Ctrl+K

brainpy.neurons.FractionalFHR

brainpy.neurons.FractionalFHR#

class brainpy.neurons.FractionalFHR(size, alpha, num_memory=1000, a=0.7, b=0.8, c=-0.775, d=1.0, delta=0.08, mu=0.0001, Vth=1.8, V_initializer=OneInit(value=2.5), w_initializer=ZeroInit, y_initializer=ZeroInit, input_var=True, name=None, keep_size=False)[source]#

The fractional-order FH-R model [1].

FitzHugh and Rinzel introduced FH-R model (1976, in an unpublished article), which is the modification of the classical FHN neuron model. The fractional-order FH-R model is described as

\[\begin{split}\begin{array}{rcl} \frac{{d}^{\alpha }v}{d{t}^{\alpha }} & = & v-{v}^{3}/3-w+y+I={f}_{1}(v,w,y),\\ \frac{{d}^{\alpha }w}{d{t}^{\alpha }} & = & \delta (a+v-bw)={f}_{2}(v,w,y),\\ \frac{{d}^{\alpha }y}{d{t}^{\alpha }} & = & \mu (c-v-dy)={f}_{3}(v,w,y), \end{array}\end{split}\]

where \(v, w\) and \(y\) represent the membrane voltage, recovery variable and slow modulation of the current respectively. \(I\) measures the constant magnitude of external stimulus current, and \(\alpha\) is the fractional exponent which ranges in the interval \((0 < \alpha \le 1)\). \(a, b, c, d, \delta\) and \(\mu\) are the system parameters.

The system reduces to the original classical order system when \(\alpha=1\).

\(\mu\) indicates a small parameter that determines the pace of the slow system variable \(y\). The fast subsystem (\(v-w\)) presents a relaxation oscillator in the phase plane where \(\delta\) is a small parameter. \(v\) is expressed in mV (millivolt) scale. Time \(t\) is in ms (millisecond) scale. It exhibits tonic spiking or quiescent state depending on the parameter sets for a fixed value of \(I\). The parameter \(a\) in the 2D FHN model corresponds to the parameter \(c\) of the FH-R neuron model. If we decrease the value of \(a\), it causes longer intervals between two burstings, however there exists \(a\) relatively fixed time of bursting duration. With the increasing of \(a\), the interburst intervals become shorter and periodic bursting changes to tonic spiking.

Examples

Parameters:

  • size (int, sequence of int) – The size of the neuron group.

  • alpha (float, tensor) – The fractional order.

  • num_memory (int) – The total number of the short memory.

References

__init__(size, alpha, num_memory=1000, a=0.7, b=0.8, c=-0.775, d=1.0, delta=0.08, mu=0.0001, Vth=1.8, V_initializer=OneInit(value=2.5), w_initializer=ZeroInit, y_initializer=ZeroInit, input_var=True, name=None, keep_size=False)[source]#

Methods

__init__(size, alpha[, num_memory, a, b, c, ...])

add_aft_update(key, fun)

Add the after update into this node

add_bef_update(key, fun)

Add the before update into this node

add_inp_fun(key, fun[, label, category])

Add an input function.

clear_input()

Empty function of clearing inputs.

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

dV(V, t, w, y, I)

dw(w, t, V)

dy(y, t, V)

get_aft_update(key)

Get the after update of this node by the given key.

get_batch_shape([batch_size])

get_bef_update(key)

Get the before update of this node by the given key.

get_delay_data(identifier, delay_pos, *indices)

Get delay data according to the provided delay steps.

get_delay_var(name)

get_inp_fun(key)

Get the input function.

get_local_delay(var_name, delay_name)

Get the delay at the given identifier (name).

has_aft_update(key)

Whether this node has the after update of the given key.

has_bef_update(key)

Whether this node has the before update of the given key.

init_param(param[, shape, sharding])

Initialize parameters.

init_variable(var_data, batch_or_mode[, ...])

Initialize variables.

jit_step_run(i, *args, **kwargs)

The jitted step function for running.

load_state(state_dict, **kwargs)

Load states from a dictionary.

load_state_dict(state_dict[, warn, compatible])

Copy parameters and buffers from state_dict into this module and its descendants.

nodes([method, level, include_self])

Collect all children nodes.

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes[, node_cls])

register_implicit_vars(*variables[, var_cls])

register_local_delay(var_name, delay_name[, ...])

Register local relay at the given delay time.

reset(*args, **kwargs)

Reset function which reset the whole variables in the model (including its children models).

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

return_info()

rtype:

Union[Variable, ReturnInfo]

save_state(**kwargs)

Save states as a dictionary.

setattr(key, value)

rtype:

None

state_dict(**kwargs)

Returns a dictionary containing a whole state of the module.

step_run(i, *args, **kwargs)

The step run function.

sum_current_inputs(*args[, init, label])

Summarize all current inputs by the defined input functions .current_inputs.

sum_delta_inputs(*args[, init, label])

Summarize all delta inputs by the defined input functions .delta_inputs.

sum_inputs(*args, **kwargs)

to(device)

Moves all variables into the given device.

tpu()

Move all variables into the TPU device.

tracing_variable(name, init, shape[, ...])

Initialize the variable which can be traced during computations and transformations.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

tree_flatten()

Flattens the object as a PyTree.

tree_unflatten(aux, dynamic_values)

Unflatten the data to construct an object of this class.

unique_name([name, type_])

Get the unique name for this object.

update([x])

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self, ...])

Collect all variables in this node and the children nodes.

Attributes

after_updates

before_updates

cur_inputs

current_inputs

delta_inputs

derivative

implicit_nodes

implicit_vars

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.

supported_modes

Supported computing modes.

varshape

The shape of variables in the neuron group.
Contents