CSE/NB 528 Final Lecture: All Good Things Must…CSE/NB 528: Final Lecture 29 Further Reading (for...
Transcript of CSE/NB 528 Final Lecture: All Good Things Must…CSE/NB 528: Final Lecture 29 Further Reading (for...
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1CSE/NB 528: Final Lecture
CSE/NB 528Final Lecture: All Good Things Must…
Lecture figures are from Dayan & Abbott’s bookhttp://people.brandeis.edu/~abbott/book/index.html
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2CSE/NB 528: Final Lecture
Course Summary (All good things must…come to an end)
Where have we been?Course Highlights
Where do we go from here?Challenges and Open Problems
Further Reading
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3CSE/NB 528: Final Lecture
What is the neural code?
14121086Time (s)
Cell J
Cell I
Cell H
Cell G
Cell F
Cell E
Cell D
Cell C
Cell B
Cell A
What is the nature of the code?How should we represent the spiking output?
single cells vs populationsrates vs spike times vs intervals
What features of the stimulus does the neural system represent?
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4CSE/NB 528: Final Lecture
Encoding and decoding neural information
Encoding: building functional models of neurons/neural systems and predicting the spiking output given the stimulus
Decoding: what can we say about the stimulus given what weobserve from the neuron or neural population?
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5CSE/NB 528: Final Lecture
Highlights: Neural Encoding
spike-triggering stimulus features
stimulus X(t)
x1
x2
x3
f1
f2
f3
multidimensionaldecision function
spiking output ρ(t)
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6CSE/NB 528: Final Lecture
STA
Gaussian priorstimulus distribution
Spike-conditional distribution
covariance
Highlights: Finding the feature space of a neural system
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7CSE/NB 528: Final Lecture
p(r|+)p(r|-)
<r>+<r>-
z
Decoding corresponds to comparing test to threshold.α(z) = P[ r ≥ z|-] false alarm rate, “size”β(z) = P[ r ≥ z|+] hit rate, “power”
Decoding: Signal detection theory
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8CSE/NB 528: Final Lecture
Close correspondence between neural performanceand behaviour
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9CSE/NB 528: Final Lecture Theunissen & Miller, 1991
RMS error in estimate
Decoding from a populatione.g. cosine tuning curves
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10CSE/NB 528: Final Lecture
MAP: s* which maximizes p[s|r]
ML: s* which maximizes p[r|s]
Difference is the role of the prior: differ by factor p[s]/p[r]
For cercal data:
More general approaches: MAP and ML
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11CSE/NB 528: Final Lecture
Highlights:Information maximizationas a design principle of the nervous system
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12CSE/NB 528: Final Lecture
The biophysical basis of neural computation
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13CSE/NB 528: Final Lecture
Excitability is due to the properties of ion channels
• Voltage dependent• transmitter dependent (synaptic)• Ca dependent
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14CSE/NB 528: Final Lecture
-
Highlights: The neural equivalent circuit
Ohm’s law: and Kirchhoff’s law
Capacitive current
Ionic currents Externally applied current
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15CSE/NB 528: Final Lecture
Simplified neural models
A sequence of neural models of increasing complexitythat approach the behavior of real neurons
Integrate and fire neuron: subthreshold, like a passive membranespiking is due to an imposed threshold at VT
Spike response model: subthreshold, arbitrary kernelspiking is due to an imposed threshold at VTpostspike, incorporates afterhyperpolarization
Simple model:complete 2D dynamical systemspiking threshold is intrinsichave to include a reset potential
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16CSE/NB 528: Final Lecture
Simplified Models: Integrate-and-Fire
V
meLm RIEVdtdV
+−−= )(τ
If V > Vthreshold SpikeThen reset: V = Vreset
Integrate-and-Fire Model
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17CSE/NB 528: Final Lecture
Simplified Models: Spike response model
Keat, Reinagel and Meister
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18CSE/NB 528: Final Lecture
Highlights: Dendritic computation
FilteringShuntingDelay linesInformation segregationSynaptic scalingDirection selectivity
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19CSE/NB 528: Final Lecture
Highlights: Compartmental Models
Coupling conductances
Neuronal structure can be modeled using electrically coupled compartments
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20CSE/NB 528: Final Lecture
Connecting neurons: Synapses
Presynaptic voltage spikes causeneurotransmitter to cross the cleft,triggering postsynaptic receptorsallowing ions to flow in, changing postsynaptic potential
Glutamate: excitatory
GABAA: inhibitory
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21CSE/NB 528: Final Lecture
Synaptic voltage changes
Size of the PSP is a measure of synaptic strength.Can vary on the short term due to input history
on the long term due to synaptic plasticity.. one way to build circuits that learn
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22CSE/NB 528: Final Lecture
Modeling Networks of Neurons
)MW( vuvv++−= F
dtdτ
Output Decay Input Feedback
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23CSE/NB 528: Final Lecture
Highlights: Unsupervised Learning
For linear neuron:
Basic Hebb Rule:
Average effect over many inputs:
Q is the input correlation matrix:
vdtd
w uw=τ
wuuw TTv ==
wuw Qvdtd
w ==τ
TQ uu=
w
Hebb rule performs principal component analysis (PCA)
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24CSE/NB 528: Final Lecture
Highlights: The Connection to Statistics
];|[ Gp vu
];|[ Gp uvCauses v
Data u
Generative model
Recognition model
(posterior)
(data likelihood)
Unsupervised learning = learning the hidden causes of input data
G = (μv, σv)
Causes of clustered
data
“Causes”of natural
images
Use EM algorithm for
learning
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25CSE/NB 528: Final Lecture
Highlights: Generative Models
Droning lecture Mathematical derivations
Lack of sleep
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26CSE/NB 528: Final Lecture
Highlights: Supervised Learning
))(( mk
kjk
jij
mi uwgWgv ∑∑=
2
,)(
21),( m
iim
mijkij vdwWE −= ∑
mku
mjx
Goal: Find W and w that minimize errors:
Backpropagation for Multilayered Networks
jk
mj
mj
jkjk
jkjk
ijijij
wx
xEw
wEww
WEWW
∂
∂⋅
∂∂
−=∂∂
−→
∂∂
−→
εε
ε
Desired output
Gradient descent learning rules:
(Delta rule)
(Chain rule)
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27CSE/NB 528: Final Lecture
Highlights: Reinforcement Learning
Learning to predict rewards:
Learning to predict delayed rewards (TD learning):
Actor-Critic Learning:Critic learns value of each state using TD learningActor learns best actions based on value of next state (using the TD error)
(http://employees.csbsju.edu/tcreed/pb/pdoganim.html)
uvrww )( −+→ ε
)( )]()1()([ )()( τεττ −−+++→ tutvtvtrww
2.5 1
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28CSE/NB 528: Final Lecture
The Future: Challenges and Open Problems
How do neurons encode information?Topics: Synchrony, Spike-timing based learning, Dynamic synapses
Does a neuron’s structure confer computational advantages?
Topics: Role of channel dynamics, dendrites, plasticity in channels and their density
How do networks implement computational principlessuch as efficient coding and Bayesian inference?
How do networks learn “optimal” representations of their environment and engage in purposeful behavior?
Topics: Unsupervised/reinforcement/imitation learning
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29CSE/NB 528: Final Lecture
Further Reading (for the summer and beyond)
Spikes: Exploring the Neural Code, F. Riekeet al., MIT Press, 1997 The Biophysics of Computation, C. Koch, Oxford University Press, 1999Large-Scale Neuronal Theories of the Brain, C. Koch and J. L. Davis, MIT Press, 1994Probabilistic Models of the Brain, R. Rao et al., MIT Press, 2002Bayesian Brain, K. Doya et al., MIT Press, 2007Reinforcement Learning: An Introduction, R. Sutton and A. Barto, MIT Press, 1998
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30CSE/NB 528: Final Lecture
Next meeting: Project presentations!
Project presentations will be on Monday, June 8 (10:30am-12:20pm) in the same classroomKeep your presentation short: ~6-8 slides, 8 mins/groupSlides:
Email Raj your powerpoint (or other) slides before 8am on Monday, June 8 to use the class laptop
ORBring your own laptop if you want to show videos etc.
Projects reports (10-15 pages) due June 10 (by email to both Raj and Adrienne before midnight)
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31CSE/NB 528: Final Lecture
Have a great
summer! Au revoir!NeuralEncoding for Dummies