# Biological Basis of the `PoissonMonauralVM.mod` Model ## Introduction The `PoissonMonauralVM.mod` code models a binaural inhomogeneous Poisson process as it pertains to auditory neuroscience. This model specifically simulates the neural response to sound stimuli received by two ears (ipsi- and contra-lateral), using distinct statistical distributions to represent the different auditory inputs. ## Biological Concepts ### Binaural Hearing - **Ipsi- and Contralateral Inputs**: In a biological context, 'ipsi-' refers to inputs from the same side of the body, and 'contra-' refers to inputs from the opposite side. In auditory neuroscience, binaural hearing involves processing sound information from both ears, allowing the brain to localize sound sources and process complex auditory scenes. ### Mathematical Modeling of Auditory Processing - **Von Mises Distribution for Ipsi-Lateral Input**: The von Mises distribution is used to model the ipsilateral auditory input. This statistical distribution is analogous to the normal distribution but on a circular domain (e.g., angular data). It reflects repetitive rhythmic patterns typical in signal processing of continuous, sinusoidal stimuli like sound waves. - **Uniform Distribution for Contra-Lateral Input**: A uniform distribution is employed for contralateral input. This choice represents an assumption of equal likelihood of receiving inputs across different phases or positions, suggesting a broader, possibly less synchronized input pattern from the contralateral side. ### Poisson Process in Neural Modeling - **Inhomogeneous Poisson Process**: The use of a Poisson process to model neural response rates captures the probabilistic nature of neural spike generation over time. Inhomogeneous processes further allow the model to account for time-varying stimuli, reflecting the dynamic nature of real-world auditory environments. ### Neural Tuning - **Tuning Parameters**: Parameters such as `freq`, `stimRate`, `stimPhaseIpsi`, and `stimPhaseContra` are employed to control the temporal characteristics of the input signals, which are critical in modeling phase-locking and frequency-following properties of auditory neurons. - **Kappa and Probability Adjustments**: The parameter `kappa` is a concentration parameter for the von Mises distribution, signifying how tightly an auditory stimulus is phase-locked, akin to the concept of tuning curves observed in auditory neurons. ## Conclusion The `PoissonMonauralVM.mod` code encapsulates a computational approach to simulating binaural auditory processing. It reflects how neurons integrate inputs from both ears using a combination of stochastic processes and distribution models to simulate the inherent variability and dynamics of natural auditory stimuli. The biological foundation of this code is grounded in auditory neuroscience principles, focusing on how the brain processes and interprets sound source directionality and rhythmicity through neural code emulation.