In conclusion, in this thesis, models for associative learning which may serve to asso-ciate meaning with sound were introduced as well as an conceptually complete model for vocal learning composed of a novel architecture and a learning mechanism that not only serves to form inverse models, but is also able to imprint spiking sequences onto recurrent networks, which may underlie the formation of sensory memories.
Throughout this thesis, all networks were numerically integrated using a simple Euler integration scheme. The simulations were written in Python and used a step size of 0.5ms for all leaky integrate-and-fire neurons, a step size of 0.01ms for the conductance-based integrate-and-fire neurons and a step size of 0.005msfor the Hodgkin-Huxley type neurons.
The values of neurons parameters can be found in the respective chapters.
Sascha Helduser, Maren Westkott, Klaus Pawelzik, and Onur G¨unt¨urk¨un. The putative pigeon homologue to song bird lman does not modulate behavioral variability. Behavioural brain research, 263:144148, 2014.
Christian Albers, Maren Westkott, and Klaus Pawelzik. Perfect associative learning with spike-timing-dependent plasticity. In C. J. C.Burges, L. Bottou, M. Welling, Z. Ghahra-mani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 17091717. Curran Associates, Inc., 2013.
Christian Albers, Maren Westkott, and Klaus Pawelzik. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity. PLoS ONE 11(2):
e0148948. doi:10.1371/journal.pone.0148948, 2016.
Maren Westkott and Klaus Pawelzik. An Integrative Account of Acoustic Sequence Imita-tion Learning in the Songbird. Frontiers in Computational Neuroscience 2015under review.
I have a lot of people to thank without whom the completion of this work would have been impossible. First and foremost, I have to thank my supervisor Klaus Pawelzik for all the creative input and all the new ideas, as well as for the fruitful discussions. I would like to thank Agnes Janßen for the relentless administrative support, which makes life in the group so much easier, as well as for the enthusiasm, good mood and cookies she provides for the group. For great technical support, I would like to thank David Rothermund, who is always available for the stupidest of questions, which he answers with remarkable calmness. I would like to thank all members of the group for providing a friendly and stimulating work environment. In particular, I am grateful to Christian Albers for the work we did together, for long fruitful discussions and for advice. I would like to thank my proof readers Christian Albers, Nergis T¨omen, Federica Capparelli, Daniel Harnack and last but not least Axel Grzymisch, who greatly contributed to making this work more readable, understandable and overall better.
Finally, I would like to thank friends and family for the ongoing support over the course of the years. In particular, I am grateful to my partner M.L. for the ongoing support, feeding and bribing throughout the writing process.
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