Dr. Farzad Farkhooi
Profil
Forschungsthemen1
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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Stand: 26.4.2026, 19:48:44 (Top-K=20, Min-Cosine=0.4)
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Publikationen22
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
Physical Review E · 104 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.
PLoS Computational Biology · 58 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.
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.
F1000Research · 22 Zitationen · DOI
Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.
Physical Review Letters · 17 Zitationen · DOI
We develop a unified theory that encompasses the macroscopic dynamics of recurrent interactions of binary units within arbitrary network architectures. Using the martingale theory, our mathematical analysis provides a complete description of nonequilibrium fluctuations in networks with a finite size and finite degree of interactions. Our approach allows the investigation of systems for which a deterministic mean-field theory breaks down. To demonstrate this, we uncover a novel dynamic state in which a recurrent network of binary units with statistically inhomogeneous interactions, along with an asynchronous behavior, also exhibits collective nontrivial stochastic fluctuations in the thermodynamical limit.
Frontiers in Systems Neuroscience · 13 Zitationen · DOI
Acoustic communication plays a key role for mate attraction in grasshoppers. Males use songs to advertise themselves to females. Females evaluate the song pattern, a repetitive structure of sound syllables separated by short pauses, to recognize a conspecific male and as proxy to its fitness. In their natural habitat females often receive songs with degraded temporal structure. Perturbations may, for example, result from the overlap with other songs. We studied the response behavior of females to songs that show different signal degradations. A perturbation of an otherwise attractive song at later positions in the syllable diminished the behavioral response, whereas the same perturbation at the onset of a syllable did not affect song attractiveness. We applied naïve Bayes classifiers to the spike trains of identified neurons in the auditory pathway to explore how sensory evidence about the acoustic stimulus and its attractiveness is represented in the neuronal responses. We find that populations of three or more neurons were sufficient to reliably decode the acoustic stimulus and to predict its behavioral relevance from the single-trial integrated firing rate. A simple model of decision making simulates the female response behavior. It computes for each syllable the likelihood for the presence of an attractive song pattern as evidenced by the population firing rate. Integration across syllables allows the likelihood to reach a decision threshold and to elicit the behavioral response. The close match between model performance and animal behavior shows that a spike rate code is sufficient to enable song pattern recognition.
BMC Neuroscience · 9 Zitationen · DOI
In the principal cells of the insect mushroom body, the Kenyon cells (KC), olfactory information is represented by a spatially and temporally sparse code. Each odor stimulus will activate only a small portion of neurons and each stimulus leads to only a short phasic response following stimulus onset irrespective of the actual duration of a constant stimulus. The mechanisms responsible for the sparse code in the KCs are yet unresolved. Here, we explore the role of the neuron-intrinsic mechanism of spike-frequency adaptation (SFA) in producing temporally sparse responses to sensory stimulation in higher processing stages. Our single neuron model is defined through a conductance-based integrate-and-fire neuron with spike-frequency adaptation [1]. We study a fully connected feed-forward network architecture in coarse analogy to the insect olfactory pathway. A first layer of ten neurons represents the projection neurons (PNs) of the antenna lobe. All PNs receive a step-like input from the olfactory receptor neurons, which was realized by independent Poisson processes. The second layer represents 100 KCs which converge onto ten neurons in the output layer which represents the population of mushroom body extrinsic neurons (ENs). Our simulation result matches with the experimental observations. In particular, intracellular recordings of PNs show a clear phasic-tonic response that outlasts the stimulus [2] while extracellular recordings from KCs in the locust express sharp transient responses [3]. We conclude that the neuron-intrinsic mechanism is can explain a progressive temporal response sparsening in the insect olfactory system. Further experimental work is needed to test this hypothesis empirically. [1] Muller et. al., Neural Comput, 19(11):2958-3010, 2007. [2] Assisi et. al., Nat Neurosci, 10(9):1176-1184, 2007. [3] Krofczik et. al. Front. Comput. Neurosci., 2(9), 2009.
Physical review. E · 7 Zitationen · DOI
We develop a method to investigate the effect of noise timescales on the first-passage time of nonlinear oscillators. Using Fredholm theory, we derive an exact integral equation for the mean event rate of a leaky integrate-and-fire oscillator that receives constant input and temporally correlated noise. Furthermore, we show that Fredholm theory provides a unified framework to determine the system scaling behavior for small and large noise timescales. In this framework, the leading-order and higher-order asymptotic corrections for slow and fast noise are naturally emerging. We show the scaling behavior in the both limits is not reciprocal. We discuss further how this approach can be extended to study the first-passage time in a general class of nonlinear oscillators driven by colored noise at arbitrary timescales.
arXiv (Cornell University) · 7 Zitationen · DOI
Insects identify and evaluate behaviorally relevant odorants in complex natural scenes where odor concentrations and mixture composition can change rapidly. In the honeybee, a combinatorial code of activated and inactivated projection neurons (PNs) develops rapidly within tens of milliseconds at the first level of neural integration, the antennal lobe (AL). The phasic-tonic stimulus-response dynamics observed in the neural population code and in the firing rate profiles of single neurons is faithfully captured by two alternative models which rely either on short-term synaptic depression, or on spike frequency adaptation. Both mechanisms work independently and possibly in parallel to lateral inhibition. Short response latencies in local interneurons indicate that local processing within the AL network relies on fast lateral inhibition that can suppress effectively and specifically odor responses in single PNs. Reviewing recent findings obtained in different insect species, we conclude that the insect olfactory system implements a fast and reliable coding scheme optimized for time-varying input within the behaviorally relevant dynamic range.
bioRxiv (Cold Spring Harbor Laboratory) · 6 Zitationen · DOI
Slow neural dynamics are believed to be important for behavior, learning and memory. Rate models operating in the chaotic regime show a rich dynamics at the scale of hundreds of milliseconds and provide remarkable learning capabilities. However, neurons in the brain communicate via spikes and it is a major challenge in computational neuroscience to obtain similar slow rate dynamics in networks of spiking neuron models. This question was addressed in a recent paper by Ostojic. The central claim of that paper is the existence of two states of asynchronous activity separated by a phase transition in spiking networks with fast synapses. We found that the analysis presented in the paper is factually incorrect and conceptually misleading. We provide compelling evidence that there is no such phase transition.
Physical Review Letters · 5 Zitationen · DOI
We develop a framework in which the activity of nonlinear pulse-coupled oscillators is posed within the renewal theory. In this approach, the evolution of the interevent density allows for a self-consistent calculation that determines the asynchronous state and its stability. This framework can readily be extended to the analysis of systems with more state variables and provides a population density treatment to evolve them in their thermodynamical limits. To demonstrate this we study a nonlinear pulse-coupled system, where couplings are dynamic and activity dependent. We investigate its stability and numerically study the nonequilibrium behavior of the system after the bifurcation. We show that this system undergoes a supercritical Hopf bifurcation to collective synchronization.
Proceedings of the National Academy of Sciences · 3 Zitationen · DOI
Dendrites play an essential role in the integration of highly fluctuating input in vivo into neurons across all nervous systems. Yet, they are often studied under conditions where inputs to dendrites are sparse. The dynamic properties of active dendrites facing in vivo-like fluctuating input thus remain elusive. In this paper, we uncover dynamics in a canonical model of a dendritic compartment with active calcium channels, receiving in vivo-like fluctuating input. In a single-compartment model of the active dendrite with fast calcium activation, we show noise-induced nonmonotonic behavior in the relationship of the membrane potential output, and mean input emerges. In contrast, noise can induce bistability in the input-output relation in the system with slowly activating calcium channels. Both phenomena are absent in a noiseless condition. Furthermore, we show that timescales of the emerging stochastic bistable dynamics extend far beyond a deterministic system due to stochastic switching between the solutions. A numerical simulation of a multicompartment model neuron shows that in the presence of in vivo-like synaptic input, the bistability uncovered in our analysis persists. Our results reveal that realistic synaptic input contributes to sustained dendritic nonlinearities, and synaptic noise is a significant component of dendritic input integration.
arXiv (Cornell University) · 2 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 representation in the later stages of cortical sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in a network with adapting neurons. We find that cellular adaptation plays a critical role in the transient reduction of the trial-by-trial variability of cortical spiking, providing an explanation for a wide-spread and hitherto unexplained phenomenon by a simple mechanism. In insect olfaction, cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body, independent of inhibitory mechanisms. Our results reveal a computational principle that relates neuronal firing rate adaptation to temporal sparse coding and variability suppression in nervous systems with a sequential processing architecture.
Physical Review Letters · 1 Zitationen · DOI
Balanced network theory describes the dynamic state of randomly connected systems with intrinsic negative feedback, predicting the suppression of correlations as connectivity increases. We show that this weak-correlation universality holds broadly but breaks down in power-law networks where the largest out-degrees scale with system size. In such networks, residual correlations cross over to a regime that retains macroscopic fluctuations in the thermodynamic limit. Unlike Erdös-Rényi networks, the degree heterogeneity of power-law topologies amplifies coherent population-level fluctuations. Our results provide a theoretical foundation for the strong correlations observed in real-world networks with sparse and heterogeneous connectivity.
1 Zitationen · DOI
bioRxiv (Cold Spring Harbor Laboratory) · DOI
Abstract Cortical circuits must stabilize activity while retaining the variability and flexibility essential for computation. This raises a fundamental question: how can excitatory (E) and inhibitory (I) synapses co-adapt through homeostatic plasticity without disrupting network function or relying on fine-tuned parameters? We propose a solution grounded in the multidimensional nature of intracellular calcium signaling, which independently regulates protein synthesis at E and I synapses. By analytically characterizing calcium dynamics driven by spike-train statistics, we show that calcium’s mean encodes firing rate while its variance reflects spike-time irregularity—two complementary features critical for stable yet flexible spiking. Leveraging this dual signal, we construct a closed-loop model in which inhibitory synapses are regulated by calcium’s mean and excitatory synapses by its variance through independent pathways. This mechanism preserves irregular spiking and stabilizes firing rates across diverse inputs. Strikingly, it also yields the empirically observed weakening of synaptic strengths with the number of inputs K as , leading to the spontaneous emergence of balanced excitatory–inhibitory dynamics. These results uncover a calcium-driven regulatory principle linking intracellular signaling to the origin of balanced activity in cortical networks.
bioRxiv (Cold Spring Harbor Laboratory) · DOI
Dendrites play an essential role in the integration of highly fluctuating input into neurons across all nervous systems. Nevertheless, they are often studied under the conditions where inputs to dendrites are sparse. Up to date, the dynamic properties of active dendrites facing in-vivo-like fluctuating input remains elusive. In this paper, we uncover fundamentally new dynamics in a canonical model of a dendritic compartment with active calcium channels, receiving in-vivo-like fluctuating input. We show in-vivo-like noise induces non-monotonic or bistable dynamics in the input-output relation of a dendritic compartment, both of which are absent in a noiseless condition. Our analysis shows that the timescales of the activation gating variable of the dendritic calcium dynamics determine noise-induced spontaneous order in the system. Noise can induce non-monotonicity or bistability with fast or slow calcium activation respectively. We characterize these noise-induced phenomena and their influence on the input-output relation. Furthermore, we show that timescales of the emerging stochastic bistable dynamics go far beyond a deterministic system due to stochastic switching between the solutions. Our results reveal that noise contributes to sustained dendritic nonlinearities, and it could be considered a principal component of the dendritic input integration strategies.
OSF Preprints (OSF Preprints)
Dataset for Engelken et al., 2016.
BMC Neuroscience · DOI
In their natural environment, animals sense and evaluate olfactory cues of time-varying composition and concentration. Their olfactory pathways are adapted to the natural stimulus statistics, thus it is not surprising that odor processing is fast [1]. Honey bees, for example, learn to discriminate odors presented as short as 200 ms [2]. The neural odor code in these animals emerges within 50ms after stimulus onset and neural representation changes dynamically during and after an odorant is present [1,3]. How is the insect olfactory system optimized to reliably estimate spatial and temporal aspects of the olfactory environment and what are the mechanisms behind rapid odor processing? To investigate odor encoding at the Antennal Lobe (AL) and the Mushroom Body (MB) level, we employ a simple phenomenological spiking network model of the honeybee olfactory system. The model implements a transformation from a low dimensional dense odorant representation in the AL to a high dimensional sparse representation in the MB. We demonstrate how information about the stimulus is present in both encoding schemes, by time resolved classification of neural activity. Our model displays sparse and robust odor representation in the Mushroom Body [4]. Typically, less than 10% of the Kenyon Cell population is activated by an odor, with only 2-3 spikes at the odor onset (Figure (Figure1A).1A). KC spikes establish a rapid odor identity code at stimulus onset, while intrinsic adaptation currents provide a persistent and prolonged odor trace (Figure (Figure1B).1B). Our approach allows us to investigate dynamical changes in odor representations and predict odor after images. Figure 1 (A) Kenyon Cell spike raster plot. Stimulation is indicated by gray shading (B) Top: Decoding Accuracy given two odors (chance level: 0.5) as a function of time based on spike count estimates in 50ms time bins. Bottom: Decoding accuracy based on KC adaptation ...
Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · DOI
This research contributes to the understanding of spike-frequency adaptation and its complex dynamics in vivo condition. It provides a detailed study of the non-renewal statistics emergence between subsequent inter-spike intervals due to adaptation. The results presented in this dissertation show that spike- induced adaptation has a major positive effect on the variability reduction and coding capacity in neuronal systems. Furthermore, I explore its filtering consequences on information coding at a network level to describe a mechanism for emergence of a sparse representation of the stimulus. The emerged sparse code can explain the activity of Kenyon cells in insect olfactory information processing.
Acta Neurobiologiae Experimentalis
SSRN Electronic Journal · DOI
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Identität, Organisation und Kontakt aus HU-FIS.
- Name
- Dr. Farzad Farkhooi
- Titel
- Dr.
- Fakultät
- Lebenswissenschaftliche Fakultät
- Institut
- Institut für Biologie
- Arbeitsgruppe
- Theoretische Neurophysiologie
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