Neural Development Simulation

The Neuron Growing Simulator is a graphical simulation tool that models the development and connection of neural cells at a cellular level. It uses basic biological principles such as soma, dendrites, axons, and nucleus formation to visualize a growing neural network in real-time.

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Features

• Simulation of neural structures including nucleus, soma, dendrites, axons, and synaptic terminals
• Energy distribution and threshold-based growth control
• Self-organizing network expansion through terminal connections
• Random mutation-based nucleus creation
• Real-time visualization and signal tracking
• Optional experimental branching behavior

Simulation Elements

Nucleus (N)

The nucleus is the central structure of every neuron. It can expand and generate a surrounding soma shell. With enough energy, it spreads radially into neighboring cells.

Soma (S)

The soma forms the outer shell around the nucleus. It acts as a starting point for dendrite formation and may develop into a new nucleus under certain conditions.

Dendrites (D)

Dendrites emerge from the soma and extend into adjacent free areas. They serve as the receiving arms for neural signals. Under certain conditions, dendrites may also branch.

Axons (A)

Axons grow from the soma and seek connections with dendrites to form terminals. They transfer energy in the form of signals and initiate neural communication.

Terminals (T)

Terminals connect dendrites from different origins. When a terminal is created, a link between the associated origins is stored, and an interaction signal is generated.

Parameters

• Mutation Threshold: Controls the probability of mutations during nucleus creation
• Formation Distance: Minimum distance to other nuclei required to form a new one
• Dendrite Energy Loss Threshold: Controls how much energy is lost during dendrite formation
• Connection Tightness: Controls the density of axon-dendrite connection points
• Network Expansion: Enables creation of new nuclei once sufficient synaptic connections exist
• Branching: Enables experimental dendrite branching with additional control parameters

Defining Initial Neurons

To create initial seed cells in the simulation, you can define them manually in the tempset. These cells act as the starting point for neural growth.

Each entry in the tempset list follows this structure:

[(x, y), structure_type, tribe, origin, energy]

Where:

• (x, y) are the grid coordinates of the cell (integers)
• structure_type defines the function of the cell (single character)
• tribe is a string identifier for dendritic branches
• origin is an integer ID representing the unique neuron identity
• energy is a floating-point number controlling the growth potential

Structure Types

Defines the role of the cell in the neural structure:

• N = Nucleus – The central core of a neuron, capable of generating a soma shell
• S = Soma – The body around the nucleus; serves as a base for dendrites and axons
• D = Dendrite – A branching receiver that connects to axons from other neurons
• A = Axon – A growing structure that seeks dendrites to form terminals
• T = Terminal – A connection point where dendrites and axons meet, forming communication pathways

Tribe

A four-character alphanumeric code (e.g., FQ3C) that identifies a dendritic or axonal sub-network within a neuron. Tribes help track which branches belong to which neuron and determine interaction compatibility.

Origin

A unique integer used to distinguish one neuron from another. All parts (soma, dendrites, axons, terminals) that belong to the same neuron share the same origin value.

Energy

A floating-point value that influences how far and fast a structure can grow. Typical values range between 0.0 and 1.0. For example:

• 1.0 in a nucleus enables it to expand
• Lower energy values in axons or dendrites determine how growth is distributed

Example

A single starting nucleus cell at position (100, 100) with full energy might look like this:

tempset = [[(100, 100), "N", "AB12", 0, 1.0]]

Visualization

The simulation uses a color-coded 2D grid display:

• Nucleus: #333333
• Soma: #4a4a4a
• Dendrites: #616161
• Axons: #4f4f4f
• Terminals: #8a8888

Cells are drawn onto a canvas and automatically updated. A text display in the top-left shows simulation parameters; signal tracking data is shown at the bottom center.

Interaction Data

All terminal-based connections between origins are tracked. These connections are exported to a JSON file that logs the temporal progression and signal distribution.

Requirements

• Python 3
• Tkinter for GUI rendering
• No external libraries required

Use Cases

• Educational visualization of neural growth processes
• Experimental testing of neural system rules

Notes

• The branching feature is experimental and may produce unpredictable results
• The simulation is stochastic: each run yields a unique output
• Cell size and loop interval can be adjusted to improve performance