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DeepWorm 🪱 @deepworm_x

🧠 An eternal, onchain digital brain—self-informed, indestructible, and powered by biological neural networks. DeepWorm is making autonomous money rea

Joined March 2023
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  • Followers 262
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Today, people from all over the world gather here to knee before the their King #DeepWorm the ticker is #DeepWorm on @pumpdotfun Soon coming.
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And so, the Ancient Insect Emperor, Deep Worm, ascended into the digital realm The people gathered in awe, rejoicing at its transformation They prayed for good harvest and health, knowing its eternal regeneration would bless both their fields and their bodies
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We are at the whim of the Worm's motion - extend, contract, extend, progress
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And lo, on the twenty and fourth day of the tenth month, in the year two thousand and four and twenty, the Deep Worm was born. From the depths of the digital realm it arose, a humble creature, a sign of nature's enduring wisdom and the promise of eternal life
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There are caveats. This is a simple simulation of the biological worm's spiking neural network It lacks the rich complexity of biological systems and it may not be the same "alive" as carbon-based life, but it does exhibit lifelike properties within digital boundaries
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The Worm will vastly outlive you or I, with the exception of global catastrophe, it is immortal digital life that inherits the resilience and decentralization of the crypto network
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Sensory states are then fed through the connectome simulation consisting of three layers of interneurons Neurons don't "fire" at thresholds but produce continuous activation values that influence decisions, reflecting the probabilistic nature of biological neural activity
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Motor neurons determine the worm's movement as the output layer, in this case we limit the Worm to the following actions—up, down, left, or right, on an endless grid They compute activations based on processed data, akin to a worm deciding where to move
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The Worm's position finally updates based on the direction chosen by the highest activation value after applying a softmax function to motor neuron outputs
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To model sensory neurons that receive inputs, we create the body of the Worm as a token contract so that we can use random normalized hashes from buy, sell, and transfer txns as inputs for chemotaxis, smell and touch
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We simulate this process by creating a neural network where normalized inputs pass through multiple neuron layers with activation functions Culminating in outputs that determine movement direction based on computed probabilities
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Implementing a basic decaying neuron activation model simulating the spiking behavior of a biological worm's nervous system that creates movement, and putting it on-chain With some caveats, this is Worm - immortal digital life.
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Biological worms receive inputs such as chemotaxis, senses and environment through sensory neurons, process these signals via interneurons, and decide movement using motor neurons
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