LMU Research Fellow
Department Biology II
By combining statistical learning to infer sequences from noisy and incomplete neural data with biologically inspired neural network models to reproduce such sequences we aim to identify the key biological substrates of learning. We investigate
- behaviour-specific distributed activity patterns recorded by our collaborators, and features enabling the online detection of clinically relevant states,
- the syntax of spatial navigation and the influence of spatial learning on the experience of time,
- abstract compositive models of memory in interaction with perception: sequences, trees, graphs.
- inference of sequential activity with hidden Markov models
- network analysis of time-resolved connectivity
- generation of sequential activity with dynamical systems and simulated spiking networks
- management of neurophysiological datasets.
- McNamara, C. G., Tejero-Cantero, A., Trouche, S., Campo-Urriza, N. & Dupret, D. Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence. Nat. Neurosci. (2014).
- Rautenberg P. L., Kumaraswamy A., Tejero-Cantero Á., Doblander C., Norouzian M., Kai K., Jacobsen H.A., Ai H, Wachtler T., Ikeno H. NeuronDepot: Keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications. Front. Neuroinform. 8, (2014).
- Kammerer, A.*, Tejero-Cantero, Á.* & Leibold, C. Inhibition enhances memory capacity: optimal feedback, transient replay and oscillations. J. Comput. Neurosci. 34, 125–136 (2012). (* equally contributed)
- Maier N.*, Tejero-Cantero Á.*, Dorrn A. L., Winterer J., Beed P. S., Morris G., Kempter R., Poulet J.F.*, Leibold C.*, Schmitz D.* Coherent phasic excitation during hippocampal ripples. Neuron 72, 137–152 (2011). (* equally contributed)