## 4 Discussion  
We developed a modified spiking neural net implementation of current state-of-the-art movement decoders for a BMI. We find that, for input temporal spiking data, SNNs and SLSTMs perform as well or even better than current state of the art models. When comparing the SLSTM models, we found that they performed statistically better than their equivalent SNN models. This is an expected result, as SLSTM models are able to capture the temporal dynamics of neuronal activity, which non-recurrent SNNs cannot. Even the LSTM (which is a recurrent classical neural network) slightly underperformed when compared to the two SLSTMs in Dataset 1 and performed far worse than the two SLSTMs in Dataset 2 (Figure 3), showing that spiking neural networks can provide much better decoders for spiking temporal data than classical neural networks in BMI applications. What is even more surprising is the fact that the SNNs also performed similarly to the LSTM model, especially in Dataset 1, and swSNN in Dataset 2. However, this could be explained by the fact that the membrane potential also offers some form of'memory', as it  
Figure 5: (**A**) Total number of spikes in various spiking models; (**B**) firing rate of neurons in layers 1-3.  
is updated every time step from the previous timestep, so perhaps SNNs without SLSTMs can capture some temporal information, even if it is not as accurate. When we look at the differences between Dataset 1 and Dataset 2, we find that the Spike-weight based models and membrane-based models performed very differently for each dataset. In Dataset 1, the membrane-based SNN offered a slight performance increase compared to the spike-weight-based SNN; however, in Dataset 2, the spike-weight-based models performed significantly better. It is possible that the performance of spike-weight- and membrane-based SNNs may be dependent on the specific dataset used, and that both have their roles in BMI development. In Dataset 1, the joystick position can be both negative and positive, but for Dataset 2, the x and y positions of a finger were only positive. Further investigation should be carried out to further explore what specific applications both methods are best suited to, and what further modifications can be made. For example, how should negative input values be translated into spikes? Another possible improvement would be not using the same spike weighting for each variable. Figure 2 illustrates that the y position predictions are worse than the x position predictions. Perhaps the spike weighting for the y variable could be adjusted or the number of nodes representing only the y position could be changed. One other possibility is that models with a sparsity of spikes could be encouraged by using a non-linear encoding scheme that encodes common values using a lower number of spikes. We found that neural networks achieve a much better performance than Kalman filters, probably due to their ability to capture non-linearities within the input data. They are much less likely to overshoot joystick position in places where the joystick remains stationary, and while several studies have attempted to solve this issue in Kalman filters [11; 49] through additional add-ons, none of the ANNs require these additions. We further investigated alternative swSNN-SLSTM architectures. Firstly, replacing the LIF neurons with synaptic neurons made the model much worse. One possible reason for this is that SNNs have problems with local minima [50] and increasing the number of trainable parameters increases the dimensionality, leading to more local minima, which can make it harder for the model to train. On the other hand, the hyperparameter optimization options we explored, such as adding an extra SLSTM layer or changing the number of nodes in the output layer, had no noticeable effect. This is especially surprising regarding the number of nodes used, as more nodes should lead to more information flow and higher precision.