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Spike train auto-structure impacts post-synaptic firing and timing-based plasticity

By Bertram Scheller, Marta Castellano, Raul Vicente and Gordon Pipa


Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification

Topics: ddc:610
Year: 2011
OAI identifier: oai:publikationen.ub.uni-frankfurt.de:24857

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  1. (2008). A learning theory forreward-modulatedspike-timingdependent plasticity with application to biofeedback. PLoS
  2. (2010). Analysis and interpretation of interval and count variability in neural spike trains,” in Analysis of Parallel Spike Trains,
  3. (2009). and Plesser,H.E.(2009).Towardsreproducible descriptions of neuronal network models.
  4. (1954). Asymptotic renewal theorems.
  5. (2008). Auto-structure of presynaptic activity defines postsynaptic firing statistics and can modulate STDP-based structure formation and learning.
  6. (2009). Beyond Poisson: increased spiketime regularity across primate parietal cortex.
  7. (2010). Brain oscillations and memory.
  8. (2000). Competitive Hebbian learning through spike-timingdependent synaptic plasticity.
  9. (2010). Connectivity reflects coding: a model of voltagebased STDP with homeostasis.
  10. (2001). Correlated neuronal activity and the flow of neuronal information.
  11. (2001). Cortical development and remapping through spike timing-dependent plasticity.
  12. (2005). Dendritic computation.
  13. (2008). Dependence of neuronal correlations on filter characteristics and marginal spike train statistics.
  14. (2008). Deterministic neural dynamics transmitted through neural networks.
  15. (1983). Diffusion approximations for neuronal activity including synaptic reversal potentials.
  16. (2011). doi: 10.3389/fncom.2011.00060 Copyright ©
  17. (2001). Dynamic predictions: oscillations and synchrony in top–down processing.
  18. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons.
  19. (1997). Fractal character of the neural spike train in the visual system of the cat.
  20. (1999). Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo.
  21. (2010). Impact of spike-train autostructureonprobabilitydistribution of joint-spike events.
  22. (2007). Inhibition determines membrane potential dynamics and controls action potential generation in awake and sleeping cat cortex.
  23. (2003). Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity.J.Neurosci.23,3697–3714.
  24. (2008). Measurement of variability dynamics in cortical spike trains.
  25. (1997). Modeling neural activity using the generalized inverse Gaussian distribution.
  26. (2006). Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition.
  27. (2009). Neural synchrony in cortical networks: history, concept and current status.
  28. (2008). Noise in the nervous system.
  29. (1954). On the cumulants of renewal processes.
  30. (2008). Phenomenological models of synaptic plasticity based on spike timing.
  31. (2009). Poisson or not poisson: differences in spike train statistics between parietal cortical areas.
  32. (1997). Regulation ofsynapticefficacybycoincidenceof postsynapticAPs and EPSPs.
  33. (2007). Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity.
  34. (2006). Relationbetweensingleneuronandpopulation spiking statistics and effects on network activity.
  35. (2009). Serial correlation in neural spike trains: experimental evidence, stochastic modeling, and single neuron variability.
  36. (2007). Serial interval statisticsofspontaneousactivityincortical neurons in vivo and in vitro.
  37. (2003). Simple model of spiking neurons.
  38. (2007). Solving the distal reward problem through linkage of STDP and dopamine signaling.
  39. (2009). SORN: a self-organizing recurrent neural network.
  40. (2011). Spike train auto-structure impacts post-synaptic firing and timing-based plasticity.
  41. (2007). Spike-timingdependent plasticity in balanced random networks.
  42. (2006). Superposition of many independent spike trains is generallynotaPoissonprocess.Phys.
  43. (2003). Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity.
  44. (2000). Synaptic plasticity: taming the beast.
  45. (2002). Temporal structure in neuronal activity during working memory in macaque parietal cortex.
  46. (1976). The spontaneous activity of neurones in the cat’s cerebral cortex.
  47. (2006). Triplets of spikes in a model of spike timing-dependent plasticity.
  48. (2006). Validation of task-related excessofspikecoincidencesbasedon NeuroXidence.

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