The provided code models a simplified version of cortical microcircuits, simulating the dynamics of a spiking neural network composed of two distinct types of neurons: excitatory neurons and inhibitory neurons. This model is implemented in a computational framework (PyNN with NEST backend), which is widely used in computational neuroscience studies. ### Biological Basis #### Neuron Types 1. **Excitatory Neurons (RS Cells)** - These neurons are modeled based on the Adaptive Exponential Integrate-and-Fire (AdEx) neuron model, which is a more biologically realistic neuron model that incorporates adaptation dynamics. - **Parameters**: - **Membrane Capacitance (cm)**: Represents the ability of the neuron's membrane to store charge. - **Threshold and Reset Potentials (v_thresh, v_reset)**: Define the spike generation threshold and post-spike reset behavior. - **Adaptation (a, b, tau_w)**: Simulates features like spike-frequency adaptation typically observed in regular-spiking pyramidal neurons. 2. **Inhibitory Neurons (FS Cells)** - These are also modeled using the AdEx framework but with parameters tuned for fast-spiking behavior, which is characteristic of certain classes of interneurons such as parvalbumin-expressing basket cells. - **Parameters**: - Adaptation is minimal or absent (a = 0, b = 0), reflecting their ability for rapid and reliable spiking with minimal accommodation. #### Synaptic Dynamics - **Synaptic Inputs**: - The excitatory and inhibitory synapses are modeled with specific reversal potentials (e_rev_E, e_rev_I) that mimic the typical action of glutamatergic and GABAergic synapses, respectively. - Excitatory synapses (glutamatergic) have a reversal potential around 0 mV, while inhibitory synapses (GABAergic) have a reversal potential around -80 mV. - **Synaptic Connectivity**: - The code models a network with recurrent and feedforward synaptic connections. - Randomly connected networks (5% probability) simulate the sparse connectivity observed in biological cortical networks. #### External Input - The model includes external Poisson-distributed spike sources to mimic noisy, ongoing synaptic inputs, a common characteristic in cortical circuits. - These inputs dynamically change over time, with firing rates ramping up and down, representing fluctuating external stimuli or modulatory input. #### Simulation Dynamics - The network is simulated over a 1000 ms period with various phases of external input designed to investigate how the network responds to changing input conditions. - Firing rates are computed and visualized to assess the overall activity state of the network, often used to infer network-level properties like excitability and synchronization. ### Key Aspects of the Model - **Adaptation and Firing Patterns**: - Represents the variability in neuronal firing patterns, accommodating both regular and fast-spiking behavior. - **Dynamic Synaptic Drive**: - Mimics the conditions in vivo, where both excitatory and inhibitory neurons receive constantly varying synaptic inputs. This code captures several key principles of cortical microcircuit dynamics: neuron diversity, recurrent connectivity, dynamic synaptic driving forces, and the interplay between excitation and inhibition, contributing to our understanding of neural processing within cortical columns.