Neural noise and neural signals - spontaneous activity and...

Neural noise and neural signals - spontaneous activity and signal transmission

in models of single nerve cells

Benjamin Lindner

Theory of Complex Systems and Neurophysics

Institut für PhysikHumboldt-Universität Berlin

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Information theory

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time

Information theory of neural spiking

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• How do we quantify information?• What is the max info a spike train can carry?• How much info does the spike train carry

about the sensory signal?

time

Information theory of neural spiking

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• How do we quantify information?

Information theory of neural spiking

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• How do we quantify information?

Information theory of neural spiking

Shannon entropy (in bits)

mean information (reduction of uncertainty) we obtain by measuring the state of a discrete

system with probabilities

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Last time: Information theory of neural spiking

• What is the max info a spike train can carry?

Specifically: a stationary spike train with rate

0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

6t

N bins

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Last time: Information theory of neural spiking

• What is the max info a spike train can carry?

Specifically: a stationary spike train with rate

Information rate of a Poisson process

0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

6t

N bins

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Last time: Information theory of neural spiking

• General Insight: Maximal entropy depends on constraints?

Fixed finite range:

Fixed mean, semi-infinite range:

Fixed mean and variance, infinite range:

uniform

exponential

Gauss

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• Last time: How much info does the spike train carry about the sensory signal?

noisy neuron output spike traininput signal

1.Compute full entropy of the output2.For frozen stimulus calculate noise entropy unrelated to the stimulus3.Take the difference Mutual information

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time window = 0.2s

time

The direct method of determining mutual informationfor a spiking neuron

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S = !

!

Sequenzen

pilog2[pi]

(Claude Shannon,1948)

Entropy of bit sequences

sequences

Time

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Variabilitywithout

reference to the signal

N = !

!

"

Sequenzen

p̃ilog2[p̃i]

#

stimulus

Entropy with frozen signal

sequences

Time

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Mutual information rate

Mutual information

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H1 Neuron in the visual system of the blow fly (Strong et al. Phys. Rev. Lett. 1998)

The direct method for determining the info rate

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H1 Neuron in the visual system of the blow fly (Strong et al. Phys. Rev. Lett. 1998)

The direct method for determining the info rate

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Finite-size corrections to the entropy

H1 Neuron in the visual system of the blow fly (Strong et al. Phys. Rev. Lett. 1998)

The direct method for determining the info rate

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Finite-size corrections to the entropy

H1 Neuron in the visual system of the blow fly (Strong et al. Phys. Rev. Lett. 1998)

The direct method for determining the info rate

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Finite-size corrections to the entropy

H1 Neuron in the visual system of the blow fly (Strong et al. Phys. Rev. Lett. 1998)

The direct method for determining the info rate

Finite-window corrections to the entropy rate

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Finite-size corrections to the entropy

H1 Neuron in the visual system of the blow fly (Strong et al. Phys. Rev. Lett. 1998)

The direct method for determining the info rate

Finite-window corrections to the entropy rate

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Information rates of real neurons

Borst & Theunissen Nat. Neurosci. (1999)

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Information rates of real neurons

Borst & Theunissen Nat. Neurosci. (1999)

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Information rates of real neurons

Borst & Theunissen Nat. Neurosci. (1999)

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Last time: The Gaussian channel

signal

noise

Assumptions

•statistically independent•Gaussian with zero mean

and

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The dynamic Gaussian channel

signal

noise

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The dynamic Gaussian channel

signal

noise

Assumptions

•statistically independent•Gaussian with zero mean•power spectra

and

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The dynamic Gaussian channel

signal

noise

Assumptions

•statistically independent•Gaussian with zero mean•power spectra

and

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The dynamic Gaussian channel (more general)

signal

noise

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The dynamic Gaussian channel (more general)

signal

noise

Assumptions

•statistically independent•Gaussian with zero mean•power spectra

and

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The dynamic Gaussian channel (more general)

signal

noise

Assumptions

•statistically independent•Gaussian with zero mean•power spectra

and

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neuron

stimulus

A lower bound on the mutual information

spike train

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linearsignal

reconstructionneuron

stimulus

Data processing inequality:

spike train

A lower bound on the mutual information

estimatedstimulus

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linearsignal

reconstructionneuron

stimulus

Data processing inequality:

spike train

A lower bound on the mutual information

estimatedstimulus

Lower bound

C(f) =|Sx,s(f)|2

Sx,x(f)Ss,s(f)

Spectral Coherence function Cross-spectrum (stimulus-spike train)

Stimulus power spectrumSpike train power spectrum

A lower bound on the mutual information

Lower bound

Comparison of direct & lower-bound methods

Aldworth et al. PLoS Comp. Biol. (2011)

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Using the coherence function to discuss frequency-dependent information transmission

Middleton et al. J. Neurophysiol. (2009) Chacron et al. Nature (2003)

P-units Pyramidal cells

Weakly electric fish

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Using the coherence function to discuss frequency-dependent information transmission

Paddle fish

Neiman & Russell Chaos (2011)

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Using the coherence function to discuss frequency-dependent information transmission

Monkey, vestibular system

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Bullfrog(

Using the coherence function to discuss frequency-dependent information transmission

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Summary: information theory of spike trains

-information can be quantified by entropies and differences between them (mutual information)

-mutual information can be determined directly (from lots of data!) or can be estimated from below (lower bound)

-information rates are often not far from their theoretical limit; they are higher for natural stimuli

References

- Dayan & Abbott Theoretical Neuroscience MIT Press (2001)

- Gerstner & Kistler Spiking Neuron Models Cambridge University Press (2002)

- Rieke et al. Spikes: Exploring the neural code MIT Press (1996)

- Pierce An Introduction to Information Theory Dover (1980)

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