The provided code is part of a computational neuroscience model simulating a particular type of synaptic transmission referred to as "Mini" release, likely inspired by the stochastic nature of neurotransmitter release in synapses. Here, the code models the probabilistic aspect of synaptic vesicle release mechanisms, which are critical in neuronal communication. ### Biological Basis 1. **Miniature Postsynaptic Currents (Minis):** - In biology, 'minis' refer to the small, spontaneous postsynaptic currents that occur without presynaptic action potentials. These are caused by the random release of a single synaptic vesicle of neurotransmitter in the synaptic cleft. - The model attempts to replicate the sporadic nature of this release, possibly to understand underlying conditions such as synapse health or activity level. 2. **Stochastic Nature of Vesicle Release:** - Vesicle release is probabilistic and is influenced by various factors such as calcium concentrations, synaptic protein sensitivity, and other intrinsic properties. - The term `S = rand(1,N_pre);` implements randomness, simulating the spontaneous release probability across multiple presynaptic terminals (`N_pre`). 3. **Parameter `epsilon`:** - The variable `epsilon` might represent a minimum threshold probability for vesicle release, accounting for baseline neurotransmitter release even under low activity conditions. This aligns with the open probability of synaptic release sites. 4. **Time-Dependent Modulation:** - The model incorporates a time factor (`timeSinceSpike`) which might relate to activity-dependent facilitation or depression. In neural systems, the history of spike activity influences vesicle availability and readiness for release. - The adjustment to `timeDifference` with a minimum time ensures stability within the simulation, acknowledging real neural systems' refractory periods and preventing immediate re-release that would be biologically implausible. 5. **Frequency (`miniFreq`) Modulation:** - Modulation of frequency fits into the Hebbian plasticity framework, where synapses undergo changes in their efficiency based on firing rates. This could represent synaptic scaling where higher/lower firing rates regulate synapse strength for homeostatic balance. 6. **Exponentially Distributed Intervals:** - The code uses the exponential function to simulate waiting times between release events (`-log(S)`), which replicates the biologically relevant Poisson process. Random occurrences like these typically follow an exponential distribution regarding interval times in biological systems. ### Conclusion The piece of code captures and simulates the inherent randomness and probabilistic nature of synaptic mini-releases, informed by known biological processes such as spontaneous vesicle release, probabilistic neurotransmitter binding, and activity-dependent synaptic modulation. By utilizing parameters like `timeSinceSpike`, `miniFreq`, and `epsilon`, it suggests an effort to align computational mechanisms with biological synaptic dynamics and explore the implications of spontaneous neurotransmission activities in the context of the neural network function, such as during sleep cycles highlighted in Krishnan et al. (2016).