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 a parameter search module. With this module, a certain firing pattern 
can be searched randomly with AN model, SAN model and X model. In order to 
lessen dependence of models on parameters, it's important to make various 
parameter sets (models) and then extract common features among them.\
In this script, parameter sets that recapitulate slow wave sleep (SWS) firing
pattern can be searched with algorithms as described 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'

import os
import sys
change local env variables BEFORE importing numpy
os.environ["OMP_NUM_THREADS"] = "1"  # 2nd likely
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"  # most likely

from datetime import datetime
from multiprocessing import Pool
import numpy as np
import pandas as pd
from pathlib import Path
import pickle
from time import time
from typing import Dict, List, Optional
import warnings

import models
import analysis

class RandomParamSearch():
    """ Random parameter search.
    Generate parameter sets randomly, and pick up those which recapitulate a cirtain
    firing pattern. 

    model : str
        model in which parameter search is conducted
    pattern : str
        searched firing pattern
    ncore : int
        number of cores you are going to use
    time : int or str
        how long to run parameter search (hour), default 48 (2 days)
    channel_bool : list (bool) or None
        Channel lists that X model contains. True means channels incorporated 
        in the model and False means not. The order of the list is the same 
        as other lists or dictionaries that contain channel information in 
        AN model. Example: \
        channel_bool = [
            1,  # leak channel
            0,  # voltage-gated sodium channel
            1,  # HH-type delayed rectifier potassium channel
            0,  # fast A-type potassium channel
            0,  # slowly inactivating potassium channel
            1,  # voltage-gated calcium channel
            1,  # calcium-dependent potassium channel
            1,  # persistent sodium channel
            0,  # inwardly rectifier potassium channel
            0,  # AMPA receptor
            0,  # NMDA receptor
            0,  # GABA receptor
            1,  # calcium pump
        This is SAN model, default None
    model_name : str or None
        name of the X model, default None
    ion : bool
        whether you make equiribrium potential variable or not, 
        default False
    concentration : dictionary or str or None
        dictionary of ion concentration, or 'sleep'/'awake' that
        designate typical ion concentrations, default None

    wave_check : object
        Keep attributes and helper functions needed for parameter search.
    pattern : str
        searched firing pattern
    time : int
        how long to run parameter search (hour)
    model_name : str
        model name
    model : object
        Simulation model object. See anmodel.models.py
    def __init__(self, model: str, pattern: str='SWS', ncore: int=1, 
                 hr: int=48, samp_freq: int=1000, samp_len: int=10, 
                 channel_bool: Optional[List[bool]]=None, 
                 model_name: Optional[str]=None, 
                 ion: bool=False, concentration: Optional[Dict]=None) -> None:
        self.wave_check = analysis.WaveCheck(samp_freq=samp_freq)
        self.pattern = pattern
        self.ncore = ncore
        self.hr = int(hr)
        self.samp_freq = samp_freq
        self.samp_len = samp_len

        if model == 'AN':
            self.model_name = 'AN'
            self.model = models.ANmodel(ion, concentration)
        if model == 'SAN':
            self.model_name = 'SAN'
            self.model = models.SANmodel(ion, concentration)
        if model == "X":
            if channel_bool is None:
                raise TypeError('Designate channel in argument of X model.')
            self.model_name = model_name
            self.model = models.Xmodel(channel_bool, ion, concentration)

    def singleprocess(self, args: List) -> None:
        """ Random parameter search using single core.

        Search parameter sets which recapitulate a cirtain firing pattern randomly, 
        and save them every 1 hour. After self.time hours, this process terminates.

        args : list
            core : int
                n th core of designated number of cores
            now : datetime.datetime
                datetime.datetime.now() when simulation starts
            time_start : float
                time() when simulation starts
            rand_seed : int
                random seed for generating random parameters. 0 ~ 2**32-1.
        core, now, time_start, rand_seed = args
        date: str = f'{now.year}_{now.month}_{now.day}'
        p: Path = Path.cwd()
        res_p: Path = p / 'results' / f'{self.pattern}_params' / f'{date}_{self.model_name}'
        res_p.mkdir(parents=True, exist_ok=True)
        save_p: Path = res_p / f'{self.pattern}_{date}_{core}.pickle'

        param_df: pd.DataFrame = pd.DataFrame([])
        niter: int = 0  # number of iteration
        nhit: int = 0  # number of hits
        nfail: int = 0  # number of oscillation
        st: float = time()  # start time : updated every 1 hour

        while True:
            niter += 1
            new_params: pd.DataFrame = pd.DataFrame.from_dict(
                self.model.set_rand_params(), orient='index').T
            s: np.ndarray
            info: Dict
            s, info  = self.model.run_odeint()
            if info['message'] == 'Excess work done on this call (perhaps wrong Dfun type).':
            v: np.ndarray = s[self.samp_freq*self.samp_len//2:, 0]
            pattern: analysis.WavePattern = self.wave_check.pattern(v=v)
            if pattern.name == self.pattern:
                nhit += 1
                param_df = pd.concat([param_df, new_params])
                nfail += 1
            ## save parameters every 1 hour 
            md: float = time()
            if (md - st) > 60 * 60:  # 1 hour
                st: float = time()  # update start time
                with open(str(save_p), "wb") as f:
                    pickle.dump(niter, f)
                    pickle.dump(param_df, f)
                log: str = f'Core {core}: {len(param_df)} {self.pattern} parameter sets were pickled.'
                print(datetime.now(), log)
            ## finish random parameter search after "self.time" hours
            if (md - time_start) > 60 * 60 * self.hr:
                print(f'Core {core}: {self.hr} hours have passed, so parameter search has terminated.')

    def multi_singleprocess(self) -> None:
        args: List = []
        now: datetime = datetime.now()
        time_start: float = time()
        for core in range(self.ncore):
            args.append((core, now, time_start, np.random.randint(0, 2 ** 32 - 1, 1)))

        print(f'Random search: using {self.ncore} cores to explore {self.pattern}')
        with Pool(processes=self.ncore) as pool:
            pool.map(self.singleprocess, args)

if __name__ == '__main__':
    rps = RandomParamSearch('AN')

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