Neural Computational Model to Study the Synaptic Process Associated with Alpha Brain Waves
EEG scans of the brain are embedded with information that describes brain state and any anomalies within these scans is a clear indication of probable neurological disease. Biologically inspired computational models have enabled neuroscience researchers in understanding and predicting neurological and psychiatric disorders. This endeavor is concentrated over developing a computation model which imitates Alpha dominant Brainwaves. We choose Alpha waves since they play an integral role in various awake cognitive states. At the same time, anomalies in alpha rhythmic oscillations are indicators of several disease conditions, for example, ‘slowing’ (reduced frequency of peak power) of the alpha rhythm is a hallmark of EEG in Alzheimer’s disease.
Workflow
- Initial Voltage of Neural Mass (collection of similar neurons) is providing wherein we assume the Neural Mass is free from any stimuli. We provide the model with noise in form of changed voltages which serves as a stimulus.
- The noise causes voltage change over several neural masses (implemented in our model) due to excitation and inhibition caused by the noise.
- The neural mass voltages generated after the model has undergone all excitations and inhibition serves as the target EEG.
- The EEG recorded is verified to be alpha dominant by determining the power of different frequencies in the resultant voltage and making sure frequencies within the alpha range 4Hz-7Hz are dominant.
Knowledge Gained
- The underlying concept of Synaptic Interactions between two neural masses.
- Determining analogies between biological structures and Artifical Neural Networks.
- Implementing of Neural Networks for computing targeted voltage outputs.