# Synapses and Neurons

Freddy uses **synapses** and **neurons** to measure AI model computation and processing.

## What are Synapses?

Synapses are Aitronos' unit of measurement for AI model output generation. They represent the computational units consumed during response creation, including:

- **Output synapses** - The visible text, images, or structured data in the response
- **Reasoning synapses** - Internal thinking and reasoning computations (when using reasoning models)
- **Tool execution synapses** - Processing for function calls and tool use


## What are Neurons?

Neurons measure the input processing capacity consumed by your requests, including:

- **Input neurons** - Processing your text, images, audio, and file inputs
- **Context neurons** - Thread history and conversation context
- **System neurons** - Instructions and system prompts


## Why Synapses & Neurons?

**Unified measurement**: Track both input processing (neurons) and output generation (synapses) separately.

**Reasoning transparency**: Accounts for the model's internal reasoning process, not just visible output.

**Fairer pricing**: You're charged for the actual computational work performed by the AI.

**Granular control**: Set limits on both input context and output length independently.

## Synapse Limits

### Setting `maxOutputSynapses`

Control the maximum length and cost of responses:


```json
{
  "maxOutputSynapses": 2048,
  "inputs": [...]
}
```

**Common limits:**

- **512 synapses** - Short answers, simple queries
- **2048 synapses** - Standard responses (default)
- **4096 synapses** - Detailed explanations, complex reasoning
- **8192 synapses** - Long-form content, multi-step tasks


### What happens when limit is reached?

The model stops generating when:

1. The response is naturally complete, or
2. `maxOutputSynapses` is reached


You'll receive a `finish_reason` indicating why generation stopped:

- `stop` - Natural completion
- `length` - Hit synapse limit
- `tool_call` - Model called a function


## Usage Tracking

Every response includes synapse and neuron usage breakdown:


```json
{
  "usage": {
    "inputNeurons": 150,
    "outputSynapses": 423,
    "reasoningSynapses": 89,
    "totalNeurons": 150,
    "totalSynapses": 512
  }
}
```

Use this data to:

- **Optimize prompts** - Reduce input neurons
- **Control costs** - Monitor total consumption
- **Debug responses** - See reasoning overhead
- **Improve performance** - Identify expensive operations


## Understanding the Measurement

### Synapses (Output)

- Each piece of generated content consumes synapses
- Reasoning models use additional synapses for internal thinking
- Tool calls and function executions count toward synapses
- Limit with `maxOutputSynapses` parameter


### Neurons (Input)

- Processing text, images, audio, and files consumes neurons
- Conversation history and thread context count as neurons
- System instructions and prompts use neurons
- Automatically managed based on model context limits


## Best Practices

✅ **Start with defaults** - 2048 synapses works for most cases

✅ **Monitor usage** - Check `usage` in responses to optimize

✅ **Set limits** - Use `maxOutputSynapses` to prevent runaway costs

✅ **Optimize inputs** - Reduce neuron consumption with concise prompts

❌ **Don't over-limit** - Too low limits may cut off important responses

❌ **Don't ignore usage data** - Track patterns to optimize costs

**Related:**

- [Reasoning Models](/docs/documentation/reasoning/reasoning-models)
- [Thread Context Modes](/docs/documentation/core-concepts/thread-context-modes) - Managing neuron usage in conversations