- LRNN. We build a one-layer RNN model. For one-step forecasting, we use the hidden dimension 1024 without weight decay on all four datasets. For multi-step forecasting, the hidden dimension is 2048 with a weight decay rate of  $10^{-4}$  for M and C datasets and  $10^{-5}$  for A and B datasets. All models are trained with the initial learning rate  $10^{-4}$  decaying by 0.95 every 50 epochs.
- RNNf [47]. RNN_f is composed of a single-layer GRU. For one-step forecasting, the hidden dimension of GRU is 1024 with a weight decay rate  $10^{-4}$  on M and C datasets and no weight decay for A and B datasets. For multi-step forecasting, we use hidden dimensions as 2048 on A and B datasets and 1024 on M and C datasets and set the weight decay rate to 0. The initial learning rate is  $10^{-4}$ , which decays by 0.95 every 50 epochs in all cases.
- RNN PSID [56]. We experiment with RNN PSID with the behavior-related hidden state of RNN PSID being 64 and the neuron-related hidden state being 512. The initial learning rate is  $10^{-4}$ . No weight decay is used in all cases.
- NDT [78]. The NDT model we use is a three-layer decoder-based Transformer. On A and B datasets, the attention dimension of the Transformer is 1024 for both multi-step and one-step forecasting. The attention dimension on M and C datasets is 512 and 256 for one and multi-step forecasting, respectively. Weight decay is set to 0 for one-step forecasting on four datasets. For multi-step forecasting, weight decay is  $10^{-4}$  on the M and C datasets and  $10^{-5}$  on A and B datasets. For all cases, we set the initial learning rate  $10^{-4}$ , which decays by 0.95 every 50 epochs in all cases.
- STNDT [37]. STNDT model we use has 10 layers of spatial-temporal blocks, each with one spatial attention Transformer and one temporal attention Transformer. The spatial Transformer has a hidden dimension as the temporal context length and the temporal attention has the dimension as the length of input neurons. We use the weight decay  $10^{-4}$  for multi-step forecasting,  $10^{-5}$  for one-step forecasting. We set the initial learning rate as  $10^{-4}$  decaying by 0.95 every 50 epochs in all cases.
-  $TERN^{1}$  [47]. We experiment TERN as a one-layer GRU followed by a Transformer layer, the hidden state of both GRU and Transformer is 1024, and the model is trained with an initial learning rate  $10^{-4}$  decaying by 0.95 every 50 epochs. The weight decay rate is  $10^{-6}$  for M and C datasets for both one-step and multi-step forecasting. Weight decay on A and B datasets is 0 and  $10^{-4}$  and for one-step and multi-step forecasting.
- GWNet [76]. We use three layers GWNet where each layer has two blocks, including the three CNN kernels for dilation, residual, and skip convolution for A and B datasets. Before generating the future neural signals, there is one end CNN layer and one read-out CNN layer. The channel size is 64 for residual and dilatation CNN, 128 for skip CNN, and 64 for end CNN on A and B datasets. For M and C datasets, the channel size for residual, dilation, skip, and end CNNs are 128, 128, 256, and 512, respectively. To be noticed, for M and C datasets, each layer of GraphWaveNet has three blocks. The initial learning rate is  $5 \times 10^{-4}$  decaying by 0.95 every 50 epochs. No weight decay is applied on A and B datasets, and  $10^{-7}$  weight decay rate on M and C datasets. Kernel size is 2 for all CNN layers,
- DCRNN [40]. DCRNN we use is built as two diffusion convolutional layers, and each layer performs 2 diffusion steps. The hidden dimension of each diffusion layer is 64 for A and B datasets and 128 for M and C datasets, no weight decay is used. The learning rate is  $5 \times 10^{-4}$  decaying by 0.95 every 50 epochs.
- Graphs4mer [66]. We use two variants of Graphs4mer, (i) with GRU (GS4-G) for temporal encoding, (ii)) with structured state space model (GS4-S), to perform the temporal encoding for all four datasets. The hidden dimension size is 256 on A and B datasets with a weight decay rate of  $1e^{-5}$ , and 128 on M and C datasets with a weight decay rate of 0. The learning rate is  $5 \times 10^{-4}$ , decaying by 0.95 every 50 epochs.
- AMAG. In a one-step forecasting scenario, we use single-layer GRU as a basic component for TR and TE with a 64-dimensional hidden size. For multi-step forecasting, we use Transformer layer as the basic component for TE and TR with hidden size 64. Sample-dependent matrix is computed using MLP taking the concatenated features from a pair of nodes as input, as used in