Transcript Unresolved questions in Neuroscience/Complex Systems

Learning with spikes, and the
Unresolved Question in
Neuroscience/Complex Systems
Tony Bell
Helen Wills Neuroscience Institute
University of California at Berkeley
Learning in real neurons:
Long-term potentiation and depression (LTP/LTD)
Bliss & Lomo 1973 discovered associative and input specific
(Hebbian) changes in sizes of EPSC’s: a potential
memory mechanism (the memory trace). Found first in
hippocampus: known to be implicated in learning and
memory. LTP from high-frequency presynaptic stimulation,
or low-frequency presynaptic stimulation and postsynaptic
depolarisation. LTD from prolonged low-frequency stimulation.
Levy & Steward (1983) played with timing of weak and strong
input from entorhinal cortex to hippocampus, finding LTD when
weak after strong, LTP when strong up to 20ms after weak or
simultaneous.
Spike Timing-Dependent Plasticity (STDP)
Markram et al (1997) find 10ms window for time-dependence
of plasticity, by manipulating pre- and post-synaptic timings.
Spike Timing Dependent Plasticity
Experimenting with
pre- and post-synaptic
spike-timings at a
synapse between a
retinal ganglion cell
and a tectal cell.
(Zhang et al, 1998)
STDP is different in
different neurons.
Diverse mechanisms Common objective??
Figure from Abbott and Nelson
STDP is different in
different neurons.
Diverse mechanisms Common objective??
This may be true,
but first we had better
understand the mechanism,
or we will most likely
think up a bad theory
based on our current
prejudices and it won’t have
any relevance to biology
(which, like the rest of the
world, is stranger than we
can suppose….)
Equation for membrane voltage (cable equation)
… membrane capacitance
… conductance along dendrite
… maximum conductance for channel species k
… time-varying fraction of those channels open
… reversal potential for channel species k
Equation for ion channel kinetics (non-linear Markov model)
etc
………. voltage: information from within the cell
………. extracellular ligand: information from other cells
…intracellular calcium: information from other molecules
Can we connect the information-theoretic
learning principles we studied yesterday to
the biophysical and molecular reality of these
processes?
Let’s give it a go in a simplified model….
the Spike Response Model (a sophisticated
variant of the ‘integrate-and-fire’ model).
HOW DOES ONE SPIKE TIMING AFFECT ANOTHER?
uk =
∑ Wij R kl (t k - tl )
l
Gerstner’s SPIKE RESPONSE MODEL:
Tkl =
∂ tk
∂ tl
=
.
Wij R kl (t k - tl )
.u
k
IMPLICIT DIFFERENTIATION
Assuming: a deterministic feedforward invertible network,
The Idea is: output spikes to be as sensitive as possible to inputs.
Maximum Likelihood: try to
map inputs uniformly into unit
hypercube:
Maximum Spikelihood: try to
map inputs into independent
Poisson processes:
y
t' i'
W
W
ti
x
p(t' i')
p(y) 1
p(y) =
p(x)
| |
p(t i)
p(t' i' ) =
|
|
OBJECTIVE FUNCTIONS FOR RATE AND SPIKING MODELS:
BE
NON-LOSSY
USE THE
BANDWIDTH
use all firing
rates equally
LIKELIHOOD
y
W
L(x) =
log |W| + ∑ log q(u i)
i
x
make the spikecount
Poisson
SPIKELIHOOD
t' i'
W
ti
L(t i) >
~
log |T| + ∑ log q(n'i )
i
THE LEARNING RULE
L(t i) >
~
log |T| + ∑ log q(n'i )
for the objective:
i
is
mean rate
sum over spikes
from neuron j
when T is a single
rate at input
synapse
Simulation results: Coincidence detection (Demultiplexing).
A 9x9 network extracts independent point processes from correlated ones
unmixing
matrix
(learned)
demultiplexed
spike
trains
time (ms)
multiplexed
spike
trains
mixing
matrix
original
spike
trains
Mixing
Unmixing
×
demultiplexed
original
Identity
=
Compare with STDP:
The Spike Response Model is causal. It only takes into account
how output spikes talk about past input spikes:
Froemke& Dan,
Nature 2002
Bell & Parra
(NIPS 17)
But real STDP has a predictive component: (spikes also talk about future spikes)
OUT
causal
predictive
IN
?
Postsynaptic calcium integrates this information (Zucker 98), both causal
(NMDA channels -> CAM-K) and predictive (L-channels -> calcineurin)
Problems with this spikelihood model:
-requires a non-lossy map [t, i] in -> [t, i]out (which we enforced…)
-learning is (horrendously) non-local
-model does not match STDP curves
-model ignores predictive information
-information only flows from synapse to soma, and not back down
By infomaxing from input spikes to output spikes, we are ignoring
the information that flows from output spikes (and elsewhere in
the dendrites) back down to where the input information came
from - the site of learning: the protein/calcium machinery at postsynaptic densities, where the plasticity calculation actually takes
place.
What happens if you include this in your Jacobian?
Then the Jacobian between all spike-timings becomes the sum
total of all intradendritic causalities. And spikes are talking to
synapses, not other spikes. This is a massively overcomplete interlevel information flow (1000 times as many synaptic events as
neural events). What kind of density estimation scheme do we
then have?
The Within models and creates the Between:
ie: inside the cells:
(timings voltage calcium)
models and creates
between the cell:
(spikes)
Post-synaptic machinery (site of learning) integrates
incoming spike information with global cell state.
2+
Ca converts timing and voltage information into molecular change
vesicle with glu receptors
is trafficked to plasma membrane
Ca2+
endoplasmic
reticulum
dendrite
Ca2+
protein machinery
AMPA
channel
neurotransmitter
(glutamate)
NMDA
channel
synapse
voltagedependent
L-channel
Networks within networks:
network of neurons
network of 2 agents
1 brain
network of protein complexes
(eg: synapses)
1 cell
network of macromolecules
A Multi-Level View of Learning
LEVEL
UNIT
INTERACTIONS
LEARNING
ecology
society
predation,
symbiosis
natural selection
society
organism
behaviour
sensory-motor
learning
organism
cell
synapse
protein
cell
synapse
protein
amino acid
spikes
voltage, Ca
direct,V,Ca
molecular forces
synaptic plasticity
(
= STDP)
bulk molecular changes
molecular changes
gene expression,
protein recycling
Increasing
Timescale
LEARNING at a LEVEL is CHANGE IN INTERACTIONS between its UNITS,
implemented by INTERACTIONS at the LEVEL beneath, and by extension
resulting in CHANGE IN LEARNING at the LEVEL above.
Separation of timescales allows INTERACTIONS at one LEVEL
to be LEARNING at the LEVEL above.
Interactions=fast
Learning=slow
Advantages:
A closed system can model itself (sleep, thought…)
World modeling is not done directly. Rather, it occurs as a
side-effect of self-modeling. The world is a ‘boundary-condition’
on this modeling, imposed by the level above - by the social level.
The variables which form the probability model are explicitly
located at the level beneath the level being modeled.
Generalising to molecular and social networks suggests that
gene expression and reward-based social agency may just be other
forms of inter-level density estimation.
Does the ‘standard model’ really suffice?
Decision
Reinforcement
Eh..somewhere else
Action
V whatever
V1
Thalamus
Retina
Does the ‘standard model’ really suffice?
Decision
Reinforcement
Eh..somewhere else
Action
V whatever
V1
Thalamus
Retina
Or is it ‘levels-chauvinism’?
The standard (or rather the slightly more emerged)
neurostatistical model, as articulated by Emo Todorov:
The emerging computational theory of perception is Bayesian inference. It
postulates that the sensory system combines a prior probability over
possible states of the world, with a likelihood that observed sensory data
was caused by each possible state, and computes a posterior probability over
the states of the world given the sensory data.
The emerging computational theory of movement is stochastic optimal control.
It postulates that the motor system combines a utility function quantifying
the goodness of each possible outcome, with a dynamics model of how outcomes
are caused by control sequences, and computes a control law (state-control
mapping) which optimizes expected utility.
But we haven’t seen yet what unsupervised models may
do when they are involved in sensory-motor loops. They
may sidestep common criticisms of feedforward
unsupervised theories ……
1
Infomax between Layers.
(eg: V1 density-estimates Retina)
y
2
Infomax between Levels.
(eg: synapses density-estimate spikes)
t
V1
all neural spikes
synapses,
dendites
synaptic
weights
x
retina
y
• within-level
• feedforward
• molecular sublevel is ‘implementation’
• social superlevel is ‘reward’
• predicts independent activity
• only models outside input
all synaptic readout
• between-level
• includes all feedback
• molecular net models/creates
• social net is boundary condition
• permits arbitrary activity dependencies
• models input and intrinsic together
pdf of all synaptic ‘readouts’
This SHIFT in looking at the problem
alters the question so that if it is
answered, we have an unsupervised
theory of ‘whole brain learning’.
pdf of all spike times
If we can
make this
pdf uniform
then we have a model
constructed from all synaptic and dendritic causality
What about the mathematics?
Is it tractable?
Not yet.
A new, in many ways satisfactory, objective
is defined, but the gradient calculation seems
very difficult.
But this is still progress.
Density Estimation when the input is affected:
Make the model
like the reality
by minimising the Kullback-Leibler Divergence:
by gradient descent in a parameter
It is easier to live in a
world where one can
of the model
change the world
to fit the model,
as well as
:
changing
one’s model
to fit the world
Conclusion:
This should be easier, but it isn’t yet.
I’m open to suggestions…
What have we learned from other complex
self-organising systems?
Is there a simpler model which captures
the essence of the problem?