The provided code appears to be a component of a computational model simulating neuronal spiking behavior. It uses probabilistic methods to generate synthetic data related to neuronal excitability. Let's break down the biological basis for each key component:
The function rnd_spike aims to model the inter-spike intervals (ISIs) that represent the time between successive action potentials (spikes) in a neuron. This interval distribution is crucial for understanding neuronal firing patterns and capturing aspects of neural coding and signal transmission within and across networks.
Exponential Distribution:
The function utilizes an exponential distribution (exp(-t/tau)) to represent the probability density function (PDF) for these intervals. Neurons often display ISI distributions that can be approximated by exponential functions, especially for neurons operating under a Poisson-like firing process. Here, tau represents the time constant that controls the average interval length, reflecting the neuron’s excitability.
Cumulative Distribution Function (CDF): The code converts the PDF of ISIs into a cumulative distribution function (CDF). This is crucial for sampling specific interval durations probabilistically, which reflects the inherent variability in neuronal firing.
The model also incorporates an amplitude component for the neuronal spikes:
Normal Distribution:
The code uses a normal distribution for amplitudes, specified by a mean (150) and standard deviation (115). This could correspond to the variability observed in action potential amplitudes across different neurons or conditions, capturing fluctuations due to noise and synaptic input variations.
Amplitude CDF:
The amplitude is similarly converted into a CDF (cdf_a), allowing the model to generate amplitudes that match a specified distribution pattern typical in biological recordings.
This computational approach is likely inspired by studies that aim to replicate spike timing and amplitude distributions observed in neuronal recordings. The parameterization and choice of statistical distributions are informed by empirical data, for instance, the reference to Glowatski et al in the function signature suggests a foundation in experimental studies on neuronal activity.
The use of a global dt (likely the simulation time step) and other parameters highlights the function's integration into a broader simulation framework, where time resolution and external influences on excitability might be dynamically adjusted.
The code’s core biological aim is to simulate realistic neuronal firing patterns, capturing important statistical features of ISIs and spike amplitudes. These features are fundamental for understanding how neurons encode information and how patterns of spikes affect downstream neural processes.