brainpy module

Numerical Differential Integration

JointEq(*eqs)	Make a joint equation from multiple derivation functions.
IntegratorRunner(target[, inits, dt, ...])	Structural runner for numerical integrators in brainpy.
odeint([f, method, var_type, dt, name, ...])	Numerical integration for ODEs.
sdeint([f, g, method, dt, name, show_code, ...])	Numerical integration for SDEs.
fdeint(alpha, num_memory, inits[, f, ...])	Numerical integration for FDEs.

Building Dynamical System

DynamicalSystem([name, mode])	Base Dynamical System class.
Container(*dynamical_systems_as_tuple[, ...])	Container object which is designed to add other instances of DynamicalSystem.
Sequential(*modules_as_tuple[, name, mode])	A sequential input-output module.
Network(*ds_tuple[, name, mode])	Base class to model network objects, an alias of Container.
NeuGroup(size[, keep_size, name, mode])	Base class to model neuronal groups.
SynConn(pre, post[, conn, name, mode])	Base class to model two-end synaptic connections.
SynOut([name, target_var])	Base class for synaptic current output.
SynSTP(*args, **kwargs)	Base class for synaptic short-term plasticity.
SynLTP(*args, **kwargs)	Base class for synaptic long-term plasticity.
TwoEndConn(pre, post[, conn, output, stp, ...])	Base class to model synaptic connections.
CondNeuGroup(size[, keep_size, C, A, V_th, ...])	Base class to model conductance-based neuron group.
Channel(size[, name, keep_size, mode])	Abstract channel class.

Simulating Dynamical System

DSRunner(target[, inputs, monitors, ...])

The runner for DynamicalSystem.

Training Dynamical System

DSTrainer(target, **kwargs)	Structural Trainer for Dynamical Systems.
BPTT(target, loss_fun[, optimizer, ...])	The trainer implementing the back-propagation through time (BPTT) algorithm for training dyamical systems.
BPFF(target, loss_fun[, optimizer, ...])	The trainer implementing back propagation algorithm for feedforward neural networks.
OnlineTrainer(target[, fit_method])	Online trainer for models with recurrent dynamics.
ForceTrainer(target[, alpha])	FORCE learning.
OfflineTrainer(target[, fit_method])	Offline trainer for models with recurrent dynamics.
RidgeTrainer(target[, alpha])	Trainer of ridge regression, also known as regression with Tikhonov regularization.

Dynamical System Helpers

DSPartial(target, *args[, child_objs, ...])
NoSharedArg(target[, name])	Transform an instance of DynamicalSystem into a callable BrainPyObject \(y=f(x)\).
LoopOverTime(target[, out_vars, no_state, name])	Transform a single step DynamicalSystem into a multiple-step forward propagation BrainPyObject.