what is the language of single cells?
DESCRIPTION
What is the language of single cells?. What are the elementary symbols of the code? Most typically, we think about the response as a firing rate, r(t), or a modulated spiking probability, P(r = spike|s(t)). Two extremes of description: - PowerPoint PPT PresentationTRANSCRIPT
What is the language of single cells?
What are the elementary symbols of the code?
Most typically, we think about the response as a firing rate, r(t), or a modulated spiking probability, P(r = spike|s(t)).
Two extremes of description:
A Poisson model, where spikes are generated randomly with rate r(t).
However, most spike trains are not Poisson (refractoriness, internal dynamics).Fine temporal structure might be meaningful.
Consider spike patterns or “words”, e.g.
• symbols including multiple spikes and the interval between• retinal ganglion cells: “when” and “how much”
Spike Triggered Average 2-Spike Triggered Average (10 ms separation)
2-Spike Triggered Average(5 ms)
Multiple spike symbols from the fly motion sensitive neuron
Spike statistics
Stochastic process that generates a sequence of events: point process
Probability of an event at time t depends only on preceding event: renewal process
All events are statistically independent: Poisson process
Poisson: r(t) = r independent of time, probability to see a spike only depends onthe time you watch.
PT[n] = (rT)n exp(-rT)/n!
Exercise: the mean of this distribution is rT the variance of this distribution is also rT.
The Fano factor = variance/mean = 1 for Poisson processes.The Cv = coefficient of variation = STD/mean = 1 for Poisson
Interspike interval distribution P(T) = r exp(-rT)
The Poisson model (homogeneous)
Probability of n spikes in time T as function of (rate
T)
Poisson approaches Gaussian for large rT (here
= 10)
How good is the Poisson model? Fano Factor
Fano factor Data fit to: variance = A meanB
A
BArea MT
How good is the Poisson model? ISI analysis
ISI Distribution from an area MT Neuron
ISI distribution generated from a Poisson model with a Gaussian refractory period
How good is the Poisson Model? CV analysis
Coefficients of
Variation for a
set of V1 and
MT Neurons
Poisson
Poisson with ref. period
spike-triggering stimulus feature
stimulus X(t)
decision function
spike output Y(t)x1
f1
P(s
pike
|x1
)x1
Decompose the neural computation into a linear stage and a nonlinear stage.
Modeling spike generation
Given a stimulus, when will the system spike?
To what feature in the stimulus is the system sensitive?
Gerstner, spike response model; Aguera y Arcas et al. 2001, 2003; Keat et al., 2001
Simple example: the integrate-and-fire neuron
Let’s start with a rate response, r(t) and a stimulus, s(t).
The optimal linear estimator is closest to satisfying
Predicting the firing rate
Want to solve for K. Multiply by s(t-’) and integrate over t:
Note that we have produced terms which are simply correlation functions:
Given a convolution, Fourier transform:
Now we have a straightforward algebraic equation for K(w):
Solving for K(t),
Predicting the firing rate
For white noise, the correlation function Css() = So K() is simply Crs().
Going back to:
spike-triggering stimulus feature
stimulus X(t)
decision function
spike output Y(t)x1
f1
P(s
pike
|x1
)
x1
The decision function is P(spike|x1). Derive from data using Bayes’ theorem:
P(spike|x1) = P(spike) P(x1 | spike) / P(x1)
P(x1) is the prior : the distribution of all projections onto f1
P(x1 | spike) is the spike-conditional ensemble : the distribution of all projections onto f1 given there has been a spike
P(spike) is proportional to the mean firing rate
Modeling spike generation
Models of neural function
spike-triggering stimulus feature
stimulus X(t)
decision function
spike output Y(t)x1
f1
P(s
pike
|x1
)
x1
Weaknesses
STA
Gaussian priorstimulus distribution
Spike-conditional distribution
covariance
Reverse correlation: a geometric view
Dimensionality reduction
Cij = < S(t – ti) S(t - tj)> - < STA(t - ti) STA (t - tj)> - < I(t - ti) I(t - tj)>
The covariance matrix is simply
Properties:
•If the computation is low-dimensional, there will be a few eigenvalues significantly different from zero
•The number of eigenvalues is the relevant dimensionality•The corresponding eigenvectors span the subspace of the relevant features
Stimulus prior
Bialek et al., 1997
Functional models of neural function
spike-triggering stimulus feature
stimulus X(t)
decision function
spike output Y(t)x1
f1
P(s
pike
|x1
)
x1
spike-triggering stimulus features
stimulus X(t)
multidimensionaldecision function
spike output Y(t)
x1
x2
x3
f1
f2
f3
Functional models of neural function
?
spike-triggering stimulus features
?
stimulus X(t)
multidimensionaldecision function
spike history feedback
spike output Y(t)
x1
x2
x3
f1
f2
f3
?
Functional models of neural function
Covariance analysis
Let’s develop some intuition for how this works: the Keat model
Keat, Reinagel, Reid and Meister, Predicting every spike. Neuron (2001)
• Spiking is controlled by a single filter• Spikes happen generally on an upward threshold crossing of
the filtered stimulus expect 2 modes, the filter F(t) and its time derivative F’(t)
-1.0
-0.5
0.0
0.5
1.0
Eig
en
valu
e
50403020100Mode Index
-1.0
-0.5
0.0
0.5
1.0
Eig
en
valu
e
50403020100Mode Index
-1.0
-0.5
0.0
0.5
1.0
Eig
en
valu
e
50403020100Mode Index
6
4
2
0
-2P
roje
ctio
n o
nto
Mo
de
26420-2
Projection onto Mode 1
6
4
2
0
-2P
roje
ctio
n o
nto
Mo
de
2
6420-2
Projection onto Mode 1
6
4
2
0
-2P
roje
ctio
n o
nto
Mo
de
2
6420-2
Projection onto Mode 1
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Sp
ike
-Tri
gg
ere
d A
vera
ge
-0.5 -0.4 -0.3 -0.2 -0.1 0.0Time before Spike (s)
Covariance analysis
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Sp
ike
-Tri
gg
ere
d A
vera
ge
-0.5 -0.4 -0.3 -0.2 -0.1 0.0Time before Spike (s)
Covariance analysis
Let’s try some real neurons: rat somatosensory cortex (Ras Petersen, Mathew Diamond, SISSA: SfN 2003).
0 200 400 600 800 1000
-400
-200
0
200
400
Time (ms)
Stim
ulat
or
posi
tion
(m
)
Record from single units in barrel cortex
Covariance analysis
Spike-triggered average:
-20020406080100120140160180-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Pre-spike time (ms)
No
rma
lise
d ve
loci
ty
Covariance analysis
Is the neuron simply not very responsive to a white noise stimulus?
Covariance analysis
Prior Spike-triggered
Difference
Covariance analysis
0 20 40 60 80 100-1
0
1
2
3
4
Feature number
No
rmal
ised
vel
oci
ty2
050100150-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Pre-spike time (ms)
Vel
oci
ty
Eigenspectrum
Leading modes
-3 -2 -1 0 1 2 30
2
4
6
8
10
Normalised "acceleration"
No
rmal
ised
firi
ng r
ate
-3 -2 -1 0 1 2 30
2
4
6
8
10
12
Normalised "velocity"
No
rmal
ised
firi
ng r
ate
-2 0 2
-3
-2
-1
0
1
2
3
"acceleration"
"vel
oci
ty"
0
2
4
6
8
10
0 1 2 30
2
4
6
8
10
"vel"2+"acc"2
No
rmal
ised
firi
ng r
ate
Input/outputrelations wrtfirst two filters,alone:
and in quadrature:
Covariance analysis
050100150-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Pre-spike time (ms)
Velo
city (
arb
itra
ry u
nits)
050100150-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Pre-spike time (ms)V
elo
city
(a
rbitr
ary
un
its)
How about the other modes?
Next pair with +ve eigenvalues Pair with -ve eigenvalues
Covariance analysis
Covariance analysis
0 0.5 1 1.5 2 2.5 30
0.5
1
1.5
"Energy"N
orm
alis
ed f
iring
rat
e
-3 -2 -1 0 1 2-3
-2
-1
0
1
2
Filter 100
Filt
er 1
01
0.2
0.4
0.6
0.8
1
1.2
1.4
Firing rate decreases with increasing projection: suppressive modes (Simoncelli et al.)
Input/output relations for negative pair