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

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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).
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;
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;
# -*- coding: utf-8 -*-

This is the analysis module for Averaged Neuron (AN) model. In this module, 
you can analyze firing patterns from AN model, mainly using frequency and
spike analysis. 

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

import os
import sys
change local env variables BEFORE importing numpy
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'

from enum import Flag, auto
import numpy as np
from scipy.signal import periodogram
from scipy import signal

class WavePattern(Flag):
    """ Enumeration class that distinguish different wave pattern.
    SWS = auto()
    SWS_FEW_SPIKES = auto()
    AWAKE = auto()
    RESTING = auto()
    EXCLUDED = auto()
    ERROR = auto()

class WaveCheck:
    """ Check which wave pattern the neuronal firing belong to.

    samp_freq : int
        sampling frequency of neuronal recordings (Hz)
    wave_patters : WavePattern
        choices of wave pattern: enumeration objects
    samp_freq : int
        sampling frequency of neuronal recordings (Hz)
    freq_spike : FreqSpike
        contains helper functions for analyzing firing pattern
        using those frequency and spikes
    def __init__(self, samp_freq: int=1000) -> None:
        self.wave_pattern = WavePattern
        self.samp_freq = samp_freq
        self.freq_spike = FreqSpike(samp_freq=samp_freq)
    def pattern(self, v: np.ndarray) -> WavePattern:

        v : np.ndarray
            membrane potential over time

            which wave pattern `v` belong to 
        if np.any(np.isinf(v)) or np.any(np.isnan(v)):
            return self.wave_pattern.EXCLUDED
        detv: np.ndarray = signal.detrend(v)
        max_potential: float = max(detv)
        f: np.ndarray  # Array of sample frequencies
        spw: np.ndarray  # Array of power spectral density or power spectrum
        f, spw = periodogram(detv, fs=self.samp_freq)
        maxamp: float = max(spw)
        nummax: int = spw.tolist().index(maxamp)
        maxfre: float = f[nummax]
        numfire: int = self.freq_spike.count_spike(v)

        if 200 < max_potential:
            return self.wave_pattern.EXCLUDED
        elif (maxfre < 0.2) or (numfire < 5*2):
            return self.wave_pattern.RESTING
        elif (0.2 < maxfre < 10.2) and (numfire > 5*5*maxfre - 1):
            return self.wave_pattern.SWS
        elif (0.2 < maxfre < 10.2) and (numfire <= 5*5*maxfre - 1):
            return self.wave_pattern.SWS_FEW_SPIKES
        elif maxfre > 10.2:
            return self.wave_pattern.AWAKE
            return self.wave_pattern.EXCLUDED

class FreqSpike:

    samp_freq : int
        sampling frequency of neuronal recordings (Hz)

    samp_freq : int
        sampling frequency of neuronal recordings (Hz)
    def __init__(self, samp_freq: int) -> None:
        self.samp_freq = samp_freq

    def count_spike(self, v: np.ndarray) -> int:
        """ Count how many times a neuron fired.

        If neuron traverse -20 mV in a very short time range (1ms), 
        traverse count is added 1. Here, spike count is calculated as 
        traverse count // 2. 

        v : np.ndarray
            membrane potential of a neuron
            spike count
        ntraverse: int = 0
        ms: int = int(self.samp_freq / 1000)
        for i in range(len(v)-1):
            if (v[i]+20) * (v[i+ms]+20) < 0:
                ntraverse += 1
        nspike: int = int(ntraverse//2)
        return nspike

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