# Biological Basis of the Code The provided code is a computational model representing the spontaneous activity of a neural network composed of excitatory and inhibitory neurons. The model attempts to simulate aspects of biological neural circuitry, particularly those found in the cerebral cortex. ## Neuron Types ### Excitatory Neurons - **Model:** The excitatory neurons are represented using an Adaptive Exponential Integrate-and-Fire (EIF_cond_exp_isfa_ista) model, which extends the basic integrate-and-fire model to include adaptation mechanisms. - **Parameters:** The excitatory neurons are modeled as "regular-spiking" neurons, with specific biophysical parameters that reflect typical excitatory neuron properties found in the cortex: - **Capacitance (`cm`)** reflects the cell’s membrane area. - **Resting and reset potentials (`v_rest`, `v_reset`)** indicate the membrane potential to which the neuron returns after a spike. - **Threshold potential (`v_thresh`)** is the voltage level necessary to initiate a spike. - **Adaptive current constants (`a`, `b`, `tau_w`)** are used to model neuron adaptation, which is a typical property of cortical excitatory neurons. ### Inhibitory Neurons - **Model:** The inhibitory neurons are modeled as "fast-spiking" interneurons, known for their quick response times and ability to regulate excitatory activity. - **Parameters:** Key features set in these neurons are: - **Membrane properties (`cm`, `v_rest`, `v_reset`, `tau_m`)** are similar to excitatory neurons but typically lack adaptation components (`a=0`, `b=0`), reflecting their fast-spiking nature with minimal adaptation. ## Synaptic Connectivity - The model incorporates both **recurrent** and **feedforward** synaptic connections using a fixed probability of connection between neurons, which is representative of the sparse connectivity often observed in cortical networks. - **Excitatory Synapses:** Provide excitation to both excitatory and inhibitory neurons, with a smaller synaptic weight, reflecting the weaker nature of excitatory transmission as compared to inhibitory. - **Inhibitory Synapses:** Implement feedback inhibition through stronger synaptic weights, indicative of the potent effect inhibitory neurons have in controlling network dynamics. ## Network Dynamics - **Spontaneous Activity:** The model simulates spontaneous network activity by introducing external inputs as Poisson-distributed spike trains. Initially, this input ramps up to a steady state representing an ambient firing rate. - **Feedback Loops:** The model includes mutual connections between excitatory and inhibitory populations, which are crucial for maintaining balance in the network and preventing runaway excitation or dampening of activity. ## Biological Relevance - **Homeostasis and Stability:** The interplay between excitatory and inhibitory populations models cortical mechanisms for maintaining a stable network state, crucial for brain function. - **Adaptation:** The inclusion of adaptive currents in excitatory neurons reflects the biological mechanisms for sustaining activity across various stimuli without overexcitation. - **Synaptic Dynamics:** The probabilistic and scale of synaptic weights simulate the variable connections and synaptic strengths found in a natural cortical environment. This model attempts to recreate key aspects of cortical neural network function, essential for understanding how the brain processes information, maintains balance, and reacts to external inputs. These mechanisms are fundamental in many cortical phenomena, including sensory processing, attention, and homeostatic plasticity.