Presentation is loading. Please wait.

Presentation is loading. Please wait.

The linear/nonlinear model s*f 1. The spike-triggered average.

Similar presentations


Presentation on theme: "The linear/nonlinear model s*f 1. The spike-triggered average."— Presentation transcript:

1 The linear/nonlinear model s*f 1

2 The spike-triggered average

3 More generally, one can conceive of the action of the neuron or neural system as selecting a low dimensional subset of its inputs. Start with a very high dimensional description (eg. an image or a time-varying waveform) and pick out a small set of relevant dimensions. s(t) s1s1 s2s2 s3s3 s1s1 s2s2 s3s3 s4s4 s5s5 s.. snsn S(t) = (S 1,S 2,S 3,…,S n ) Dimensionality reduction P(response | stimulus)  P(response | s 1, s 2,.., s n )

4 Linear filtering Linear filtering = convolution = projection s1s1 s2s2 s3s3 Stimulus feature is a vector in a high-dimensional stimulus space

5 How to find the components of this model s*f 1

6 The input/output function is: which can be derived from data using Bayes’ rule: Determining the nonlinear input/output function

7 Tuning curve: P(spike|s) = P(s|spike) P(spike) / P(s) Nonlinear input/output function Tuning curve: P(spike|s) = P(s|spike) P(spike) / P(s) s P(s|spike)P(s) s P(s|spike)P(s)

8 Models with multiple features Linear filter s & nonlinearity: r(t) = g(f 1 *s, f 2 *s, …, f n *s)

9 Spike-triggered average Gaussian prior stimulus distribution Spike-conditional distribution covariance Determining linear features from white noise

10 The covariance matrix is Properties: The number of eigenvalues significantly different from zero is the number of relevant stimulus features The corresponding eigenvectors are the relevant features (or span the relevant subspace ) Stimulus prior Bialek et al., 1988; Brenner et al., 2000; Bialek and de Ruyter, 2005 Identifying multiple features Spike-triggered stimulus correlation Spike-triggered average

11 Inner and outer products u  v =  u i v i u  v = projection of u onto v u  u = length of u u x v = M where M ij = u i v j

12 Eigenvalues and eigenvectors M u = u

13 Singular value decomposition m n = xx M U  V

14 Principal component analysis M M * = (U  V)(V *  * U * ) = U  (V V * )  * U * = U  * U * C = U  U *

15 The covariance matrix is Properties: The number of eigenvalues significantly different from zero is the number of relevant stimulus features The corresponding eigenvectors are the relevant features (or span the relevant subspace ) Stimulus prior Bialek et al., 1988; Brenner et al., 2000; Bialek and de Ruyter, 2005 Identifying multiple features Spike-triggered stimulus correlation Spike-triggered average

16 Let’s develop some intuition for how this works: a filter-and-fire threshold-crossing model with AHP Keat, Reinagel, Reid and Meister, Predicting every spike. Neuron (2001) Spiking is controlled by a single filter, f (t) Spikes happen generally on an upward threshold crossing of the filtered stimulus  expect 2 relevant features, the filter f (t) and its time derivative f ’(t) A toy example: a filter-and-fire model

17 STA f(t) f'(t) Covariance analysis of a filter-and-fire model

18 Example: rat somatosensory (barrel) cortex Ras Petersen and Mathew Diamond, SISSA Record from single units in barrel cortex Some real data

19 Spike-triggered average: Pre-spike time (ms) Normalised velocity White noise analysis in barrel cortex

20 Is the neuron simply not very responsive to a white noise stimulus? White noise analysis in barrel cortex

21 PriorSpike- triggered Difference Covariance matrices from barrel cortical neurons

22 Pre-spike time (ms) Velocity EigenspectrumLeading modes Eigenspectrum from barrel cortical neurons

23 Input/output relations wrt first two filters, alone: and in quadrature: Input/output relations from barrel cortical neurons

24 Pre-spike time (ms) Velocity (arbitrary units) How about the other modes? Next pair with +ve eigenvalues Pair with -ve eigenvalues Less significant eigenmodes from barrel cortical neurons

25 Firing rate decreases with increasing projection: suppressive modes Input/output relations for negative pair Negative eigenmode pair

26 Salamander retinal ganglion cells perform a variety of computations Michael Berry

27 Almost filter-and-fire-like

28 Not a threshold-crossing neuron

29 Complex cell like

30 Bimodal: two separate features are encoded as either/or

31 Complex cells in V1 Rust et al., Neuron 2005

32 integrators H1, some single cortical neurons differentiators Retina, simple cells, HH neuron, auditory neurons frequency-power detectors V1 complex cells, somatosensory, auditory, retina Basic types of computation

33 When have you done a good job? When the tuning curve over your variable is interesting. How to quantify interesting?

34 Tuning curve: P(spike|s) = P(s|spike) P(spike) / P(s) Goodness measure: D KL (P(s|spike) | P(s)) When have you done a good job? Tuning curve: P(spike|s) = P(s|spike) P(spike) / P(s) s Boring: spikes unrelated to stimulus P(s|spike)P(s) s Interesting: spikes are selective P(s|spike)P(s)

35 Maximally informative dimensions Choose filter in order to maximize D KL between spike-conditional and prior distributions Equivalent to maximizing mutual information between stimulus and spike Sharpee, Rust and Bialek, Neural Computation, 2004 Does not depend on white noise inputs Likely to be most appropriate for deriving models from natural stimuli s = I*f P(s|spike)P(s)

36 1.Single, best filter determined by the first moment 2.A family of filters derived using the second moment 3.Use the entire distribution: information theoretic methods Removes requirement for Gaussian stimuli Finding relevant features

37 Less basic coding models Linear filter s & nonlinearity: r(t) = g(f 1 *s, f 2 *s, …, f n *s) …shortcomings?

38 Less basic coding models GLM: r(t) = g(f 1 *s + f 2 *r) Pillow et al., Nature 2008; Truccolo,.., Brown, J. Neurophysiol. 2005

39 Less basic coding models GLM: r(t) = g(f*s + h*r) …shortcomings?

40 Less basic coding models GLM: r(t) = g(f 1 *s + h 1 *r 1 + h 2 *r 2 +…) …shortcomings?

41 Poisson spiking

42 Shadlen and Newsome, 1998

43 Poisson spiking Properties: Distribution: P T [k] = (rT) k exp(-rT)/k! Mean: = rT Variance: Var(x) = rT Interval distribution: P(T) = r exp(-rT) Fano factor: F = 1

44 Fano factor Data fit to: variance = A  mean B A B Area MT Poisson spiking in the brain

45 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 Poisson spiking in the brain

46 Hodgkin-Huxley neuron driven by noise


Download ppt "The linear/nonlinear model s*f 1. The spike-triggered average."

Similar presentations


Ads by Google