequency [kHz]
4
1
d
Elev[deg]
[deg]
Elev
4
1
c
Schnupp et al Nature 2001
-5 0 5 10
dB
1
0
-60
-180 -120 -60
100
0
e
0.5
0
200 ms
60
rate (Hz)
response
b
C81
16
0
f
60 120 180
Azim [deg]
200
0
0
100
ms 200
Are Neurons “Noisy” Rate Coders?
Or Precision Spike Timers?
Mainen & Sejnowski, Science 1995
•
•
•
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 ()
What about
Spike Latency
Codes?
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)
Who reads the
neural code, and
how do they do it?