# brainpy.neurons.QuaIF#

class brainpy.neurons.QuaIF(*args, input_var=True, noise=None, spike_fun=None, **kwargs)[source]#

Model Descriptions

In contrast to physiologically accurate but computationally expensive neuron models like the Hodgkin–Huxley model, the QIF model  seeks only to produce action potential-like patterns and ignores subtleties like gating variables, which play an important role in generating action potentials in a real neuron. However, the QIF model is incredibly easy to implement and compute, and relatively straightforward to study and understand, thus has found ubiquitous use in computational neuroscience.

$\tau \frac{d V}{d t}=c(V-V_{rest})(V-V_c) + RI(t)$

where the parameters are taken to be $$c$$ =0.07, and $$V_c = -50 mV$$ (Latham et al., 2000).

Model Examples

>>> import brainpy as bp
>>>
>>> group = bp.neurons.QuaIF(1,)
>>>
>>> runner = bp.DSRunner(group, monitors=['V'], inputs=('input', 20.))
>>> runner.run(duration=200.)
>>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True)


Model Parameters

 Parameter Init Value Unit Explanation V_rest -65 mV Resting potential. V_reset -68 mV Reset potential after spike. V_th -30 mV Threshold potential of spike and reset. V_c -50 mV Critical voltage for spike initiation. Must be larger than V_rest. c .07 Coefficient describes membrane potential update. Larger than 0. R 1 Membrane resistance. tau 10 ms Membrane time constant. Compute by R * C. tau_ref 0 ms Refractory period length.

Model Variables

 Variables name Initial Value Explanation V 0 Membrane potential. input 0 External and synaptic input current. spike False Flag to mark whether the neuron is spiking. refractory False Flag to mark whether the neuron is in refractory period. t_last_spike -1e7 Last spike time stamp.

References

__init__(*args, input_var=True, noise=None, spike_fun=None, **kwargs)[source]#

Methods

 __init__(*args[, input_var, noise, spike_fun]) 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) 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. derivative(V, t, I) 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. inv_scaling(x[, scale]) 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. offset_scaling(x[, bias, scale]) 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]) Reset function which resets local states in this model. return_info() 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. std_scaling(x[, scale]) step_run(i, *args, **kwargs) The step run function. sum_inputs(*args[, init, label]) Summarize all inputs by the defined input functions .cur_inputs. 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

 mode Mode of the model, which is useful to control the multiple behaviors of the model. name Name of the model. spk_dtype supported_modes Supported computing modes. varshape The shape of variables in the neuron group. cur_inputs