The code provided is focused on modeling inhibitory synaptic plasticity within a recurrent neural network. This type of model is relevant in understanding how synaptic modifications affect network dynamics, particularly the balance of excitation and inhibition, which is crucial for normal brain function. ### Biological Basis #### Inhibitory Synaptic Plasticity The model incorporates a mechanism of synaptic plasticity specifically attributed to inhibitory synapses. Inhibition in the brain is largely mediated by neurons releasing gamma-aminobutyric acid (GABA), which binds to GABA receptors on target neurons. Inhibitory plasticity adjusts the strength of these inhibitory synapses and plays a critical role in maintaining the balance between excitation and inhibition, preventing excessive firing that can lead to conditions like epilepsy. #### Excitation-Inhibition Balance The balance between excitation and inhibition is fundamental to neuronal network function. Excitatory neurons, primarily using glutamate as a neurotransmitter, drive neural activity, while inhibitory neurons modulate this activity by dampening overexcitation. This balance allows for complex activity patterns necessary for functions like sensation, memory, and cognition. #### Neural Network Structure - **Excitatory and Inhibitory Neurons**: The model consists of a network with a specified ratio of excitatory to inhibitory neurons, reflecting a common structure in the cortex. - **Synaptic Dynamics**: The equations for synaptic currents involve variables such as `g_ampa` and `g_gaba`, representing conductances through AMPA receptors (excitatory) and GABA receptors (inhibitory), respectively. The dynamics follow biophysical parameters such as synaptic time constants (`tau_ampa` and `tau_gaba`), which influence how quickly synaptic currents rise and decay. #### STDP (Spike-Timing-Dependent Plasticity) The model employs a mechanism of STDP for inhibitory synapses. Spike-timing-dependent plasticity is a biological learning rule observed experimentally, where the timing of spikes between pre- and post-synaptic neurons can lead to strengthening or weakening of synaptic connections. In the code, this property is applied to inhibitory connections to adapt their strengths based on activity patterns, aiding in homeostasis and network functionality. #### Other Biological Factors - **Membrane Dynamics**: Variables like `v` (voltage), `gl` (leak conductance), and `el` (resting potential) simulate the membrane potential dynamics of neurons, capturing properties like resting potential and threshold for spiking. - **Background Current and Membrane Capacitance**: These factors simulate constant background input akin to the normal fluctuations in synaptic input naturally present in neural circuits, as well as membrane capacitance affecting how neurons integrate inputs over time. Overall, the code models how inhibitory plasticity modulates the neural network dynamics, highlighting the critical role of inhibitory neurons in maintaining stability and functionality across diverse neural circuits. This model can provide insights into how biological mechanisms might be leveraged for maintaining excitation-inhibition balance and ensuring proper neuronal functioning.