Neurodynamics Simulation


For brain modeling, BrainPy provides the interface of brainpy.NeuGroup, brainpy.TwoEndConn, and brainpy.Network for convenient neurodynamics simulation.

import brainpy as bp
import numpy as np


brainpy.NeuGroup is used for neuron group modeling. User-defined neuron group models must inherit from the brainpy.NeuGroup. Let’s take the leaky integrate-and-fire (LIF) model and Hodgkin–Huxley neuron model as the illustrated examples.

LIF model

The formal equations of a LIF model is given by:

\[\begin{split}\tau_m \frac{dV}{dt} = - (V(t) - V_{rest}) + I(t) \\ \text{after}\, V(t) \gt V_{th}, V(t) =V_{rest} \, \text{last}\, \tau_{ref}\, \text{ms}\end{split}\]

where \(V\) is the membrane potential, \(V_{rest}\) is the rest membrane potential, \(V_{th}\) is the spike threshold, \(\tau_m\) is the time constant, \(\tau_{ref}\) is the refractory time period, and \(I\) is the time-variant synaptic inputs.

As stated above, the numerical integration of the differential equation in LIF model can be coded as:

def int_V(V, t, Iext, V_rest, R, tau):
    return (- (V - V_rest) + R * Iext) / tau

Then, we will define the following items to store the neuron state:

  • V: The membrane potential.

  • input: The synaptic input.

  • spike: Whether produce a spike.

  • refractory: Whether the neuron is in refractory state.

  • t_last_spike: The last spike time for calculating refractory state.

Based on these states, the updating logic of LIF model from the current time \(t\) to the next time \(t+dt\) will be coded as:

class LIF(bp.NeuGroup):
    target_backend = ['numpy', 'numba']

    def __init__(self, size, t_refractory=1., V_rest=0.,
                 V_reset=-5., V_th=20., R=1., tau=10., **kwargs):
        # parameters
        self.V_rest = V_rest
        self.V_reset = V_reset
        self.V_th = V_th
        self.R = R
        self.tau = tau
        self.t_refractory = t_refractory

        # variables
        self.t_last_spike = bp.ops.ones(size) * -1e7
        self.refractory = bp.ops.zeros(size)
        self.input = bp.ops.zeros(size)
        self.spike = bp.ops.zeros(size)
        self.V = bp.ops.ones(size) * V_reset

        super(LIF, self).__init__(size=size, **kwargs)

    def int_V(V, t, Iext, V_rest, R, tau):
        return (- (V - V_rest) + R * Iext) / tau

    def update(self, _t):
        for i in range(self.size[0]):
            if _t - self.t_last_spike[i] <= self.t_refractory:
                self.refractory[i] = 1.
                self.refractory[0] = 0.
                V = self.int_V(self.V[i], _t, self.input[i], self.V_rest, self.R, self.tau)
                if V >= self.V_th:
                    self.V[i] = self.V_reset
                    self.spike[i] = 1.
                    self.t_last_spike[i] = _t
                    self.spike[i] = 0.
                    self.V[i] = V
            self.input[i] = 0.

That’s all, we have coded a LIF neuron model.

Each NeuGroup has a powerful function: .run(). In this function, it receives the following arguments:

  • duration: Specify the simulation duration. Can be a tuple with (start time, end time). Or it can be a int to specify the duration length (then the default start time is 0).

  • inputs: Specify the inputs for each model component. With the format of (target, value, [operation]). The default operation is +, which means the input value will be added to the target. Or, the operation can be +, -, *, /, or =.

Now, let’s run it.

group = LIF(100, monitors=['V'])
[5]:, inputs=('input', 26.), report=True)
bp.visualize.line_plot(group.mon.ts, group.mon.V, show=True)
Compilation used 0.0000 s.
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Simulation is done in 0.550 s.

[6]:, 400.), report=True)
bp.visualize.line_plot(group.mon.ts, group.mon.V, show=True)
Compilation used 0.0010 s.
Start running ...
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Simulation is done in 0.542 s.


As you experienced just now, the benefit of inheriting brainpy.NeuGroup lies at the following several ways:

  • Easy way to monitor variable trajectories.

  • Powerful “inputs” support.

  • Continuous running support.

  • Progress report.

On the model definition, BrainPy endows you the fully data/logic flow control. You can define models with any data you need and any logic you want. There are little limitations/constrains on your customization. 1, you should set what computing backend do your defined model support by the keyword target_backend. 2, you should “super()” initialize the brainpy.NeuGroup with the keyword of the group size. 3, you should define the update function.

Hodgkin–Huxley model

The updating logic in the above LIF model is coded with a for loop, which is very suitable for Numba backend (because Numba is a Just-In-Time compiler, and it is good at the for loop optimization). However, for array-oriented programming languages, such as NumPy, PyTorch and TensorFlow, this coding schema is inefficient. Here, let’s use the HH neuron model as example to demonstrate how to code an array-based neuron model for general backends.

class HH(bp.NeuGroup):
    target_backend = 'general'

    def diff(V, m, h, n, t, Iext, gNa, ENa, gK, EK, gL, EL, C):
        alpha = 0.1 * (V + 40) / (1 - bp.ops.exp(-(V + 40) / 10))
        beta = 4.0 * bp.ops.exp(-(V + 65) / 18)
        dmdt = alpha * (1 - m) - beta * m

        alpha = 0.07 * bp.ops.exp(-(V + 65) / 20.)
        beta = 1 / (1 + bp.ops.exp(-(V + 35) / 10))
        dhdt = alpha * (1 - h) - beta * h

        alpha = 0.01 * (V + 55) / (1 - bp.ops.exp(-(V + 55) / 10))
        beta = 0.125 * bp.ops.exp(-(V + 65) / 80)
        dndt = alpha * (1 - n) - beta * n

        I_Na = (gNa * m ** 3.0 * h) * (V - ENa)
        I_K = (gK * n ** 4.0) * (V - EK)
        I_leak = gL * (V - EL)
        dVdt = (- I_Na - I_K - I_leak + Iext) / C

        return dVdt, dmdt, dhdt, dndt

    def __init__(self, size, ENa=50., EK=-77., EL=-54.387,
                 C=1.0, gNa=120., gK=36., gL=0.03, V_th=20.,
        # parameters
        self.ENa = ENa
        self.EK = EK
        self.EL = EL
        self.C = C
        self.gNa = gNa
        self.gK = gK
        self.gL = gL
        self.V_th = V_th

        # variables
        self.V = bp.ops.ones(size) * -65.
        self.m = bp.ops.ones(size) * 0.5
        self.h = bp.ops.ones(size) * 0.6
        self.n = bp.ops.ones(size) * 0.32
        self.spike = bp.ops.zeros(size)
        self.input = bp.ops.zeros(size)

        self.integral = bp.odeint(f=self.diff, method='rk4', dt=0.01)
        super(HH, self).__init__(size=size, **kwargs)

    def update(self, _t):
        V, m, h, n = self.integral(self.V, self.m, self.h, self.n, _t,
                                   self.input, self.gNa, self.ENa, self.gK,
                                   self.EK, self.gL, self.EL, self.C)
        self.spike = (self.V < self.V_th) * (V >= self.V_th)
        self.V = V
        self.m = m
        self.h = h
        self.n = n
        self.input[:] = 0

In HH example, all the operations (including “zeros”, “ones” and “exp”) are used from the brainpy.ops as bp.ops.zeros, bp.ops.ones and bp.ops.exp. What’s more, we set the “target_backend” as general, which means it can run on any backends. So, let’s try to run this model on various backends.

First is PyTorch.


group = HH(100, monitors=['V']), inputs=('input', 10.))
bp.visualize.line_plot(group.mon.ts, group.mon.V, show=True)

Second is NumPy.


group = HH(100, monitors=['V']), inputs=('input', 10.))

bp.visualize.line_plot(group.mon.ts, group.mon.V, show=True)

The last is Numba.


group = HH(100, monitors=['V']), inputs=('input', 10.))
bp.visualize.line_plot(group.mon.ts, group.mon.V, show=True)


For synaptic connections, BrainPy provides brainpy.TwoEndConn to help you construct the projection between pre-synaptic and post-synaptic neuron groups, and provides brainpy.connect.Connector for synaptic connectivity between pre- and post- groups.

  • The benefit of using brainpy.TwoEndConn lies at the automatical synaptic delay. The synapse modeling usually includes a delay time (typically 0.3–0.5 ms) required for a neurotransmitter to be released from a presynaptic membrane, diffuse across the synaptic cleft, and bind to a receptor site on the post-synaptic membrane. BrainPy provides register_constant_dely() for automatical state delay.

  • Another benefit of using brainpy.connect.Connector lies at the connectivity structure construction. brainpy.connect.Connector provides various synaptic structures, like “pre_ids”, “post_ids”, “conn_mat”, “pre2post”, “post2pre”, “pre2syn”, “post2syn”, “pre_slice”, and “post_slice”. Users can “requires” such data structures by calling connector.requires('pre_ids', 'post_ids', ...). We will detail this function in Synaptic Connections.

Here, let’s illustrate how to use brainpy.TwoEndConn with the AMPA synapse model.

AMPA Synapse Model

class AMPA(bp.TwoEndConn):
    target_backend = ['numpy', 'numba']

    def __init__(self, pre, post, conn, delay=0., g_max=0.10, E=0., tau=2.0, **kwargs):
        # parameters
        self.g_max = g_max
        self.E = E
        self.tau = tau
        self.delay = delay

        # connections
        self.conn = conn(pre.size, post.size)
        self.conn_mat = conn.requires('conn_mat')
        self.size = bp.ops.shape(self.conn_mat)

        # variables
        self.s = bp.ops.zeros(self.size)
        self.g = self.register_constant_delay('g', size=self.size, delay_time=delay)

        super(AMPA, self).__init__(pre=pre, post=post, **kwargs)

    def int_s(s, t, tau):
        return - s / tau

    def update(self, _t):
        self.s = self.int_s(self.s, _t, self.tau)
        for i in range(self.pre.size[0]):
            if self.pre.spike[i] > 0:
                self.s[i] += self.conn_mat[i]
        self.g.push(self.g_max * self.s)
        g = self.g.pull() -= bp.ops.sum(g, axis=0) * ( - self.E)

To define a two-end synaptic projection is very much like the NeuGroup. Users need to inherit the brainpy.TwoEndConn, and provide the “target_backend” specification, “update” function and then “super()” initialize the parent class. But what different are two aspects: 1. connection. We need construct the synaptic connectivity by “connector.requires”. 2. delay. We can register a constant delay variable by “self.register_constant_delay()”.

Here, we create a matrix-based connectivity (with the shape of (num_pre, num_post)).


And then register a delay variable “self.g” with the shape of (num_pre, num_post).


Now, let’s put the above defined HH model and AMPA synapse together to construct a network with brainpy.Network.

group = HH(10, monitors=['V', 'spike'])
syn = AMPA(pre=group, post=group, conn=bp.connect.All2All(), delay=1.5, monitors=['s'])
net = bp.Network(group, syn), inputs=(group, "input", 20.), report=True)
Compilation used 0.3254 s.
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Simulation is done in 0.611 s.

fig, gs = bp.visualize.get_figure(2, 1, 3, 8)

fig.add_subplot(gs[0, 0])
bp.visualize.line_plot(group.mon.ts, group.mon.V, legend='pre-V')

fig.add_subplot(gs[1, 0])
bp.visualize.line_plot(syn.mon.ts, syn.mon.s, legend='syn-s', show=True)

Backend-independent Property

Neurodynamics simulation in BrainPy has characteristics of high portability. It’s also backend-independent. Currently, BrainPy inherently supports the tensor-oriented backends (such like NumPy, PyTorch, TensorFlow), and the JIT compilers (like Numba). After model coding, users can switch the backend easily by using brainpy.backend.set(backend_name):

# deploy all models to NumPy backend

# deploy all models to Numba CPU backend


Moreover, customize your preferred backend is also easy.

['as_tensor', 'zeros', 'ones', 'arange', 'concatenate', 'where', 'reshape']