Algorithm 1: Pseudocode for Our SSA-SpiNNaker Model/Algorithm

Data: IMDB Dataset loaded from the Keras library       Result:       Positive or Negative Review based on user inputs Step 1: Import all required python modules Training dataset (train x, train y) Test Dataset (test x, test y) Step 2: Initialize the ANN model as a sequential model Define activation function Add input layer Add input layer Add output layer Step 3: Compile the model Train the model Save the model Step 4: Convert ANN weights to synaptic weights Initialize the SNN model from saved ANN model Setup simulation environment Initialize simulation parameters Step 5: foreach: Time step in the simulation duration do                                                    Foreach: For each layer in the SNN model: do                                                                               Foreach: For each neuron in the layer: do Based on incoming spikes and weights update the neuron membrane potential. If the membrane potential exceeds firing threshold, emit a spike. By activating connected synapses, propagate the spike to next later. Step 6: Collect output spikes Evaluate the performance of the model Test the model with user provided reviews