Ave. neuron model for slow-wave sleep in cortex Tatsuki 2016 Yoshida 2018 Rasmussen 2017 (all et al)

 Download zip file 
Help downloading and running models
Accession:264519
Averaged neuron(AN) model is a conductance-based (Hodgkin-Huxley type) neuron model which includes a mean-field approximation of a population of neurons. You can simulate previous models (AN model: Tatsuki et al., 2016 and SAN model: Yoshida et al., 2018), and various models with 'X model' based on channel and parameter modules. Also, intracellular and extracellular ion concentration can be taken into consideration using the Nernst equation (See Ramussen et al., 2017).
References:
1 . Tatsuki F, Sunagawa GA, Shi S, Susaki EA, Yukinaga H, Perrin D, Sumiyama K, Ukai-Tadenuma M, Fujishima H, Ohno R, Tone D, Ode KL, Matsumoto K, Ueda HR (2016) Involvement of Ca(2+)-Dependent Hyperpolarization in Sleep Duration in Mammals. Neuron 90:70-85 [PubMed]
2 . Yoshida K, Shi S, Ukai-Tadenuma M, Fujishima H, Ohno RI, Ueda HR (2018) Leak potassium channels regulate sleep duration. Proc Natl Acad Sci U S A 115:E9459-E9468 [PubMed]
3 . Rasmussen R, Jensen MH, Heltberg ML (2017) Chaotic Dynamics Mediate Brain State Transitions, Driven by Changes in Extracellular Ion Concentrations. Cell Syst 5:591-603.e4 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Mouse; Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex layer 5 interneuron;
Channel(s): I Potassium; Ca pump; I A; I Calcium; I K,Ca; I K,leak; I Na,p; I Sodium; Kir; IK Bkca; IK Skca;
Gap Junctions:
Receptor(s): NMDA; AMPA; GabaA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: Python;
Model Concept(s): Sleep; Bifurcation; Activity Patterns; Action Potentials; Bursting; Calcium dynamics; Persistent activity; Brain Rhythms; Sleep-Wake transition;
Implementer(s): Tatsuki, Fumiya [tatsuki-tky at umin.ac.jp]; Yoshida, Kensuke [kensuyoshida-tky at umin.ac.jp]; Yamada, Tetsuya ; Katsumata, Takahiro [alextfkd at m.u-tokyo.ac.jp]; Shi, Shoi [sshoi0322-tky at umin.ac.jp] ; Ueda, R, Hiroki [hiroki.ueda at nifty.com];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; AMPA; NMDA; I Na,p; I A; I K,leak; I K,Ca; I Sodium; I Calcium; I Potassium; Ca pump; Kir; IK Bkca; IK Skca; Gaba; Glutamate;
/
averaged_neuron
anmodel
__init__.py
analysis.py
channels.py
models.py
params.py
search.py
                            
# -*- coding: utf-8 -*-

"""
This is parameter module for Averaged Neuron (AN) model.
These parameters are identical to those in Tatsuki et al., 2016
and Yoshida et al., 2018.
"""

__author__ = 'Fumiya Tatsuki, Kensuke Yoshida, Tetsuya Yamada, \
              Takahiro Katsumata, Shoi Shi, Hiroki R. Ueda'
__status__ = 'Published'
__version__ = '1.0.0'
__date__ = '15 May 2020'


class Constants:
    """ Constant values needed for AN model.

    These constant values are based on previous researches.  See Tatsuki et al., 2016, 
    Yoshida et al., 2018 and Compte et al., 2003.

    Attributes
    ----------
    cm : float
        membrane capacitance (uF/cm2)
    area : float
        area of neuron (mm2)
    tauA : float
        time constant of inactivation variable for fast A-type potassium channel
    s_a_ampar : float
        coefficient of f(V)
    s_tau_ampar : float
        time constant of gating variable differential equation of AMPAR
    x_a_nmdar : float
        coefficient of f(V)
    x_tau_nmdar : float
        time constant for differential equation of second-order gating variable x
    s_a_nmdar : float
        coefficient of (1 - s)
    s_tau_nmdar : float
        time constant for differential equation of gating variable s
    s_a_gabar : float
        coefficient of f(V)
    s_tau_gabar : float
        time constant for differential equation of gating variable s
    vL : float
        equilibrium potential of leak channel (mV)
    vNaL : float
        equilibrium potential of leak sodium channel (mV)
    vNa : float
        equilibrium potential of sodium ion (mV)
    vK : float
        equilibrium potential of potassium ion (mV)
    vCa : float
        equilibrium potential of calcium channel (mV)
    vAMPAR : float
        equilibrium potential of AMPA receptor (mV)
    vNMDAR : float
        equilibrium potential of NMDA receptor (mV)
    vGABAR : float
        equilibrium potential of GABA receptor (mV)
    an_ini : list (float)
        initial parameters for differential equations of AN model:
            v : membrane potential
            h_nav : inactivation variable of voltage-gated sodium channel
            n_kvhh : activation variable of HH-type voltage-gated 
                     potassium channel
            h_kva : inactivation variable of fast A-type potassium channel
            m_kvsi : activation variable of slowly inactivating potassium channel
            s_ampar : gating variable of AMPA recptor
            x_nmdar : second-order gating variable of NMDA receptor
            s_nmdar : gating variable of NMDA receptor
            s_gabar : gating variable of GABA receptor
            ca : intracellular calcium concentration
    san_ini : list (float)
        initial parameters for differential equations of SAN model:
            v : membrane potential
            n_kvhh : activation variable of HH-type voltage-gated 
                     potassium channel
            ca : intracellular calcium concentration
    """
    def __init__(self) -> None:
        self.cm = 1.0
        self.area = 0.02

        self.a_ca = 0.5
        self.kd_ca = 30.0
        self.tau_a = 15.0
        self.s_a_ampar = 3.48
        self.s_tau_ampar = 2.0
        self.s_a_nmdar = 0.5
        self.s_tau_nmdar = 100.0
        self.x_a_nmdar = 3.48
        self.x_tau_nmdar = 2.0
        self.s_a_gabar = 1.0
        self.s_tau_gabar = 10.0

        self.vL = -60.95
        self.vNaL = 0.
        self.vNa = 55.0
        self.vK = -100.0
        self.vCa = 120.0
        self.vAMPAR = 0.
        self.vNMDAR = 0.
        self.vGABAR = -70.0
        self.an_ini = [
            -45.,   # 0 : v
            0.045,  # 1 : h_nav
            0.54,   # 2 : n_kvhh
            0.045,  # 3 : h_kva
            0.34,   # 4 : m_kvsi
            0.01,   # 5 : s_ampar
            0.01,   # 6 : x_nmdar
            0.01,   # 7 : s_nmdar
            0.01,   # 8 : s_gabar
            1.,     # 9 : Ca
            ]
        self.san_ini = [
            -45.,   # 0 : v
            0.54,   # 1 : n_kvhh
            1.,     # 2 : Ca
        ]


class Ion:
    """ Constant values needed for AN model with ion and typical ion concentrations.

    These constant values are based on Ramussen et al., 2017.

    Attributes
    ---------
    r : float
        gas constant (J/K/mol)
    t : float
        body temprature (K)
    f : float
        Faraday constant (C/mol)
    awake_ion : dictionary (float)
        typical ion concentrations which recapitulates awake firing pattern
    sleep_ion : dictionary (float)
        typical ion concentrations which recapitulates sleep (SWS) firing pattern
    """
    def __init__(self) -> None:
        self.r = 8.314472
        self.t = 310.
        self.f = 9.64853399 * 10000

        self.awake_ion = {
            'ex_na': 140,
            'in_na': 7.0,
            'ex_k': 4.4,
            'in_k': 140,
            'ex_cl': 140,
            'in_cl': 7.0,
            'ex_ca': 1.2,
            'in_ca': 0.001,
            'ex_mg': 0.7,
        }
        self.sleep_ion = {
            'ex_na': 140.0,
            'in_na': 7.0,
            'ex_k': 3.9,
            'in_k': 140.0,
            'ex_cl': 140.0,
            'in_cl': 7.0,
            'ex_ca': 1.35,
            'in_ca': 0.001,
            'ex_mg': 0.8,
        }


class TypicalParam:
    """ Typical parameter set that recapitulate a cirtain firing pattern.

    Attributes
    ----------
    an_sws : dictionary (float)
        typical parameter set which recapitulate SWS firing pattern in AN model
        See : Tatsuki et al., 2016
    san_sws : dictionary (float)
        typical parameter set which recapitulate SWS firing patter in SAN model
        See : Yoshida et al., 2018 figure 1L.
    """
    def __init__(self) -> None:
        self.an_sws = {
            'g_leak': 0.03573,
            'g_nav': 12.2438,
            'g_kvhh': 2.61868,
            'g_kva': 1.79259,
            'g_kvsi': 0.0350135,
            'g_cav': 0.0256867,
            'g_kca': 2.34906,
            'g_nap': 0.0717984,
            'g_kir': 0.0166454,
            'g_ampar': 0.513425,
            'g_nmdar': 0.00434132,
            'g_gabar': 0.00252916,
            't_ca': 121.403,
        }
        self.san_sws = {
            'g_leak': 0.016307,
            'g_kvhh': 19.20436,
            'g_cav': 0.1624,
            'g_kca': 0.7506,
            'g_nap': 0.63314,
            't_ca': 739.09,
        }

Loading data, please wait...