| | Models | Description |
| 1. |
Calyx of Held, short term plasticity (Yang Z et al. 2009)
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This model investigates mechanisms contributing to short term plasticity at the calyx of Held, a giant glutamatergic synapse in the mammalian brainstem auditory system. It is a stochastic version of the model described in:
Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2007). A biophysical model of short-term plasticity at the calyx of Held.
Neurocomputing, 70:1626-1629.
This version introduces stochastic vesicle recycling and release. It has been used to investigate the information transmission
properties of this synapse, as detailed in:
Yang, Z., Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2008).
Wide-band information transmission at the calyx of Held. Neural Computation, 21(4):991-1018.
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| 2. |
Coincidence detection in avian brainstem (Simon et al 1999)
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A detailed biophysical model of coincidence
detector neurons in the nucleus laminaris (auditory brainstem) which are
purported to detect interaural time differences (ITDs) from Simon et al 1999. |
| 3. |
Coincidence detection in MSO principal cells (Goldwyn et al. 2019)
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How a particular combination of anatomical and biophysical properties results in a short integration window (good for detection of closely-coincident inputs) while also enabling efficient axonal firing with brief interspike intervals (needed to faithfully report a series of coincidences between high frequency presynaptic spike trains). |
| 4. |
Dipolar extracellular potentials generated by axonal projections (McColgan et al 2017)
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" ... Here, we established experimentally and
theoretically that contributions of axons to EFPs can be significant. Modeling action
potentials propagating along axons, we showed that EFPs were prominent in the
presence of terminal zones where axons branch and terminate in close succession, as
found in many brain regions. Our models predicted a dipolar far field and a polarity
reversal at the center of the terminal zone. ..." |
| 5. |
Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)
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This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985). |
| 6. |
High entrainment constrains synaptic depression in a globular bushy cell (Rudnicki & Hemmert 2017)
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" ... Here we show how different levels of synaptic depression shape firing
properties of GBCs in in vivo-like conditions using computer simulations.
We analyzed how an interplay of synaptic depression (0 % to 70 %) and the
number of auditory nerve fiber inputs (10 to 70) contributes to the
variability of the experimental data from previous studies. ... Overall, this study helps to understand how synaptic
properties shape temporal processing in the auditory system. It also integrates,
compares, and reconciles results of various experimental studies." |
| 7. |
Interaural time difference detection by slowly integrating neurons (Vasilkov Tikidji-Hamburyan 2012)
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For localization of a sound source, animals and humans process the microsecond interaural time differences of arriving sound waves. How nervous systems, consisting of elements with time constants of about and more than 1 ms, can reach such high precision is still an open question. This model shows that population of 10000 slowly integrating Hodgkin-Huxley neurons with inhibitory and excitatory inputs (EI neurons) can detect minute temporal disparities in input signals which are significantly less than any time constant in the system. |
| 8. |
Modelling platform of the cochlear nucleus and other auditory circuits (Manis & Compagnola 2018)
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"Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, bio-physically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. ..." |
| 9. |
Response properties of an integrate and fire model (Zhang and Carney 2005)
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"A computational technique is described for calculation of the interspike interval and poststimulus time histograms for the responses of an
integrate-and-fire model to arbitrary inputs.
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For stationary inputs, the regularity of the output was studied in detail for various model parameters.
For nonstationary inputs, the effects of the model parameters on the output synchronization index were explored.
... these response properties have been reported for some cells in the ventral cochlear nucleus in the auditory brainstem.
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| 10. |
Simulating ion channel noise in an auditory brainstem neuron model (Schmerl & McDonnell 2013)
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" ... Here we demonstrate that biophysical models of channel noise can give rise to two
kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem
neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action
potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model.
The
second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of
noise inhibit tonic firing and replace it with burstlike dynamics. ..."
Preprint available at http://arxiv.org/abs/1311.2643 |
