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What is the Other 85% of V1 Doing?
Olshausen & Field
Problems in Systems Neuroscience, 2004
Brian Potetz
1/26/05
http://www.cnbc.cmu.edu/cns
Only 15% Understood
1.
2.
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4.
5.
Biased sampling of neurons
Biased experimental stimulus
Biased theories (towards simplicity)
Neural interdependence & contextual effects
Ecological deviance
15% ¼ 35%
x
40%
Problem 1: Biased Neural Sampling
• Large cell bodies, stronger action potentials
– Miss 5-10% of cells
• Discarded “visually unresponsive” cells
– Spontaneous, bursting, or tonic cells
– Miss 5-10% of cells
• Bias towards high firing rates
– Miss 50-60% of cells
• Generous estimate: 40% of cells are unsampled.
Sustaining High Firing Rates is Difficult
Energy consumption of:
Cortex
Whole brain
Whole body
• Yet even for natural images, 9Hz recorded averages are typical
Problem 1: Biased Neural Sampling
Lennie, “The Cost of Cortical Computation” (2003)
Spike Counts of Single Neurons Follow
Exponential Distribution for Natural Scenes
Single Neurons (anaesthetised cat V1, ave firing rate = 4Hz)
Population Average:
Problem 1: Biased Neural Sampling
Baddeley et al, “Responses of neurons in primary and
inferior temporal visual cortices to natural scenes” (1997)
So Neurons with 1Hz average
rates may go unnoticed
Problem 1: Biased Neural Sampling
Estimating Percent of Unsampled Cells
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Assume log-normal distribution of average
firing rates (why?)
Assume average average firing rate is 1Hz
Assume measuring threshold is 1Hz
(measured population mean)
Lesson of the Rat Hippocampus
• Via single electrode recording, granule
dentate gyrus cells thought to be mostly
high-rate (theta-cells) interneurons.
• Chronic implants reveal that most are very
low-rate: 0.1Hz is common.
• What are non-geniculate granule cells of
layer 4 doing (30:1)?
Problem 5: Ecological Deviance
Anaesthetized cat response to natural movie, vs linear model prediction (Gray lab)
Another group (David, Vinje & Gallant), with sophisticated nonlinear models learned
over natural stimulus, can capture only 20% of neural response variance, even
correcting for inter-trial variance (using awake monkeys).
Problem 2: Biased Stimuli
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If neurons are so highly nonlinear in the natural
environment, why focus on linear measurement
techniques?
“There is no principled reason for using
sinewaves to study vision”
Authors suggest an alternating approach of
natural stimuli, then attempt to reduce stimuli to
“tease apart” phenomena.
Authors advocate studying V1 further, rather
than assuming current models are correct.
Problem 3: Biased Theories
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Working theories for subsets of data are more
easily published (and remembered) than messy,
unexplained data.
Possible example: V1 as edge-detector
Possible example: simple & complex cells
Problem 4: Interdependence and
Contextual Effects
• By suppressing intracortical signals using electric
simulation, LGN input was estimated causing 35%
of simple cell response variance. (Chung & Ferster, 98)
• By recording optical imaging, local field potential,
and single cell response simultaneously, 80% of
V1 cell response was attributed to ongoing
population activity. (Arieli et al, 96)
Extra-classical receptive fields
The effect of oriented bars outside of the classical RF is
likely to be only one example of many contextual effects.
Problem 4: Interdependence and Contextual Effects
Synchrony
• The fact that EEG and local
field potentials are
measurable suggests that
synchrony takes place in the
cortex
• Worgotter (1996) showed
that LGN and V1 receptive
fields widen as synchrony
increases (as measured by
EEG).
Problem 4: Interdependence and Contextual Effects
Alternative Theories
1. Limitations of prediction for dynamic
systems
2. Sparse, overcomplete representations
3. Contour integration
4. 3D Surface representation
5. Top-down feedback, Bayesian inference
6. Dynamic routing
Conclusions
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Natural stimulus
Multi-unit recordings
Chronic implants
Public database of recordings for others to
model