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394 lines
16 KiB
394 lines
16 KiB
2 years ago
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from __future__ import print_function
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from neuron import h
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from .terminal import Terminal
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from ..util import random_seed
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# utility class to create parameter lists...
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# create like: p = Params(abc=2.0, defg = 3.0, lunch='sandwich')
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# reference like p.abc, p.defg, etc.
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class Params(object):
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def __init__(self, **kwds):
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self.__dict__.update(kwds)
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class StochasticTerminal(Terminal):
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"""
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Axon terminal with multi-site sctochastic release mechanism.
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"""
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def __init__(
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self,
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pre_sec,
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target_cell,
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nzones=1,
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multisite=True,
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message=None,
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type="lognormal",
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identifier=0,
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stochastic_pars=None,
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calcium_pars=None,
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delay=0,
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debug=False,
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select=None,
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spike_source=None,
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dep_flag=1,
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):
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"""
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This routine creates a (potentially) multisite synapse using a NEURON mod file with:
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- A MultiSiteSynapse release mechanism that includes stochastic release, with a lognormal
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release latency distribution. The Facilitation and Depression of release are governed
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by parameters obtaine from fitting the Dittman-Kreitzer-Regher (DKR) model (J Neurosci. 2000 Feb 15;20(4):1374-85.)
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to experimental data at various
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frequencies.
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- A "cleft" mechanism (models diffusion of transmitter). Note that the cleft is inserted as part of the
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presynaptic section, but is not connected to the postsynaptic side yet.
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Each release site is stochastically independent, but all sites within a terminal are drive by the
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same presynaptic action potentials.
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Turning off the depression and facilitation in the kinetic portion of the model substantially decreases
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the time the terminal mechanism takes to run.
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Parameters
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----------
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pre_sec : :obj:`section`
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The NEURON section where the synaptic mechanisms should be inserted.
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target_cell : :obj:`Cell`
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The target cell object that the synapse will innervate.
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nzones : int
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The number of activate zones to insert into the section.
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multisite : bool, default: True
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A flag that determines whether the terminal actually creates multiple
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release zones (True) or just creates a single release zone that
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varies its amplitude based on the depression/facilitation state.
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message : str
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A message to when instantiating (mostly for verification of code flow).
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type: str (default: 'lognormal')
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'lognormal' sets the release event latency distribution to use a lognormal function. Currently,
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no other function is supported.
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identifier : int (default: 0)
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An identifier to associate with these release sites so we can find them later.
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stochastic_pars : dict (default: None)
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A dictionary of parameters (Param class) used to specifiy the stochastic behavior of this site,
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including release latency, stdev, and lognormal distribution paramaters
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calcium_pars : dict (default: None)
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A dictionary of parameters (Param class) to determine the calcium channels in this section.
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If None, then no calcium channels are inserted; otherwise, a P-type calcium conductance and a dynamic
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mechanism are inserted, and their conductance is set.
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delay : float (default: 0)
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Delay time in msec from action potential until transmitter release for this terminal.
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debug : bool (default: False)
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Flag to print stuff out while debugging.
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spike_source : :obj:`section` (default: None)
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The input spike source to use in net con - default is to use pre_sec when set to None.
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dep_flag : int (default: 1)
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Set to 1 for depression mechanism (slows computation), 0 to turn off the depression calculations
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Returns
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-------
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list
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the list contains the terminal, the relsites, and the list of cleft mechanisms:
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- terminal: this is the pointer to the terminal section that was inserted (same as pre_sec if it was
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specified)
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- relsite: a list of the nzones release sites that were created
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- cleft: a list of the nzones cleft mechanisms that were created.
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"""
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Terminal.__init__(self, pre_sec)
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# set parameter control for the stochastic release of vesicles...
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# this structure is passed to stochastic synapses, and replaces several variables
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# that were previously defined in the call to that function.
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thresh = -30 # mV - AP detection on the presynaptic side.
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ANTerminals_Latency = 0.5 # latency
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vPars = Params(
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LN_Flag=1,
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LN_t0=10.0,
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LN_A0=0.05,
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LN_tau=35,
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LN_std=0.05,
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Lat_Flag=1,
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Lat_t0=10.0,
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Lat_A0=0.140,
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Lat_tau=21.5,
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latency=ANTerminals_Latency,
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)
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# NOTE: stochastic_pars must define parameters used by multisite, including:
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# .delay is the netcon delay between the presynaptic AP and the start of release events
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# .Latency is the latency to the mean release event... this could be confusing.
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if stochastic_pars is None:
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stochastic_pars = vPars
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message = (
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" >> creating terminal with %d release zones using lognormal release latencies (coh4)"
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% nzones
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)
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if debug:
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print(message)
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terminal = pre_sec
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# terminal.push()
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if calcium_pars is not None:
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terminal.insert("cap") # insert calcium channel density
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terminal().cap.pcabar = calcium_pars.Ca_gbar
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terminal.insert("cad")
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# Create point process to simulate multiple independent release zones.
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relsite = h.MultiSiteSynapse(0.5, sec=terminal)
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relsite.nZones = nzones
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if multisite:
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relsite.multisite = 1
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relsite.rseed = random_seed.current_seed() # use global random seed
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relsite.latency = stochastic_pars.latency
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relsite.latstd = stochastic_pars.LN_std
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self.n_rzones = nzones
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else:
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relsite.multisite = 0
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self.release_rng = h.Random(random_seed.current_seed())
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self.release_rng.uniform(0, 1)
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relsite.setUniformRNG(self.release_rng)
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self.n_rzones = 1
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relsite.Dep_Flag = dep_flag # control synaptic dynamics
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if debug is True:
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relsite.debug = 1
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relsite.Identifier = identifier
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# if type == 'gamma':
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# gd = gamma.rvs(2, size=10000)/2.0 # get a sample of 10000 events with a gamma dist of 2, set to mean of 1.0
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# if relsite.latstd > 0.0:
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# gds = relsite.latency+std*(gd-1.0)/gd.std() # scale standard deviation
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# else:
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# gds = relsite.latency*np.ones((10000,1))
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# if type == 'lognormal':
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# if std > 0.0:
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# gds = lognormal(mean=0, sigma=relsite.latstd, size=10000)
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# else:
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# gds = np.zeros((10000, 1))
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# use the variable latency mode of COH4. And, it is lognormal no matter what.
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# the parameters are defined in COH4.mod as follows
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# Time course of latency shift in release during repetitive stimulation
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# Lat_Flag = 0 (1) : 0 means fixed latency, 1 means lognormal distribution
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# Lat_t0 = 0.0 (ms) : minimum time since simulation start before changes in latency are calculated
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# Lat_A0 = 0.0 (ms) : size of latency shift from t0 to infinity
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# Lat_tau = 100.0 (ms) : rate of change of latency shift (from fit of a+b(1-exp(-t/tau)))
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# : Statistical control of log-normal release shape over time during repetive stimulation
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# LN_Flag = 0 (1) : 0 means fixed values for all time
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# LN_t0 = 0.0 (ms) : : minimum time since simulation start before changes in distribution are calculated
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# LN_A0 = 0.0 (ms) : size of change in sigma from t0 to infinity
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# LN_tau = 100.0 (ms) : rate of change of sigma over time (from fit of a+b*(1-exp(-t/tau)))
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relsite.LN_Flag = (
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stochastic_pars.LN_Flag
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) # enable use of lognormal release latency
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relsite.LN_t0 = stochastic_pars.LN_t0
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relsite.LN_A0 = stochastic_pars.LN_A0
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relsite.LN_tau = stochastic_pars.LN_tau
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relsite.Lat_Flag = stochastic_pars.Lat_Flag
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relsite.Lat_t0 = stochastic_pars.Lat_t0
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relsite.Lat_A0 = stochastic_pars.Lat_A0
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relsite.Lat_tau = stochastic_pars.Lat_tau
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# mpl.figure(2)
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h.pop_section()
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self.relsite = relsite
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if spike_source is None:
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spike_source = pre_sec(0.5)._ref_v
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self.netcon = h.NetCon(spike_source, relsite, thresh, delay, 1.0, sec=pre_sec)
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self.netcon.weight[0] = 1
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self.netcon.threshold = -30.0
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self.setPsdType(target_cell, select)
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def setPsdType(self, target_cell, select=None):
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"""
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Assign a postsynpatic density type - selection of receptors to be
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associated with the presynaptic terminal.
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Parameters
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----------
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target_cell : :obj:`NEURON section`
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Define the target cell so that the correct PSD is inserted
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select : str
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Not used in current implementation.
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"""
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# TODO: must resurrect this for inhibitory synapses.
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# (and move constants out to their respective synapse locations)
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# elif psdtype.startswith('gly'):
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# self.setDF(target_cell, 'ipsc', select) # set the parameters for release
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self.setDF(target_cell, "epsc") # set the parameters for release
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################################################################################
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# The following routines set the synapse dynamics, based on measurements and fit
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# to the DKR model.
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################################################################################
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def setDF(self, target_cell, synapsetype, select=None):
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"""
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Set the facilitation/depression parameters for the multisite release model.
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The parameters used here were obtained from an optimized fit of the DKR
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model to stimulus and recovery data for the auditory nerve synapses onto bushy
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and T-stellate cells at 100, 200 and 300 Hz (simultaneous fitting), for times out to about
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0.5 - 1.0 second. Data were collected by Dr. Ruili Xie and Dr. Yong Wang.
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Fitting by Paul Manis.
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Parameters
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----------
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target_cell : :obj:`NEURON section`
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Define the target cell so that the correct release kinetics are used
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synapsetype : str
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String defining the type of synapse: 'ipsc' or 'epsc'
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select : str (default: None)
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Select kinetcs from a particular example cell.
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"""
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from .. import cells
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if isinstance(target_cell, cells.Bushy):
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if synapsetype == "ipsc":
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if select is None:
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self.bushy_ipsc_average()
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else:
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self.bushy_ipsc_single(select=select)
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elif isinstance(target_cell, cells.TStellate):
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if synapsetype == "ipsc":
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self.stellate_ipsc()
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def set_params(self, **params):
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"""Set arbitrary parameters on the release mechanism.
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Parameters
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----------
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\**params : dict
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dictionary of parameters to set on the release mechanism.
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"""
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for k, v in params.items():
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setattr(self.relsite, k, v)
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def stellate_ipsc(self):
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""" Facilitation/Depression parameters for DKR model for IPSCs onto stellate cells.
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IPSCs were derived from stimulation of the dorsal cochlear nucleus, and so represent
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the glycinergic synapses from tuberculoventral neurons.
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Data is average of 3 cells studied with recovery curves and individually fit, at 100 Hz.
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"""
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self.relsite.F = 0.23047
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self.relsite.k0 = 1.23636
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self.relsite.kmax = 45.34474
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self.relsite.taud = 98.09
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self.relsite.kd = 0.01183
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self.relsite.taus = 17614.50
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self.relsite.ks = 17.88618
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self.relsite.kf = 19.11424
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self.relsite.tauf = 32.28
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self.relsite.dD = 2.52072
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self.relsite.dF = 2.33317
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self.relsite.glu = 3.06948
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def bushy_ipsc_average(self):
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""" Facilitation/Depression parameters for DKR model for IPSCs onto bushy cells.
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IPSCs were derived from stimulation of the dorsal cochlear nucleus, and so represent
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the glycinergic synapses from tuberculoventral neurons.
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Data for the kinetcs are the average of 16 bushy cells.
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The individual fits were compiled, and an average computed for just the 100 Hz data
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across the individual fits. This average was then fit to the DKR model
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(also see Matthew Xu-Friedman's papers).
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The individual cells show a great deal of variability, from straight depression, to
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mixed depression/facilitaiton, to facilation alone. This set of parameters generates
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a weak facilitation followed by depression back to baseline.
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There are other data sets in the source that are commented out.
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"""
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# print( "USING average kinetics for Bushy IPSCs")
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# average of 16cells for 100 Hz (to model); no recovery.
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self.relsite.F = 0.18521
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self.relsite.k0 = 2.29700
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self.relsite.kmax = 27.6667
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self.relsite.taud = 0.12366
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self.relsite.kd = 0.12272
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self.relsite.taus = 9.59624
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self.relsite.ks = 8.854469
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self.relsite.kf = 5.70771
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self.relsite.tauf = 0.37752
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self.relsite.dD = 4.00335
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self.relsite.dF = 0.72605
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self.relsite.glu = 5.61985
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def bushy_ipsc_single(self, select=None):
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"""
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Facilitation/Depression parameters for DKR model for IPSCs onto 3 individual bushy cells.
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IPSCs were derived from stimulation of the dorsal cochlear nucleus, and so represent
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the glycinergic synapses from tuberculoventral neurons.
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Parameters
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----------
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select : int, default=None
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select which cell's measured kinetics will be used:
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0: Use average
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1: use data from 30aug08f
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2: select = 2, use data from 30aug08h
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3: select = 3, use data from 31aug08b (single cell, clean dataset)
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"""
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# print ("Using bushy ipsc")
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if select is None or select > 4 or select <= 0:
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self.bushy_ipsc_average()
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return
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if select is 1: # 30aug08f
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# print ("using 30aug08f ipsc")
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|
|
self.relsite.F = 0.221818
|
||
|
|
self.relsite.k0 = 0.003636364
|
||
|
|
self.relsite.kmax = 0.077562107
|
||
|
|
self.relsite.taud = 0.300000
|
||
|
|
self.relsite.kd = 1.112554
|
||
|
|
self.relsite.taus = 3.500000
|
||
|
|
self.relsite.ks = 0.600000
|
||
|
|
self.relsite.kf = 3.730452
|
||
|
|
self.relsite.tauf = 0.592129
|
||
|
|
self.relsite.dD = 0.755537
|
||
|
|
self.relsite.dF = 2.931578
|
||
|
|
self.relsite.glu = 1.000000
|
||
|
|
|
||
|
|
if select is 2: # 30aug08h
|
||
|
|
# print ("using 30aug08H ipsc")
|
||
|
|
self.relsite.F = 0.239404
|
||
|
|
self.relsite.k0 = 3.636364 / 1000.0
|
||
|
|
self.relsite.kmax = 16.725479 / 1000.0
|
||
|
|
self.relsite.taud = 0.137832
|
||
|
|
self.relsite.kd = 0.000900
|
||
|
|
self.relsite.taus = 3.500000
|
||
|
|
self.relsite.ks = 0.600000
|
||
|
|
self.relsite.kf = 4.311995
|
||
|
|
self.relsite.tauf = 0.014630
|
||
|
|
self.relsite.dD = 3.326148
|
||
|
|
self.relsite.dF = 0.725512
|
||
|
|
self.relsite.glu = 1.000000
|
||
|
|
|
||
|
|
if select is 3:
|
||
|
|
# print ("using IPSC#3 ")
|
||
|
|
self.relsite.F = 0.29594
|
||
|
|
self.relsite.k0 = 0.44388 / 1000.0
|
||
|
|
self.relsite.kmax = 15.11385 / 1000.0
|
||
|
|
self.relsite.taud = 0.00260
|
||
|
|
self.relsite.kd = 0.00090
|
||
|
|
self.relsite.taus = 11.40577
|
||
|
|
self.relsite.ks = 27.98783
|
||
|
|
self.relsite.kf = 30.00000
|
||
|
|
self.relsite.tauf = 0.29853
|
||
|
|
self.relsite.dD = 3.70000
|
||
|
|
self.relsite.dF = 2.71163
|
||
|
|
self.relsite.glu = 4.97494
|