hh_cond_beta_gap_traub – Hodgkin-Huxley neuron with gap junction support and beta function synaptic conductances
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Description
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``hh_cond_beta_gap_traub`` is an implementation of a modified Hodgkin-Huxley model
that also supports gap junctions.
This model is derived from the ``hh_conda_exp`` model, but supports double-exponential-shaped
(beta-shaped) synaptic conductances and also supports gap junctions. The model is originally
based on a model of hippocampal pyramidal cells by Traub and Miles [1]_.
The key differences between the current model and the model in [1]_ are:
- This model is a point neuron, not a compartmental model.
- This model includes only ``I_Na`` and ``I_K``, with simpler ``I_K`` dynamics than
in [1]_, so it has only three instead of eight gating variables;
in particular, all Ca dynamics have been removed.
- Incoming spikes induce an instantaneous conductance change followed by
exponential decay instead of activation over time.
Postsynaptic currents
---------------------
Incoming spike events induce a postsynaptic change of conductance modelled by a
beta function as outlined in [3]_ [4]_. The beta function is normalized such that an
event of weight 1.0 results in a peak current of 1 nS at :math:`t = \tau_{rise,xx}`
where xx is ex or in.
Spike Detection
---------------
Spike detection is done by a combined threshold-and-local-maximum search: if
there is a local maximum above a certain threshold of the membrane potential,
it is considered a spike.
Gap Junctions
-------------
Gap Junctions are implemented by a gap current of the form
:math:`g_{ij}( V_i - V_j)`.
.. note::
In this model, a spike is emitted if :math:`V_m \geq V_T + 30` mV and
:math:`V_m` has fallen during the current time step.
To avoid multiple spikes from occurring during the falling flank of a
spike, it is essential to choose a sufficiently long refractory period.
Traub and Miles used :math:`t_{ref} = 3` ms ([1]_, p 118), while we used
:math:`t_{ref} = 2` ms in [1]_.
Parameters
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The following parameters can be set in the status dictionary.
============ ====== =======================================================
V_m mV Membrane potential
V_T mV Voltage offset that controls dynamics. For default
parameters, V_T = -63mV results in a threshold around
-50mV
E_L mV Leak reversal potential
C_m pF Capacity of the membrane
g_L nS Leak conductance
tau_rise_ex ms Excitatory synaptic beta function rise time
tau_decay_ex ms Excitatory synaptic beta function decay time
tau_rise_in ms Inhibitory synaptic beta function rise time
tau_decay_in ms Inhibitory synaptic beta function decay time
t_ref ms Duration of refractory period (see Note)
E_ex mV Excitatory synaptic reversal potential
E_in mV Inhibitory synaptic reversal potential
E_Na mV Sodium reversal potential
g_Na nS Sodium peak conductance
E_K mV Potassium reversal potential
g_K nS Potassium peak conductance
I_e pA External input current
============ ====== =======================================================
References
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.. [1] Traub RD and Miles R (1991). Neuronal Networks of the Hippocampus.
Cambridge University Press, Cambridge UK.
.. [2] http://modeldb.yale.edu/83319
.. [3] Rotter S and Diesmann M (1999). Exact digital simulation of
time-invariant linear systems with applications to neuronal modeling.
Biological Cybernetics 81:381 DOI: https://doi.org/10.1007/s004220050570
.. [4] Roth A and van Rossum M (2010). Chapter 6: Modeling synapses.
in De Schutter, Computational Modeling Methods for Neuroscientists,
MIT Press.
Sends
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SpikeEvent
Receives
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SpikeEvent, CurrentEvent, DataLoggingRequest
See also
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:doc:`Neuron `, :doc:`Hodgkin-Huxley `, :doc:`Conductance-Based `