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SUBMIT A PRSUBMIT AN ISSUElast edit: Jan 13, 2025

Understanding Neurons

The design of Bittensor subnets is inspired by the structure of a simple neural network, with each neuron being either a miner or validator.

Neuron requirements

See minimum compute requirements for compute, memory, bandwidth and storage requirements for neurons.

Neuron Architecture Overview​

Neurons in a subnet operate within a server-client architecture:

  • Axon (Server): Miners deploy Axon servers to receive and process data from validators.
  • Dendrite (Client): Validators use Dendrite clients to transmit data to miners.
  • Synapse (Data Object): Encapsulates and structures data exchanged between neurons.

Additionally, the [Metagraph serves as a global directory for managing subnet nodes, while the Subtensor connects neurons to the blockchain.

Neuron-to-neuron communication​

Neurons exchange information by:

  • Encapsulating the information in a Synapse object.
  • Instantiating server (Axon) and client (dendrite) network elements and exchanging Synapse objects using this server-client (Axon-dendrite) protocol. See the below diagram.
Incentive Mechanism Big PictureIncentive Mechanism Big Picture

Axon​

The axon module in Bittensor API uses FastAPI library to create and run API servers. For example, when a subnet miner calls,

axon = bt.axon(wallet=self.wallet, config=self.config)

then an API server with the name axon is spawned on the subnet miner node. This axon API server receives incoming Synapse objects from subnet validators, i.e., the axon starts to serve on behalf of the subnet miner.

Similarly, in your subnet miner code you must use the axon API to spawn an API server to receive incoming Synapse objects from the subnet validators.

Dendrite​

Axon is a server instance. Hence, a subnet validator will instantiate a dendrite client on itself to transmit information to axons that are on the subnet miners. For example, when a subnet validator runs the below code fragment:

    responses: List[bt.Synapse] = await self.dendrite(
        axons=axons,
        synapse=synapse,
        timeout=timeout,
    )

then the subnet validator:

  • Has instantiated a dendrite client on itself.
  • Transmitted synapse objects to a set of axons (that are attached to subnet miners).
  • Waits until timeout expires.

Synapse​

Synapse is a data object. Subnet validators and subnet miners use Synapse data objects as the main vehicle to exchange information. The Synapse class inherits from the BaseModel of the Pydantic data validation library.

For example, in the Text Prompting Subnet, the subnet validator creates a Synapse object, called PromptingSynapse, with three fields. The fields roles and messages are set by the subnet validator during the initialization of this Prompting data object, and they cannot be changed after that. A third field, completion, is mutable. When a subnet miner receives this Prompting object from the subnet validator, the subnet miner updates this completion field. The subnet validator then reads this updated completion field.

The Neuron Metagraph​

A metagraph is a data structure that contains comprehensive information about current state of the subnet. When you inspect the metagraph of a subnet, you will find detailed information on all the nodes (neurons) in the subnet. A subnet validator should first sync with a subnet's metagraph to know all the subnet miners that are in the subnet. The metagraph can be inspected without participating in a subnet.