NMDA

class braincell.synapse.NMDA(in_size, alpha1=Quantity(2., 'kHz'), beta1=Quantity(0.01, 'kHz'), alpha2=Quantity(1., 'kHz'), beta2=Quantity(0.5, 'kHz'), T=1.0)

NMDA synapse model class.

This class implements the dynamics of an NMDA-type synapse using an exponential Euler integration scheme. The synaptic conductance is updated based on presynaptic spike input and multiple time constants.

Parameters:

• in_size (int | Sequence[int] | integer | Sequence[integer]) – The input size or shape of the synapse.

• alpha1 (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – The rise rate constant for the synaptic conductance g. Defaults to 2 / ms.

• beta1 (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – The decay rate constant for the synaptic conductance g. Defaults to 0.01 / ms.

• alpha2 (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – The rise rate constant for the auxiliary variable x. Defaults to 1 / ms.

• beta2 (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – The decay rate constant for the auxiliary variable x. Defaults to 0.5 / ms.

• T (Array | ndarray | bool | number | bool | int | float | complex | Quantity | Callable) – The synaptic efficacy or scaling factor. Defaults to 1.0.

init_state(**kwargs)

Initialize the hidden states g (synaptic conductance) and x (auxiliary variable) to zeros.

reset_state(**kwargs)

Reset the hidden states g (synaptic conductance) and x (auxiliary variable) to zeros.

update(pre_spike)

Update the synaptic conductance g and auxiliary variable x based on the presynaptic spike input.

The updates follow the differential equations:

dg/dt = alpha1 * x * (1 - g) - beta1 * g braincell/dt = alpha2 * pre_spike * T * (1 - x) - beta2 * x

Parameters:

pre_spike – Presynaptic spike input.

Returns:

Updated synaptic conductance value g.