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Stabilising the output frequency in multiplicative STDP

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Recapitulation. STDP. Homeostatic scaling. Model. Aim of research. Net. 4 scenarios ... If the presynaptic cell fires first, the synapse is potentiated ... – PowerPoint PPT presentation

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Title: Stabilising the output frequency in multiplicative STDP


1
Stabilising the output frequency in
multiplicative STDP
  • Fleur Zeldenrust
  • 7 November 2005

2
Contents
  • Recapitulation
  • STDP
  • Homeostatic scaling
  • Model
  • Aim of research
  • Net
  • 4 scenarios
  • Conclusion and Discussion

3
Bi Poo (1998)
Spike Timing Dependent Plasticity
4
Spike Timing Dependent PlasticitySong et al.,
2000
  • If the presynaptic cell fires first, the synapse
    is potentiated
  • If the postsynaptic cell fires first, the synapse
    is depressed
  • Important ratio between potentiation and
    depression a

5
Homeostatic Scaling of Excitability
  • keep the neuron within its working range
  • adjust excitability
  • e.g. van Welie et al. (2004)
  • Ca CaT regulates gL to attain target firing
    rate (Golowasch et al. 99)

6
Net
  • 1000 inputs
  • homeostatic scaling
  • STDP
  • Poisson

7
Model
  • Input-output relation
  • Weight changes (STDP)
  • Homeostatic scaling

8
Aim of research
  • Interactions of STDP with homeostatic scaling of
    excitability
  • Can homeostatic scaling stabilise the output
    frequency?
  • Would learning still be possible?

9
4 scenarios
  • Homogeneous inputs, uncorrelated
  • Homogeneous inputs, homogeneously correlated
  • Homogeneous inputs, inhomogeneously correlated
  • Inhomogeneous inputs, uncorrelated

10
Homogeneous inputs, uncorrelated
  • all inputs have the same mean frequency
  • only autocorrelations
  • assumption all weights have the same value

11
Homogeneous inputs, uncorrelated
  • 2 steady state solutions of which one is stable
  • output rate stabilisation
  • dependence on potentiation/ depression ratio

12
Homogeneous inputs, homogeneously correlated
  • all inputs have the same mean frequency
  • varying correlations between all the inputs
  • assumption all weights have the same value

13
Homogeneous inputs, homogeneously correlated
  • 2 steady state solutions of which one is stable
  • output rate stabilisation
  • dependence on potentiation/ depression ratio and
    amount of correlation

14
Homogeneous inputs, inhomogeneously correlated
  • all inputs have the same mean frequency
  • the group im1 iN is correlated
  • assumption two groups of homogeneous weights

15
Homogeneous inputs, inhomogeneously correlated
  • 4 steady state solutions of which one is stable
  • output rate stabilisation
  • dependence on potentiation/ depression ratio,
    amount of correlation and group size m

16
Homogeneous inputs, inhomogeneously correlated
17
Inhomogeneous inputs, uncorrelated
  • two groups of input frequencies
  • only autocorrelations
  • assumption two groups of homogeneous weights

18
Inhomogeneous inputs, uncorrelated
  • 4 steady state solutions of which one is stable
  • w1 and w2 overlap
  • output rate stabilisation
  • dependence only on potentiation/ depression ratio

19
Conclusion
  • A net with STDP and homeostatic scaling of
    excitability can stabilise the output frequency
    while the weights remain sensitive to
    correlations (not to frequencies).

20
Discussion
  • What is the role of the timescale of homeostatic
    scaling?
  • Stability of the homogeneous states?
  • More groups than two?
  • What happens without homeostatic scaling of
    excitability?
  • Dependence on the target frequency?

21
References
  • 1 Bi, G. and Poo, M., Synaptic Modifications in
    Cultured Hippocampal Neurons Dependence on Spike
    Timing, Synaptic Strength, and Postsynaptic Cell
    Type, The Journal of Neuroscience, Vol.18, pp.
    10464-10472, 1998
  • 2 Brunel, N., Dynamics of Sparsely Connected
    Networks of Excitatory and Inhibitory Spiking
    Neurons, Journal of Computational Neuroscience 8,
    pp. 183-208, 2000
  • 3 Rossem, M.C.W. van, Bi, G.Q. and Turrigiano,
    G.G., Stable Hebbian learning from Spike
    Timing-Dependent Plasticity, The Journal of
    Neuroscience, Vol.20, pp. 8812-8821, 2000
  • 4 Song, S., Miller, K.D. and Abbott, L.F.,
    Competitive Hebbian learning through
    spike-timing-dependent synaptic plasticity,
    Nature, Vol.3 no. 9, pp. 919-926, 2000
  • 5 Welie, I. van, Hooft, J.A. van and Wadman,
    W.J., Homeostatic scaling of neuronal
    excitability by synaptic modulation of somatic
    hyperpolarization activated Ih channels, PNAS,
    Vol. 101, no. 14, pp. 5123-5128, 2004
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