Biological Basis of the Code

The provided code is part of a test suite for the NEST (NEural Simulation Tool) simulator, which is used to model networks of spiking neurons. The focus of this code is on validating the neuronal connectivity functions provided by the PyNEST connect API.

Neuronal Model

The model used in this code is the Integrate-and-Fire (IAF) neuron model, denoted by "iaf_neuron". The IAF neuron is a simplified representation of a biological neuron, primarily characterized by its ability to integrate incoming synaptic inputs (i.e., synaptic currents or conductances) until a threshold is reached. Once the threshold is crossed, the neuron generates a spike (or action potential) and resets its membrane potential.

Characteristics:

• Membrane Potential Integration: The IAF neuron model integrates postsynaptic potentials linearly until a predefined threshold is surpassed.
• Spiking Behavior: Once a spike is generated, the neuron's membrane potential resets, simulating the refractory period observed in biological neurons.

Synaptic Connections

The main aim of the code is to verify the creation and configuration of connections between neurons in a network:

• Synaptic Weight: The strength of the connection between pre- and post-synaptic neurons, analogous to synaptic efficacy in biological synapses. The weight dictates how much influence an incoming spike has on the postsynaptic neuron's membrane potential.

• Synaptic Delay: Represents the neural transmission delay from the pre- to the post-synaptic neuron, which is a critical aspect of temporal dynamics in neural communication. It accounts for both axonal propagation time and synaptic processing time.

The code simulates different connectivity patterns that can be found in neural circuits:

• ConvergentConnect: Models multiple presynaptic neurons connecting to a single postsynaptic neuron, analogous to many-to-one synaptic convergence found in the brain.

• DivergentConnect: Simulates one presynaptic neuron connecting to multiple postsynaptic neurons, similar to one-to-many synaptic divergence.

Error Handling and Biological Relevance

The code also tests for inappropriate connections, such as connecting neurons to devices like voltmeters or spike detectors, which would be biologically nonsensical:

• Unexpected Event: Attempting to connect a spike detector (a readout device) to an intrinsic part of a neural network (a neuron) is biologically analogous to trying to send a signal where it is meant to be measured.

Overall, the tests in this script help verify that the NEST simulator accurately models elementary biological neuronal behaviors and synaptic connectivity, which are foundational to understanding more complex neural network functions in the brain.