Setting parameters and initial states

In this tutorial, you will learn how to:

• set parameters of Jaxley models such as compartment radius or channel conductances
• set initial states
• set synaptic parameters

Here is a code snippet which you will learn to understand in this tutorial:

cell = ...  # See tutorial on Basics of Jaxley.
cell.insert(Na())

cell.set("radius", 1.0)  # Set compartment radius.
cell.branch(0).set("Na_gNa", 0.1)  # Set sodium maximal conductance.
cell.set("v", -65.0)  # Set initial voltage.

net = ...  # See tutorial on Networks of Jaxley.
fully_connect(net.cell(0), net.cell(1), IonotropicSynapse())
net.IonotropicSynapse().set("IonotropicSynapse_gS", 0.01)

In the previous two tutorials, you learned how to build single cells or networks and how to simulate them. In this tutorial, you will learn how to change parameters of such simulations.

Let’s get started!

import matplotlib.pyplot as plt
import numpy as np
import jax.numpy as jnp
from jax import jit, vmap

import jaxley as jx
from jaxley.channels import Na, K, Leak

Preface: Building the cell or network

We first build a cell (or network) in the same way as we showed in the previous tutorials:

dt = 0.025
t_max = 10.0

comp = jx.Compartment()
branch = jx.Branch(comp, nseg=2)
cell = jx.Cell(branch, parents=[-1, 0])

Setting parameters in Jaxley

To modify parameters of the simulation, you can use the .set() method. For example

cell.set("radius", 0.1)

will modify the radius of every compartment in the cell to 0.1 micrometer. You can also modify the parameters only of some branches:

cell.branch(0).set("radius", 1.0)

or even of compartments:

cell.branch(0).comp(0).set("radius", 10.0)

You can always inspect the current parameters by inspecting cell.nodes, which is a pandas Dataframe that contains all information about the cell. You can use .set() to set morphological parameters, channel parameters, synaptic parameters, and initial states. Note that Jaxley uses the same units as the NEURON simulator, which are listed here.

Setting morphological parameters

Jaxley allows to set the following morphological parameters:

• radius: the radius of the (zylindrical) compartment (in micrometer)
• length: the length of the zylindrical compartment (in micrometer)
• axial_resistivity: the resistivity of current flow between compartments (in ohm centimeter)

cell.branch(0).set("axial_resistivity", 1000.0)
cell.set("length", 1.0)  # This will set every compartment in the cell to have length 1.0.

cell.nodes

	comp_index	branch_index	cell_index	length	radius	axial_resistivity	capacitance	v
0	0	0	0	1.0	1.0	1000.0	1.0	-70.0
1	1	0	0	1.0	1.0	1000.0	1.0	-70.0
2	2	1	0	1.0	1.0	5000.0	1.0	-70.0
3	3	1	0	1.0	1.0	5000.0	1.0	-70.0

Setting channel parameters

You can also modify channel parameters (again, units are listed here). Every parameter that should be modifiable has to be defined in self.channel_params of the channel.

cell.insert(Na())
cell.branch(1).comp(0).set("Na_gNa", 0.1)  # mS/cm^2

cell.nodes

	comp_index	branch_index	cell_index	length	radius	axial_resistivity	capacitance	v	Na	Na_gNa	eNa	vt	Na_m	Na_h
0	0	0	0	1.0	1.0	1000.0	1.0	-70.0	True	0.05	50.0	-60.0	0.2	0.2
1	1	0	0	1.0	1.0	1000.0	1.0	-70.0	True	0.05	50.0	-60.0	0.2	0.2
2	2	1	0	1.0	1.0	5000.0	1.0	-70.0	True	0.10	50.0	-60.0	0.2	0.2
3	3	1	0	1.0	1.0	5000.0	1.0	-70.0	True	0.05	50.0	-60.0	0.2	0.2

Setting synaptic parameters

In order to set parameters of synapses, you have to use net.SynapseName.set(), e.g.:

from jaxley.synapses import IonotropicSynapse
from jaxley.connect import fully_connect

num_cells = 2
net = jx.Network([cell for _ in range(num_cells)])
fully_connect(net.cell(0), net.cell(1), IonotropicSynapse())

# Unlike for channels, you have to index into the synapse with `net.SynapseName`
net.IonotropicSynapse.set("IonotropicSynapse_gS", 0.1)  # nS

You can inspect synaptic parameters and states with net.edges:

net.edges

	pre_locs	post_locs	pre_branch_index	post_branch_index	pre_cell_index	post_cell_index	type	type_ind	global_pre_comp_index	global_post_comp_index	global_pre_branch_index	global_post_branch_index	IonotropicSynapse_gS	IonotropicSynapse_e_syn	IonotropicSynapse_k_minus	IonotropicSynapse_s
0	0.25	0.25	0	1	0	1	IonotropicSynapse	0	0	6	0	3	0.1	0.0	0.025	0.2

Setting initial states

Finally, you can also set initial states. These include the initial voltage v and the states of all channels and synapses (which must be defined in self.channel_states of the channel. For example:

net.set("v", -72.0)  # mV
net.IonotropicSynapse.set("IonotropicSynapse_s", 0.1)  # nS

net.nodes

	comp_index	branch_index	cell_index	length	radius	axial_resistivity	capacitance	v	Na	Na_gNa	eNa	vt	Na_m	Na_h
0	0	0	0	1.0	1.0	1000.0	1.0	-72.0	True	0.05	50.0	-60.0	0.2	0.2
1	1	0	0	1.0	1.0	1000.0	1.0	-72.0	True	0.05	50.0	-60.0	0.2	0.2
2	2	1	0	1.0	1.0	5000.0	1.0	-72.0	True	0.10	50.0	-60.0	0.2	0.2
3	3	1	0	1.0	1.0	5000.0	1.0	-72.0	True	0.05	50.0	-60.0	0.2	0.2
4	4	2	1	1.0	1.0	1000.0	1.0	-72.0	True	0.05	50.0	-60.0	0.2	0.2
5	5	2	1	1.0	1.0	1000.0	1.0	-72.0	True	0.05	50.0	-60.0	0.2	0.2
6	6	3	1	1.0	1.0	5000.0	1.0	-72.0	True	0.10	50.0	-60.0	0.2	0.2
7	7	3	1	1.0	1.0	5000.0	1.0	-72.0	True	0.05	50.0	-60.0	0.2	0.2

net.edges

	pre_locs	post_locs	pre_branch_index	post_branch_index	pre_cell_index	post_cell_index	type	type_ind	global_pre_comp_index	global_post_comp_index	global_pre_branch_index	global_post_branch_index	IonotropicSynapse_gS	IonotropicSynapse_e_syn	IonotropicSynapse_k_minus	IonotropicSynapse_s
0	0.25	0.25	0	1	0	1	IonotropicSynapse	0	0	6	0	3	0.1	0.0	0.025	0.1

Summary

You can now modify parameters of your Jaxley simulation. In the next tutorial, you will learn how to make parameter sweeps (or stimulus sweeps) fast with jit-compilation and GPU parallelization.