Brian 2

Step 1: Define the membrane capacitance, ion channel conductances, and reversal potentials.

Step 2: Write the differential equations for the membrane potential and channel gating variables.

Step 3: Implement the model in Brian 2 using the `NeuronGroup` and `Equations` objects.

Step 4: Run the simulation and plot the membrane potential over time.

Step 1: Define the parameters of the leaky integrate-and-fire neuron model.

Step 2: Create a `NeuronGroup` with a specified number of neurons.

Step 3: Implement synaptic connections between neurons using the `Synapses` object.

Step 4: Simulate the network activity and analyze the firing rate distributions.

Step 1: Define the STDP learning rule as a function of the time difference between pre- and post-synaptic spikes.

Step 2: Implement the learning rule in the `Synapses` object.

Step 3: Simulate the network activity with the STDP rule enabled.

Step 4: Analyze the changes in synaptic weights over time.

Step 1: Design a network architecture that promotes oscillatory activity (e.g., with inhibitory feedback loops).

Step 2: Define the neuron and synapse parameters.

Step 3: Simulate the network activity.

Step 4: Analyze the frequency and amplitude of the oscillations.

Step 1: Define the different neuron types and their connectivity patterns.

Step 2: Implement the model in Brian 2.

Step 3: Simulate the network activity in response to different stimuli.

Step 4: Compare the simulation results with experimental data from cortical recordings.