Source code for bmtk.simulator.bionet.cell

# Copyright 2017. Allen Institute. All rights reserved
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from neuron import h
import numpy as np


pc = h.ParallelContext()  # object to access MPI methods
MPI_RANK = int(pc.id())


class Cell(object):
    """A abstract base class for any cell object.

    A base class for implementation of a cell-type objects like biophysical cells, LIF cells, etc. Do not instantiate
    a Cell object directly. Cell classes act as wrapper around HOC cell object with extra functionality for setting
    positions, synapses, and other parameters depending on the desired cell class.
    """
    def __init__(self, node, population_name, network=None):
        self._node = node
        self._network = network

        self._gid = network.gid_pool.get_gid(name=population_name, node_id=node.node_id)
        self._node_id = node.node_id
        self._props = node
        self._netcons = []  # list of NEURON network connection object attached to this cell

        self._pos_soma = []
        self.set_soma_position()

        # register the cell
        pc.set_gid2node(self.gid, MPI_RANK)

        # Load the NEURON HOC object
        self._hobj = node.load_cell()

        self._edge_props = []

    @property
    def node(self):
        return self._node

    @property
    def hobj(self):
        return self._hobj

    @property
    def gid(self):
        return self._gid

    @property
    def node_id(self):
        return self._node_id

    @property
    def group_id(self):
        return self._node.group_id

    @property
    def network_name(self):
        return self._node.network

    @property
    def netcons(self):
        return self._netcons

    @property
    def soma_position(self):
        return self._pos_soma

    def set_soma_position(self):
        positions = self._node.position
        if positions is not None:
            self._pos_soma = positions.reshape(3, 1)

    def init_connections(self):
        self.rand_streams = []
        self.prng = np.random.RandomState(self.gid)  # generate random stream based on gid

    def scale_weights(self, factor):
        for nc in self.netcons:
            weight = nc.weight[0]
            nc.weight[0] = weight*factor

    def get_connection_info(self):
        return []

    def set_syn_connections(self, edge_prop, src_node, stim=None):
        raise NotImplementedError

    def __getitem__(self, node_prop):
        return self._node[node_prop]