Python : Ciona intestinalis Larva Neural Simulation

[kimpro82]

1. Project Overview & Scope

• Objective: Chordate-Prototype Neural Circuit Multi-Sensory & Motor Response Simulator
  • Validate behavioral responses under light, mechanical, and chemical stimuli by integrating NetPyNE neural simulation and Pygame 2D visual environments, based on the Ciona intestinalis connectome dataset from networks.skewed.de.
• Target Organism: Ciona intestinalis larva (~177 neurons)
• Data Source & License: https://networks.skewed.de/net/cintestinalis (CC BY 4.0 compliant; attribution required in research/derivatives)
• Core Tech Stack:
  • Neural Engine: NetPyNE (Synaptic connectivity and spike dynamics simulation)
  • Visualization & Control: Pygame (Pre-rasterized transparent PNG sprite rendering and real-time coordinate transformations)

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2. Detailed Test Scenarios (Sensory-Motor Test Phases)

Phase 1: Phototaxis / Shadow Response Test

• Goal: Validate directional movement when light/shadow stimuli are applied to visual receptor neurons.
• Input Mechanism: Current injection ($I_{inj}$) into sensory neurons based on the distance between virtual light coordinates and the larva's head (ocellus).
• Output & Success Criteria: Meaningful changes in tail wave frequency or movement vectors, forming a trajectory toward or away from the light source.

Phase 2: Mechanical Escape Response Test

• Goal: Trigger immediate avoidance responses (reversal/turning) upon noxious physical stimuli.
• Input Mechanism: Transient high-amplitude spike current injection into anterior papillae/sensory neurons.
• Output & Success Criteria: Inhibition of forward motor neurons and activation of the reversal circuit, resulting in directional retreat within 0.5 seconds of stimulus onset.

Phase 3: Chemotaxis Gradient Test

• Goal: Validate target-finding behavior through chemical sensory neurons within a gradient field.
• Input Mechanism: Dynamic calculation of chemical sensory neuron stimulation based on position within a 2D concentration map (matrix).
• Output & Success Criteria: Formation of a biased random walk pattern toward increasing concentration gradients, successfully reaching the target destination.

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3. Data & Architecture Pipeline

1. Data Loading & Processing:
   • Parse the skewed.de connectome dataset into Python data structures (DataFrame/NetworkX) and map them into NetPyNE nodes and synapse matrices.
2. Real-Time Synchronization Loop:
   • Synchronize Pygame's frame update rate with NetPyNE's simulation time steps.
   • Decode neural outputs (motor neuron firing rate differentials) into 2D physical vectors (velocity $V$, angular velocity $\omega$) to update the larva sprite position in real time.
3. Compliance Management:
   • Explicitly state the original data source (Skewed.de Ciona connectome) and CC BY 4.0 license in documentation and UI.