Dr. Farzad Farkhooi

Profil

Zusammenfassung

Dr. Farkhooi erforscht die mathematische Beschreibung von Netzwerkdynamiken im Gehirn, insbesondere wie Neuronen durch Anpassungsmechanismen und zeitliche Korrelationen ihre Aktivität regulieren. Seine Expertise liegt in der theoretischen Modellierung von Spike-Dynamiken und Populationscodes, mit Fokus darauf, wie biologische Netzwerke trotz Rauschen zuverlässig und sparsam Informationen verarbeiten.

Skills

Name

Dr. Farzad Farkhooi

Titel

Dr.

Fakultät

Lebenswissenschaftliche Fakultät

Institut

Institut für Biologie

Arbeitsgruppe

Theoretische Neurophysiologie

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HU-FIS-Profil

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Zuletzt gescrapt

28.6.2026, 01:05:03

• 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

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

  2009

  Physical Review E · 105 Zitationen · 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 · 59 Zitationen · DOI

  Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture.

• Adaptation reduces variability of the neuronal population code

  2011

  Physical Review E · 53 Zitationen · DOI

  Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for generalized non-renewal processes to calculate the interval and count statistics of superimposed processes governed by a slow adaptation variable. For an ensemble of neurons with spike-frequency adaptation, this results in the regularization of the population activity and an enhanced postsynaptic signal decoding. We confirm our theoretical results in a population of cortical neurons recorded in vivo.

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