Learing rules
- class snngrow.base.learning.STDP.STDP(node: BaseNode, connection: Module, trace_pre: float = 0.0, trace_post: float = 0.0, tau_pre: float = 2.0, tau_post: float = 2.0)
Bases:
Module- Parameters:
node – BaseNode neuron
connection – an instance of a connection layer can have only one operation
trace_pre – the trace of a presynaptic neuron
trace_post – the trace of a postsynaptic neuron
tau_pre – the time constant of the trace of a presynaptic neuron
tau_post – the time constant of the trace of a postsynaptic neuron
- Type:
torch.nn.Module
- Type:
float
- Type:
float
- Type:
float
- Type:
float
STDP learning rule.
- cal_trace(in_spike, out_spike)
- Parameters:
in_spike – input spike
out_spike – out spike
dw – weight update
- Type:
torch.Tensor
- Type:
torch.Tensor
- Return type:
torch.nn.Module
Calculate weight update and trace.
- forward(x)
- Parameters:
x – input spike
spike – output spike
dw – weight update
- Type:
torch.Tensor
- Return type:
torch.Tensor
- Return type:
torch.nn.Module
The forward propagation process.
- reset()
Reset trace and time constant to default.
- training: bool