Synchrony with shunting inhibition in a feedforward inhibitory network

Talathi, Sachin S.; Hwang, Dong‐Uk; Carney, Paul R.; Ditto, William L.

doi:10.1007/s10827-009-0210-2

Cited by 8 publications

(12 citation statements)

References 37 publications

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“…Slow currents that cause adaptation from cycle to cycle also violate the assumption of pulsatile coupling. One possible solution to the problem of higher order components of the resetting is to apply repetitive inputs at a fixed delay rather than just a single pulse ([52]), and another solution utilized an empirically derived renormalization factor to account for second order effects that continued beyond the first stimulus interval in a cycle, and even for third order resetting ([61], [62]).…”

Section: Summary and Significancementioning

confidence: 99%

Pulse coupled oscillators and the phase resetting curve

Canavier

Achuthan

2010

Mathematical Biosciences

109

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Limit cycle oscillators that are coupled in a pulsatile manner are referred to as pulse coupled oscillators. In these oscillators, the interactions take the form of brief pulses such that the effect of one input dies out before the next is received. A phase resetting curve (PRC) keeps track of how much an input advances or delays the next spike in an oscillatory neuron depending upon where in the cycle the input is applied. PRCs can be used to predict phase locking in networks of pulse coupled oscillators. In some studies of pulse coupled oscillators, a specific form is assumed for the interactions between oscillators, but a more general approach is to formulate the problem assuming a PRC that is generated using a perturbation that approximates the input received in the real biological network. In general, this approach requires that circuit architecture and a specific firing pattern be assumed. This allows the construction of discrete maps from one event to the next. The fixed points of these maps correspond to periodic firing modes and are easier to locate and analyze for stability compared to locating and analyzing periodic modes in the original network directly. Alternatively, maps based on the PRC have been constructed that do not presuppose a firing order. Specific circuits that have been analyzed under the assumption of pulsatile coupling include one to one lockings in a periodically forced oscillator or an oscillator forced at a fixed delay after a threshold event, two bidirectionally coupled oscillators with and without delays, a unidirectional N-ring of oscillators, and N all-to-all networks.

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Section: Summary and Significancementioning

confidence: 99%

Pulse coupled oscillators and the phase resetting curve

Canavier

Achuthan

2010

Mathematical Biosciences

109

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“…The feedback inhibition and connectivity of this network was designed to amplify specific oscillatory signals (Stacey et al 2009;Tort et al 2007), activity which is known to be present in brain cortex (Murphy and Miller 2009). Finally, each cell type in the model is homogeneous, so it does not account for varied cell physiology as in other modeling work (Talathi et al 2010).…”

Section: Computer Modelmentioning

confidence: 99%

Network recruitment to coherent oscillations in a hippocampal computer model

Stacey

Krieger

Litt

2011

Journal of Neurophysiology

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Stacey WC, Krieger A, Litt B. Network recruitment to coherent oscillations in a hippocampal computer model. J Neurophysiol 105: 1464-1481, 2011. First published January 27, 2011 doi:10.1152/jn.00643.2010.-Coherent neural oscillations represent transient synchronization of local neuronal populations in both normal and pathological brain activity. These oscillations occur at or above gamma frequencies (Ͼ30 Hz) and often are propagated to neighboring tissue under circumstances that are both normal and abnormal, such as gamma binding or seizures. The mechanisms that generate and propagate these oscillations are poorly understood. In the present study we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of stochastic resonance and coherence resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. "Epileptic" high-frequency oscillations (HFOs), produced by pathologies such as increased synaptic activity and recurrent connections, were superior at recruiting neighboring tissue. "Normal" HFOs, associated with fast firing of inhibitory cells and sparse pyramidal cell firing, tended to suppress surrounding cells and showed very limited ability to recruit. These findings point to synaptic noise and physiological coupling as important targets for understanding the generation and propagation of both normal and pathological HFOs, suggesting potential new diagnostic and therapeutic approaches to human disorders such as epilepsy. epilepsy; synchrony; noise COHERENT OSCILLATIONS in the brain are present in a variety of conditions and span a broad range of frequencies and brain regions. These oscillations are often characterized on the basis of their frequency.

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“…3. Nonlinear, threshold coupling which models a chemical synapse and accounts for the dynamics of the release and absorption of neurotransmitters in the synaptic cleft (Abarbanel et al 2003;Talathi et al 2010):…”

Section: Synapse Modelsmentioning

confidence: 99%

Detecting effective connectivity in networks of coupled neuronal oscillators

et al. 2011

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The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.

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Synchrony with shunting inhibition in a feedforward inhibitory network

Cited by 8 publications

References 37 publications

Pulse coupled oscillators and the phase resetting curve

Pulse coupled oscillators and the phase resetting curve

Network recruitment to coherent oscillations in a hippocampal computer model

Detecting effective connectivity in networks of coupled neuronal oscillators

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