The code provided models a neuronal network, focusing on aspects of neuroplasticity and synaptic activity, within a simulated context that involves specific stimuli referred to as "US" (unconditioned stimulus) and "nutella" as terms unique to this simulation. The model primarily utilizes parameters and processes relevant to neuronal firing, synaptic plasticity, and physiological responses akin to real biological systems. Here's a breakdown of the biological aspects presented in the code: ### Neuronal Network and Dynamics - **Leaky Integrate-and-Fire Neurons**: The model incorporates a population of 100 neurons, each of which behaves according to a leaky integrate-and-fire mechanism. This is a common model for neuron dynamics, where the neuron's membrane potential is influenced by inputs and decays over time, mimicking biological neural behavior. The membrane potential resets after reaching a threshold, firing a spike in the process. - **Membrane and Synaptic Dynamics**: - **Membrane Time Constant (tm)**: Reflects how quickly the neuron can integrate incoming signals before firing, analogous to the biological membrane's capacitance and resistance. - **Synaptic Decay and Rise Times (td, tr)**: Model the temporal dynamics of synaptic currents, capturing how neurotransmitter effects diminish over time after a presynaptic spike. ### Synaptic Plasticity - **Plasticity Modulation**: The code implements synaptic plasticity, where synaptic strengths (weights) are modulated over time based on neuronal activity, governed by variables like `aux` and `tau_aux`. This mimics the biological process of learning through long-term potentiation or depression. - **Hebbian Plasticity Elements**: Though not named explicitly, the code integrates an adjustment mechanism for synaptic weights, which resonates with Hebbian principles ("cells that fire together, wire together"), allowing synaptic changes in response to activity patterns. ### Calcium Dynamics - **Calcium Rise and Decay Times (tcar, tcad)**: Model calcium ion concentration dynamics, critical in synaptic plasticity processes, signal transduction, and neurotransmitter release. Calcium signaling is central to the activation of various pathways that lead to changes in synaptic efficacy. ### Stimulus Parameters - **Input Stimulus ("US" and "Nutella")**: The simulation contextually activates networks in response to external stimuli, reflecting neural responses to external environmental changes, similar to how biological systems process sensory inputs. - **Stochastic Input Generation**: The code creates Poisson-distributed input spike trains, capturing the inherent variability in synaptic transmission and neuronal firing observed in biological systems. This randomness is crucial for creating realistic neural models. ### Noise and Adaptation - **Noise Inclusion (sigma)**: Introduced to simulate variability in membrane potential changes, accounting for inevitable biological noise within neurons that affect firing patterns. - **Adaptation Mechanism (w, d)**: Implements a neuronal adaptation process, where the neuron's firing response decreases with persistent activity, mimicking spike-frequency adaptation seen in real neurons under continuous stimulation. ### Analysis and Output - **Recording and Analysis**: The model records outputs like post-synaptic currents and firing rates over time, much like electrophysiological studies tracking neuronal activity. These outputs allow for post-simulation analysis including PCA (Principal Component Analysis), which aids in understanding large-scale activity patterns akin to interpreting neural ensemble dynamics in brain studies. This model broadly captures essential features of neuron behavior, synaptic interactions, and adaptive learning typical in neural circuits, distilling complex biological processes into computationally tractable elements for hypothesis testing and understanding neural phenomena.