The provided code models a two-area spiking network representing two cortical areas (V1 and AL) as described in the study by Meijer et al., Cell Reports 2020. This simulation is designed to mimic the communication and dynamics observed in biological neural circuits, specifically focusing on the visual cortex (V1) and the anterior lateral (AL) areas. ### Biological Basis 1. **Cortical Areas Modeled:** - **V1 (Primary Visual Cortex):** This is the first cortical area to receive visual input from the thalamus and is responsible for processing basic visual features. In the code, neurons in V1 receive a constant background input and additional stimuli during specific periods (`Tpulse`). - **AL (Anterior Lateral Area):** This area receives input from both V1 and other regions, integrating and processing visual information further. In the code, AL neurons have a smaller baseline input compared to V1 to adjust for additional input from V1. 2. **Neuronal Dynamics:** - Neurons are modeled using a single-compartment spiking neuron model characterized by parameters such as membrane potential (`v`), resting potential (`vrest`), and the threshold for spiking (`vth`). - Membrane potential updates represent the dynamics of ion channel activity, capturing how spikes and synaptic inputs change the potential across the membrane. 3. **Synaptic Transmission:** - **Short-term Synaptic Depression (STD):** Synapse strength between neurons is dynamic, and the code includes STD, represented by variables like `Use` (synaptic efficacy) and `taud` (recovery time constant). This models the decrease in synaptic strength following high activity and its subsequent recovery. - **Recurrent and External Inputs:** Neurons receive inputs both from external stimuli (`Iext`) and recurrent connections within the network, mimicking excitatory and inhibitory synaptic connections seen in the brain. 4. **Stochasticity:** - **Noise:** Biological neural systems exhibit a degree of randomness, modeled here using white noise (`xi`), which introduces variability in neural firing similar to observed neural variability in vivo. 5. **Refractory Period:** - The model includes a refractory period (`tauref`), reflecting the biological period post-spike during which the neuron cannot fire again, thus preventing immediate repeated firing. 6. **Calcium Dynamics:** - **Calcium Concentration:** Calcium signaling is crucial for neurotransmitter release and synaptic plasticity. In this model, separate calcium dynamics for V1 and AL are tracked, reflecting how the rate of calcium influx impacts overall neuronal activity and communication between areas. ### Conclusion The model encapsulates the essential dynamics of two interconnected cortical regions by simulating spiking neurons with synaptic and calcium dynamics, noise, and refractory mechanisms. It aims to reproduce the biological complexity of neural communication pathways that process and integrate visual information in the brain.