Dr. Farzad Farkhooi

Dr. Farkhooi erforscht die Dynamik kortikaler Netzwerke und die Mechanismen, die zu stabiler, aber flexibler Hirnaktivität führen. Sein aktueller Fokus liegt auf der Theorie ausgewogener Netzwerke (balanced networks), insbesondere wie Netzwerk-Topologie und synaptische Plastizität zusammenwirken, um Fluktuationen zu kontrollieren und gleichzeitig Rechenflexibilität zu bewahren. Er entwickelt mathematische Rahmenwerke (Fredholm-Theorie, Martingal-Theorie, Renewal-Theorie), um das Verhalten von Spike-Netzwerken unter realistischen, verrauschten Bedingungen vorherzusagen. Seine Erkenntnisse sind relevant für das Verständnis von Sensorverarbeitung, neuronale Kodierung und die Grundlagen stabiler Netzwerk-Computation — mit potenziellen Anwendungen in neuromorphen Systemen und KI-Architekturen, die biologische Netzwerk-Prinzipien nachahmen.

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• Erweiterung der Standard-Gleichgewichtstheorie für kortikale Netzwerke: Multiple Zeitskalen in kortikalen Dynamiken

  Quelle ↗

  Förderer: DFG Eigene Stelle (Sachbeihilfe) Zeitraum: 04/2019 - 09/2020 Projektleitung: Dr. Farzad Farkhooi

• Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability

  2009

  Physical Review E · DOI

  The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50%, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency-adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors.

• Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways

  2013

  PLoS Computational Biology · DOI