TNI: Computational Neuroscience Instructors:Peter Latham Maneesh Sahani Peter Dayan

TNI: Computational Neuroscience

Instructors: Peter LathamManeesh SahaniPeter Dayan

TAs: Arthur Guez, [email protected] Pachitariu, [email protected]

Website: http://www.gatsby.ucl.ac.uk/~aguez/tn1/

Lectures: Tuesday/Friday, 11:00-1:00.Review: Tuesday, starting at 4:30.

Homework: Assigned Friday, due Friday (1 week later).first homework: assigned Oct. 7, due Oct. 14.

What is computational neuroscience?

Our goal: figure out how the brain works.

10 microns

There are about 10 billion cubes ofthis size in your brain!

How do we go about making sense of this mess?

David Marr (1945-1980) proposed three levels of analysis:

1. the problem (computational level) 2. the strategy (algorithmic level) 3. how it’s actually done by networks of neurons (implementational level)

Example #1: memory.

the problem:recall events, typically based on partial information.

Example #1: memory.

the problem:recall events, typically based on partial information.associative or content-addressable memory.

an algorithm:dynamical systems with fixed points.

r3

r2

r1 activity space

Example #1: memory.

the problem:recall events, typically based on partial information.associative or content-addressable memory.

an algorithm:dynamical systems with fixed points.

neural implementation:Hopfield networks.

xi = sign(∑j Jij xj)

Example #2: vision.

the problem (Marr):2-D image on retina → 3-D reconstruction of a visual scene.

Example #2: vision.

the problem (modern version):2-D image on retina → recover the latent variables.

housesuntreebad artist

Example #2: vision.

the problem (modern version):2-D image on retina → recover the latent variables.

housesuntreebad artistcloud

Example #2: vision.

the problem (modern version):2-D image on retina → reconstruction of latent variables.

an algorithm:graphical models.

x1 x2 x3

r1 r2 r3 r4

latent variables

low level representation

Example #2: vision.



x1 x2 x3

r1 r2 r3 r4

latent variables

low level representation

inference

Example #2: vision.



implementation in networks of neurons:no clue.

Comment #1:

the problem:the algorithm:neural implementation:

Comment #1:

the problem: easierthe algorithm: harderneural implementation: harder

often ignored!!!

Comment #1:


A common approach:

Experimental observation → model

Usually very underconstrained!!!!

Comment #1:


Example i: CPGs (central pattern generators)

rate

rate

Too easy!!!

Comment #1:


Example ii: single cell modeling

C dV/dt = -gL(V – VL) – n4(V – VK) …

dn/dt = …

…

lots and lots of parameters … which ones should you use?

Comment #1:


Example iii: network modeling

lots and lots of parameters × thousands

Comment #2:


You need to know a lot of math!!!!! r3

r2

r1 activity space

x1 x2 x3

r1 r2 r3 r4

Comment #3:


This is a good goal, but it’s hard to do in practice.

Our actual bread and butter: 1. Explaining observations (mathematically) 2. Using sophisticated analysis to design simple experiments that test hypotheses.

Comment #3:

Two experiments:

- record, using loose patch, from a bunch of cells in culture- block synaptic transmission- record again

- found quantitative support for the balanced regime.

J. Neurophys., 83:808-827, 828-835, 2000

Comment #3:

Two experiments:

- perform whole cell recordings in vivo- stimulate cells with a current pulse every couple hundred ms- build current-triggered PSTH

- showed that the brain is intrinsically very noisy, and is likely to be using a rate code.

Nature, 466:123-127 (2010)

Comment #4:


some algorithms are easy to implement on a computerbut hard in a brain, and vice-versa.

these are linked!!!

Comment #4:

hard for a brain, easy for a computer:

A-1

z=x+y∫dx ...

easy for a brain, hard for a computer:

associative memory

Comment #4:


some algorithms are easy to implement on a computerbut hard in a brain, and vice-versa.

we should be looking for the vice-versa ones.

it can be hard to tell which is which.

these are linked!!!

Basic facts about the brain

Your brain

Your cortex unfolded

~30 cm

~0.5 cm

neocortex (cognition)

subcortical structures(emotions, reward,homeostasis, much muchmore)

6 layers

Your cortex unfolded

1 cubic millimeter,~3*10-5 oz

1 mm3 of cortex:

50,000 neurons10000 connections/neuron(=> 500 million connections)4 km of axons

1 mm3 of cortex:


1 mm2 of a CPU:

1 million transistors2 connections/transistor(=> 2 million connections).002 km of wire

1 mm3 of cortex:


whole brain (2 kg):

1011 neurons1015 connections8 million km of axons

1 mm2 of a CPU:


whole CPU:

109 transistors2*109 connections2 km of wire

1 mm3 of cortex:


whole brain (2 kg):

1011 neurons1015 connections8 million km of axons

1 mm2 of a CPU:


whole CPU:

109 transistors2*109 connections2 km of wire

volt

age

100 mstime

-50 mV

+20 mV

dendrites (input)

soma (spike generation)

axon (output)

1 ms

current flow

synapse

current flow

synapse

volt

age

100 mstime

-50 mV

+20 mV

neuron jneuron i

neuron j emits a spike:

V o

n n

euro

n i

t

10 ms

EPSP

neuron jneuron i


V o

n n

euro

n i

t

10 ms

IPSP

neuron jneuron i


V o

n n

euro

n i

t

10 ms

IPSP

amplitude = wij

neuron jneuron i


V o

n n

euro

n i

t

10 ms

IPSP

amplitude = wij

changes withlearning

current flow

wij

A bigger picture view of the brain

xr

sensory processing

motor processing

x'

r'

cognitionmemory

action selection

peripheral spikes

latent variables

motor actions

peripheral spikes

brain

r̂ “direct” code forlatent variables

r'̂ “direct” code formotor actions

Who is walking behind the picket fence?

r

r

r

r

r

r

you are thecutest stickfigure ever!

r

you are thecutest stickfigure ever!

xr

sensory processing

motor processing

x'

r'

cognitionmemory

action selection

peripheral spikes

latent variables

motor actions

peripheral spikes

brain



xr

sensory processing

motor processing

x'

r'

cognitionmemory

action selection

peripheral spikes

latent variables

motor actions

peripheral spikes

brain



In some sense, action selection is the most importantproblem:

if we don’t choose the right actions, we don’t reproduce, and all the neural coding and computation in the world isn’t going to help us.

Do I call him and risk rejection and humiliation,or do I play it safe, and stay home on Saturdaynight and eat oreos?

Do I call her and risk rejection and humiliation,or do I play it safe, and stay home on Saturdaynight and eat oreos?

xr

sensory processing

motor processing

x'

r'

cognitionmemory

action selection

peripheral spikes

latent variables

motor actions

peripheral spikes

brain



Problems:1. How does the brain extract latent variables?2. How does it manipulate latent variables?3. How does it learn to do both?

Ask at two levels:1. What are the algorithms?2. How are they implemented in neural hardware?

What do we know about the brain?

Highly

biased

a. Anatomy. We know a lot about what is where. But becareful about labels: neurons in motor cortex sometimes

respond to color.

Connectivity. We know (more or less) which areais connected to which.

The van Essen diagram


respond to color.

Connectivity. We know (more or less) which areais connected to which.

wij


respond to color.

Connectivity. We know (more or less) which areais connected to which. We don’t know the wiring diagramat the microscopic level.

wij


respond to color.

Connectivity. We know (more or less) which areais connected to which. We don’t know the wiring diagramat the microscopic level. But we might in a few decades!

b. Single neurons. We know very well how point neurons work(think Hodgkin Huxley).

Dendrites. Lots of potential for incredibly complexprocessing.

My guess: all they do make neurons bigger and reduce wiring length (see the work of Mitya Chklovskii).

m neurons

n neurons

L

L

total wire length without dendrites: ~nmL

m neurons

n neurons

L

Ltotal length = mL

total length = nL

total wire length without dendrites: ~nmL

total wire length with dendrites: ~(n+m)L

b. Single neurons. We know very well how point neurons work(think Hodgkin Huxley).

Dendrites. Lots of potential for incredibly complexprocessing.

My guess: all they do is make neurons bigger and reduce wiring length (see the work of Mitya Chklovskii).

How much I would bet that that’s true: 20 p.

c. The neural code.

My guess: once you get away from periphery, it’s mainly firing rate: an inhomogeneous Poisson process with a refractory period is a good model of spike trains.

How much I would bet: £100.

The role of correlations. Still unknown.

My guess: don’t have one.

d. Recurrent networks of spiking neurons. This is a field thatis advancing rapidly! There were two absolutely seminalpapers about a decade ago:

van Vreeswijk and Sompolinsky (Science, 1996)van Vreeswijk and Sompolinsky (Neural Comp., 1998)

We now understand very well randomly connected networks(harder than you might think), and (I believe) we are onthe verge of:

i) understanding networks that have interesting computational properties.ii) computing the correlational structure in those networks.

e. Learning. We know a lot of facts (LTP, LTD, STDP).

• it’s not clear which, if any, are relevant. • the relationship between learning rules and computation is essentially unknown.

Theorists are starting to develop unsupervised learning algorithms, mainly ones that maximize mutual information. These are promising, but the link to the brain has not been fully established.

e. Learning. We know a lot of facts (LTP, LTD, STDP).

• it’s not clear which, if any, are relevant. • the relationship between learning rules and computation is essentially unknown.

Theorists are starting to develop unsupervised learning algorithms, mainly ones that maximize mutual information. These are promising, but the link to the brain has not been fully established.

What is unsupervised learning?

Learning structure from data without any help from anybody.

Example: most visual scenes are very unlikely to occur.

1000 × 1000 pixels => million dimensional space.

space of possible pictures is much smaller, and forms a very complicated manifold:

possible visualscenes






visual scenes






visual scenes


Learning from spikes:

neurons 1

neu

ron

2


Learning from spikes:

neurons 1

neu

ron

2dog

cat



Which is real and which is a painting?

A word about learning (remember these numbers!!!):

You have about 1015 synapses.

If it takes 1 bit of information to set a synapse,you need 1015 bits to set all of them.

30 years ≈ 109 seconds.

To set 1/10 of your synapses in 30 years,

you must absorb 100,000 bits/second.

Learning in the brain is almost completely unsupervised!!!

stolen from Geoff Hinton

f. Where we know algorithms we know the neuralimplementation (sort of):

vestibular system, sound localization, echolocation, addition

This is not a coincidence!!!!

Remember David Marr:

1. the problem (computational level) 2. the strategy (algorithmic level) 3. how it’s actually done by networks of neurons (implementational level)

What we know: my score (1-10).

a. Anatomy. 5b. Single neurons. 6c. The neural code. 6d. Recurrent networks of spiking neurons. 3e. Learning. 2

The hard problems:1. How does the brain extract latent variables? 1.0012. How does it manipulate latent variables? 1.0023. How does it learn to do both? 1.001

Outline:

1. Basics: single neurons/axons/dendrites/synapses. Latham2. Language of neurons: neural coding. Sahani3. Learning at the network and behavioral level. Dayan 4. What we know about networks (very little). Latham

Outline for this part of the course (biophysics):

1. What makes a neuron spike.2. How current propagates in dendrites.3. How current propagates in axons.4. How synapses work.5. Lots and lots of math!!!

Documents

TNI: Computational Neuroscience Instructors:Peter Latham Maneesh Sahani Peter Dayan