The code provided is designed to simulate aspects of cortical circuit dynamics within a computational model grounded in computational neuroscience. It focuses on replicating the behavior of different types of neurons and their interactions through synaptic connections, aiming to provide insight into network activity patterns. Below are key biological aspects modeled in the code: ### Neuron Types The code models three primary neuron types, each with distinct properties that mirror biological neuron characteristics: 1. **Regular Spiking (RS) Excitatory Neurons:** These correspond to typical pyramidal neurons that exhibit regular spiking patterns. Key parameters include the after-hyperpolarization weight (`ahpwt`), resting membrane potential (`RMP`), and threshold voltage (`VTH`). Biological phenomena of graded action potentials, refractory periods, and synaptic integration are incorporated through parameters like refract and time constants for various neurotransmitter receptors. 2. **Low-Threshold Spiking (LTS) Interneurons:** These are inhibitory neurons that are often linked with certain types of synaptic inhibition. They are characterized by specific action potential thresholds and relatively fast recovery from hyperpolarization. Attributes like `ahpwt` and `tauahp` denote the strength and duration of after-hyperpolarization currents. 3. **Fast Spiking (FS) Interneurons:** Also inhibitory, FS interneurons are indicative of certain subpopulations such as parvalbumin-positive cells. Their properties include rapid action potentials and a high firing rate, modeled through `ahpwt` and shorter refractory periods (`refrac`), reflecting their role in shaping the timing and synchrony of network activity. ### Synaptic Connections The model delineates excitatory (`AM2`, `NM2`) and inhibitory (`GA`) synaptic receptor types, corresponding to AMPA, NMDA, and GABAergic receptor-mediated currents: - **Excitatory Synapses:** `AM2` and `NM2` represent dynamic ion channel conductances for AMPA and NMDA receptors. These receptors facilitate influxes of Na+ and Ca2+ ions, crucial for generating and modulating excitatory postsynaptic potentials and plasticity. - **Inhibitory Synapses:** `GA` relates to GABA receptors, responsible for the Cl⁻ ion conductance leading to hyperpolarization of the post-synaptic neuron, thereby inhibiting neuronal firing. ### Model Dynamics The code simulates the dynamics of neuronal firing by modulating synaptic weights, external inputs, and plasticity through functions like `setwmatex()` which adjusts weights, `setcstim()` which models ongoing external inputs, and `setshock()` which handles shock stimuli meant to probe the network’s response to artificial inputs. ### Schizophrenia-Related Parameters The `schizon()` and `schizoff()` procedures likely simulate pathophysiological conditions particular to neuropsychiatric disorders (e.g., schizophrenia) by altering receptor dynamics (e.g., NMDA receptor conductances). This reflects the relevance of NMDA receptor hypofunction hypotheses in schizophrenia research. ### Experimental Manipulations The model includes functionalities to introduce shocks (simulating external stimulation or synaptic disruptions) and adjusts networks based on experiments, such as modifying synaptic weights and bursting patterns, indicating studies investigating cortical reactivity and adaptation. In summary, this hoc scripting code models the interplay of various neural elements within columns of a cortical network, focusing on synaptic dynamics, neuron firing types, and external inputs. It's useful for exploring scenarios like synaptic plasticity, inhibition-excitation balance, and network responses to perturbations that are biologically relevant for understanding cortical processing and disorders.