# Biological Basis of the 'superdeep' Model Code The provided code is part of a computational neuroscience model that simulates synaptic connections in neural circuits. Here's an overview of the biological processes it aims to emulate: ## Synaptic Connections The core purpose of the code is to create synaptic connections between neurons. These synapses are crucial for communication within the brain, as they allow electrical or chemical signals to pass between neurons. The code represents this biological mechanism by establishing connections between "presynaptic" neurons, which send signals, and "postsynaptic" neurons, which receive them. ### Types of Neurons 1. **Real Neurons:** - The code models real (biological) neurons, which serve as both presynaptic and postsynaptic cells. Real neurons are cells capable of generating and propagating action potentials—electrical impulses that travel along the axon to convey information. 2. **Artificial Neurons:** - Another component of the model involves artificial neurons. These are not real biological neurons but computational constructs that can simulate specific neuronal behaviors such as rhythmic bursting. Artificial neurons may control complex dynamic processes that can influence the activity of other artificial or biological neurons within the network. ## Synaptic Properties - **Synaptic Weight:** - The synaptic weight represents the strength of the connection. In a biological context, this relates to the efficacy of neurotransmitter release and receptor response at the synapse, crucial for synaptic plasticity and learning. - **Delay (Axonal Delay):** - The code models synaptic delays, also known as axonal delays, which represent the time it takes for an electrical impulse to travel from the presynaptic neuron to the postsynaptic neuron. This is biologically realistic, as synaptic and axonal conduction is not instantaneous and varies based on factors like axon length and diameter. ## GIDs and Object References The use of Global Identifiers (GIDs) and object references in the code parallels the unique identification of neurons in anatomical circuits. Similar to how real neurons have distinct locations and connections, the model uses identifiers to specify which neurons are interacting. ## Synapse Types and Indexes The code's mention of synapse types and indexes mirrors the diversity of synapses in biological systems. Different neurons have varying types of synaptic connections, which can be excitatory or inhibitory and rely on different neurotransmitter systems. In summary, the 'superdeep' model code aims to recreate the complex interactions and properties of neural synapses, thereby providing a framework to study how information is processed and stored in neural circuits. By simulating synaptic weights and delays, the model captures critical features of neuronal communication and could be used to investigate questions related to neural network dynamics, plasticity, and neurophysiological processes.