Generalize recurrent-to-readout and readout-to-recurrent delays

[JesusEV]

This PR introduces modifications to handle the propagation delay of spikes propagating from the recurrent layer to the readout layer when this delay surpasses one resolution step. It also supports learning signals with delays exceeding one resolution step.

Additionally, the PR implements a series of consistency checks to ensure the proper configuration of generalized delay parameters (d_rec_out and d_out_rec) across various types of eprop neurons throughout the neural network.

Consistency Check for Recurrent and Output Neurons (d_out_rec):

• Step 1: Recurrent neurons (eprop_iaf, eprop_iaf_adapt, eprop_iaf_psc_delta) transmit a test event using a delay specified by their own d_rec_out value.
• Step 2: Output neurons (eprop_readout) receive the test event and compare the actual delay against their own d_rec_out parameter.

Connection-Specific Consistency Checks:

• Rec-Rec Connection:

  • Connections between recurrent neurons can retrieve the delay parameters (d_rec_out and d_out_rec) from the target recurrent neuron.

• Rec-Out Connection:

  • For connections from recurrent to output neurons, the delay is verified to match the d_rec_out set by the target output neuron using the check_connection function.

• Out-Rec Connection (Feedback Connection):

  • For feedback connections from output to recurrent neurons, the delay is verified to match the d_out_rec set by the target output neuron using the check_connection function.

The following diagram illustrates these consistency checks.

[akorgor]

• Could you please also adjust the eprop_supervised_classification_evidence-accumulation.py script?