Imagine a pet that learns and adapts, not through pre-programmed routines, but through a simulated neural network. Such a creature is now a reality, thanks to an innovative project that brings the complexities of cephalopod behavior to the digital realm.

Autonomous Behavior and Learning

This digital squid isn’t just a static image; it’s a dynamic simulation driven by a neural network. the squid moves autonomously, making decisions based on his current state (hunger, sleepiness, etc.). This autonomy is key to its realistic behavior.

• Foraging Simulation: The squid implements a vision cone for food detection, simulating realistic foraging behavior. This allows it to actively seek out sustenance within its digital environment.
• Neural Network Decision-Making: The neural network can make decisions and form associations. This is crucial for learning and adapting to new situations.
• Hebbian Learning: Weights are analysed, tweaked and trained by hebbian learning algorithm. This allows the squid to strengthen connections between neurons based on experience.
• Memory Influence: Experiences from short-term and long-term memory can influence decision-making. This allows the squid to learn from past events and apply that knowledge to future decisions.
• Neurogenesis: The squid can create new neurons in response to his environment (Neurogenesis), further enhancing its adaptability.

The Science Behind the Simulation

The project utilizes a sophisticated neural network to mimic the decision-making processes of a real squid. This involves simulating various biological functions,from hunger and sleepiness to visual perception and memory. The Hebbian learning algorithm plays a crucial role in shaping the squid’s behavior over time.