BPCPPDYSOE21 – wissenschaftliches Programm
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BP: Fachverband Biologische Physik
BP 6: Systems Biology I
BP 6.4: Vortrag
Montag, 22. März 2021, 12:00–12:20, BPc
Dynamics, Statistics and Coding in Random Rate and Binary Networks — •Tobias Kühn1,2,3, Christian Keup2,3, David Dahmen2, and Moritz Helias2,3 — 1MSC de l'Université de Paris, ENS, CNRS, Paris, France — 2INM-6, Forschungszentrum Jülich, Germany — 3Department of Physics, RWTH Aachen, Germany
Cortical neurons communicate with spikes, discrete events in time. Functional network models often employ rate units that are continuously coupled by analog signals. Is there a benefit of discrete signaling? By a unified mean-field theory for large random networks of rate and binary units, we show that both models can be matched to have identical statistics up to second order. Their stimulus processing properties, however, are different: contrary to rate networks, the chaos transition in binary networks strongly depends on network size, and we discover a chaotic submanifold in binary networks that does not exist in rate models. Its dimensionality increases with time after stimulus onset and reaches a fixed point that depends on the synaptic coupling strength. Low-dimensional stimuli are transiently expanded into higher-dimensional representations that live within the manifold. We find that classification performance first increases and then degrades due to variability in the manifold. During this transient, resilience to noise by far exceeds that of rate models with matched statistics, which are always regular. In their respective chaotic regime, however, rate networks show similar a mechanism of transient signal amplification, same for spiking networks [Keup et al. arXiv:2002.11006]. Ack.: Helmholtz assn. (VH-NG-1028); RWTH (ERS seed fund neuroIC002).