Benjamin Lindner

In my talk I discuss seemingly complex spontaneous spike statistics of some sensory neurons, that arises due to correlated fluctuations and adaptation mechanisms. I sketch novel analytical methods to deal with the associated non-Markovian first-passage-time problems. I furthermore show that such correlated (colored) noise also emerges in an autonomous fashion in recurrent neural networks and leads to similar features in the spike statistics of cortical cells. Fluctuations do not only lead to interesting spontaneous neural activity but also shape the way in which neurons encode information about time-dependent stimuli. I elucidate in particular mechanisms of information filtering in single neurons and neural populations.

References:

B. Dummer, S. Wieland, and B. Lindner Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity Front. Comp. Neurosci. 8 , 104 (2014)

T. Schwalger, F. Droste, and B. Lindner Statistical structure of neural spiking under non-Poissonian or other non-white stimulation J. Comp. Neurosci. 39 , 29 (2015)

S. Wieland, D. Bernardi, T. Schwalger, and B. Lindner Slow fluctuations in recurrent networks of spiking neurons Phys. Rev. E 92 040901(R) (2015)

B. Lindner Mechanisms of information filtering in neural systems IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2 5 (2016)

J. Doose, G. Doron, M. Brecht, and B. Lindner Noisy juxtacellular stimulation in vivo leads to reliable spiking and reveals high-frequency coding in single neurons J. Neurosci. 36 11120 (2016)

J. Grewe, A. Kruscha, B. Lindner, and J. Benda Synchronous Spikes are Necessary but not Sufficient for a Synchrony Code PNAS 114 E1977 (2017)