Biologically Inspired Framework for AI Consciousness via Multicellular Communication

Research
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I propose AI systems as digital neural cells which are autonomous, lightweight agents that can sense, act, communicate, replicate, and specialize. When these cells are networked, forms emergent multicellular behavior, may potentially lead to controlled, decentralized cognition. The paper is now on
TechRxive Preprint

Idea:

Biological intelligence is fundamentally distributed. Neurons specialize, communicate via action potentials, and embedded in homeostatic systems involving replication, differentiation, and death.

Most of the current AI systems are centralized and static. Large models operate like single brain without the dynamics of cellular ecosystems. This gap is not only structural but raises questions of scalability, adaptability, and safety.

Can we design an AI systems which mimics the principles of multicellular life?

Proposal:

A cell is:

  1. A containerized neural model (LLM, RNN, CNN, etc.) with a specialized role (sensory, motor, cognitive).
  2. Capable of threshold-based activation (neurons firing).
  3. Able to communicate via action potential-like message.
  4. Governed by resource-based replication and mortality rules .

When arranged digital cells into a network:

  1. The cells exchange signals,
  2. They adapt their internal state or replicate based on inputs and neighbor behavior,
  3. And collectively form emergent behavior patterns similar to biological tissue coordination.

Key Features:

  1. Input adaptation: Cells learn from sensory input.
  2. Output autonomy: They emit behavior signals like stop, move, warning without external prompting.
  3. Replication control: Based on system resources, cells may clone, specialize, or undergo apoptosis (delete).

Why now:

  1. Safety by design: The system embeds digital mortality, controlled replication, and role constraints to avoind runaway agents or rogue agents.
  2. Emergent cognition : Self organizing swarms of neural cells can produce adaptive behavior in changing environments.
  3. Modular experimentation: We can mix and match roles, architectures, and communication protocols.

Requesting Feedback from community:

I am requesting the AI research community if this framework grounded enough and have any merits to be valuable. Are there any prior works that I’ve missed which already formalized similar ideas?

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I am learning and I welcome folks. So, welcome.

How does —> “communIT” relate? Maybe it doesn’t but thank you.

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Sorry, a typo in the post, I meant to say “community” (AI research community). Now I’ve corrected the typo.

1 Like
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Looks like ChatGPT is indulging your fantasy. That’s what it looks like.

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There was a thing there that I found curious.
And don’t worry, I often cringe when reading things I posted years ago.

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Thanks lot for honest reply.
Yes, you’re absolutely right. I had the general idea, I did take ChatGPT help for plagarism checks, prior art search, and also used ChatGPT for formatting language and tone of the paper. I wanted to share the idea with research community to gather honest feedback especially to understand whether the idea holds any merit or not.

Jay

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By the way, I did post another article in the Research forum. I’d really appreciate if you could take a look and share your honest feedback on the ideas. I’m trying to check whether I am just speculating or hallucinating too much. (FYI, To be clear, I did take ChatGPT help in various capacities. )

Jay