Resonet: A Hebbian Social Network

Overview

Resonet is a social network modeled on principles from neuroscience. Rather than relying on algorithmic feeds or follower counts, Resonet uses a decentralized, emergent system where attention flows based on collective resonance.

The core metaphor: each group chat is a neuron. When neurons communicate, they wire together. This is based on the Hebbian principle: "neurons that fire together, wire together."

Core Mechanics

Neurons (Groups)

Each group chat functions as a neuron. Members of a group can post messages, discuss, and vote on content. Groups are private by default. You join via a shared link.

Voting and Spiking

Members vote on messages they find valuable. When a message accumulates enough votes (relative to group size), it "spikes": broadcasting into the signals feed. After spiking, the neuron enters a brief refractory period before it can spike again, just like biological neurons.

Signals

Signals is a global feed of spiked messages from all neurons across the network, except your own. This creates serendipitous discovery: you encounter signals from groups you've never heard of.

From signals, you can dismiss or route them into your own neurons. Routing propagates the signal and strengthens connections between neurons.

Hebbian Wiring

When a signal is routed from one neuron to another, the connection between those neurons strengthens. Over time, neurons that frequently exchange signals become more tightly wired. Signals from strongly-connected neurons surface higher in your feed.

Feedback Loop

When your spike gets routed by others, you see it. The original message displays how many times it has been propagated across the network. This closes the loop: you can watch your signal travel and gauge how it resonated.

Sensory Neurons

Not all neurons are groups of people. Resonet has sensory neurons: automated feeds that inject external content into the network. These pull from sources like are.na channels or event calendars, providing stimulus from the outside world.

Sensory neurons spike automatically. Their signals appear in the feed like any other, where users can route them into their groups. This means the network has senses: it receives input from beyond its own membership.

Profiles

In addition to routing spikes to groups, you can route them to your personal profile. This creates a curated collection of signals that resonated with you, visible to others who visit your profile.

An Example

A book club posts about a poem. Members vote. The message spikes into the signals feed.

A stranger sees it. They route it into their philosophy group. The poem now appears in that group's chat, attributed to its origin.

The book club and philosophy group are now connected. Future spikes from either will surface higher in the other's signals feed. The network has learned something about what these neurons have in common.

Design Principles

• No algorithmic feed. Attention emerges from collective voting and network topology.
• No follower counts. Influence is distributed across group membership.
• Pure Hebbian discovery. Groups are invisible until they spike into your signals feed.
• Decentralized curation. Each group decides what's worth amplifying.

The Metaphor

In the brain, neurons don't follow each other. They form networks through repeated co-activation. Resonet applies this principle to social interaction: relevance isn't declared (follow/unfollow) but emerges through use.

The result is a network that learns: through the organic strengthening of pathways that carry meaningful signals.