The code provided is a computational model that simulates activity in a neural network using the conceptual framework of computational neuroscience. Here's a breakdown of the biological basis relevant to this code: ### Neuronal Structure and Dynamics 1. **Neuron Types**: - The code refers to different neuron types such as "RS-cell" (Regular Spiking) and "FS-cell" (Fast Spiking) in the default configuration string. These are typical classifications used to describe how neurons respond to sustained inputs. RS-cells are usually excitatory neurons (often pyramidal cells) displaying regular spiking behavior, while FS-cells typically represent inhibitory neurons like GABAergic interneurons that can fire at high frequencies. 2. **Neuronal Modeling**: - The simulation is likely using point neuron models that encapsulate the dynamics of neuron membrane potentials without necessarily incorporating spatial components (i.e., dendrites and axons). The presence of terms like "LIF" (Leaky Integrate-and-Fire) in the configuration string suggests that simplified neuron models are being used, which capture essential cellular dynamics with fewer computational demands. 3. **Membrane Dynamics**: - The simulation likely employs membrane equations that simulate the time evolution of the membrane potential concerning synaptic input and intrinsic membrane properties. Although not detailed here, these would involve ion channel kinetics controlling the flow of ions (Na+, K+, etc.) critical for action potential generation. ### Synaptic Connectivity 1. **Synaptic Inputs and Connectivity**: - Synaptic connections form the basis of communication between neurons within the network. The function `get_connectivity_and_synapses_matrix` provides the synaptic coupling matrix and properties of synaptic interactions based on specified network configurations. - The code acknowledges both excitatory and inhibitory inputs, which are crucial for balancing network activity and supporting behaviors such as oscillations, synchronization, and plasticity. 2. **Afferent Excitatory Input**: - The argument `afferent_exc_fraction` suggests tuning the fraction of external excitatory inputs the network receives. This parameter can mimic neuromodulatory or afferent influences such as sensory inputs that drive the network's activity patterns. ### External Stimulus Modeling 1. **Stimulation Protocol**: - The model applies a temporally structured external input to the network, modeled as a double Gaussian function (`double_gaussian`). This choice of input shape captures a rise and fall pattern akin to many biological processes like synaptic release or evoked potentials in response to stimuli. - Key parameters like `amp`, `t0`, `T1`, and `T2` control the amplitude, timing, and temporal dynamics of the input, allowing the user to simulate varying biological conditions and responses such as bursts of activity or sustained tonic inputs. ### Network Simulation 1. **Model Dynamics and Simulation Control**: - The network simulation is managed using the `run_simulation_with_input` function, which initializes neuronal populations and synaptic connections as specified and runs the network dynamics over time (`tstop`). - The simulation might involve various neurons (excitable elements) and synapses modulated by stochastic factors (e.g., `SEED` for reproducibility), reflecting inherent biological variability. ### Conclusion Overall, this code is designed to simulate a biological neural network's activity, emphasizing the interplay of excitatory and inhibitory processes, timed synaptic inputs, and simplified neuron models. It allows the exploration of network dynamics under different input scenarios, which is pivotal for understanding complex neural systems and their emergent properties in biological contexts.