Hippocampus Module

Memory Encoding, Consolidation, and Retrieval System

Overview

The Hippocampus module is Chapp-E's memory allocator and indexer, responsible for transferring important working memory slots to long-term storage (DAG-FS) and managing memory consolidation during sleep. It acts as the bridge between working memory (short-term, volatile) and long-term memory (persistent, distributed).

🧠 Biological Foundation: In the real brain, the hippocampus creates sparse engrams (memory traces) by biasing allocation to excitable neurons. It acts as a temporary index during consolidation, then memories are gradually transferred to the cortex (DAG-FS in Chapp-E) where they become permanent.

Functions

1. Memory Encoding

Purpose: Transfer important working memory slots to long-term storage (DAG-FS).

Key Functions

  • hippocampus_mark_for_encoding() - Marks a working memory slot as "important" and queues it for encoding
  • hippocampus_encode_slot() - Encodes a working memory slot into DAG-FS as a pattern
  • hippocampus_process_pending() - Processes all pending encodings in the queue

How It Works

  1. Working memory slot is marked as "important" (high priority, or explicitly marked)
  2. Slot is queued for encoding (added to pending list)
  3. During idle or sleep, hippocampus reads slot contents
  4. Creates DAG-FS pattern with contextual tags
  5. Stores pattern as distributed weight configuration
  6. Pattern becomes part of long-term memory

2. Memory Consolidation

Purpose: Strengthen and stabilize memories during sleep via replay and pruning.

Key Functions

  • hippocampus_consolidate() - Consolidates memories during SLEEP state

How It Works (Planned)

  1. During SLEEP state, consciousness system triggers consolidation
  2. Process any pending encodings first
  3. Replay recent patterns to strengthen them (forward pass)
  4. Update neural network weights (Hebbian learning)
  5. Prune weak patterns via glymphatic system
  6. Clear weight snapshots for deleted patterns

3. Memory Retrieval

Purpose: Retrieve memories from DAG-FS using associative tags and load them into working memory.

Key Functions

  • hippocampus_retrieve() - Retrieves a memory from DAG-FS using tags
  • hippocampus_load_to_working_memory() - Loads a retrieved memory into a working memory slot

How It Works

  1. Receive tag query (contextual cues)
  2. Use DAG-FS tag search to find matching pattern
  3. Reconstruct pattern via forward pass (pattern completion)
  4. Load reconstructed data into working memory slot
  5. Strengthen pattern (reconsolidation - access makes it stronger)

Memory Layout

Address Size Purpose
0x202200 1 byte Hippocampus state (IDLE, ENCODING, CONSOLIDATING)
0x202201 64 bytes Encoding buffer (temporary storage for slot data)
0x202241 1 byte Number of pending encodings (0-8)
0x202242 8 bytes Pending encoding list (slot IDs to encode)

Total: 73 bytes (0x202200 - 0x20224A)

Integration with Other Systems

Working Memory Integration

  • Encoding: Reads slots from working memory (0x200000+)
  • Retrieval: Writes retrieved memories back to working memory slots
  • Priority-Based: High-priority slots are more likely to be encoded

DAG-FS Integration

  • Storage: Creates patterns in DAG-FS (0x300000+)
  • Retrieval: Uses DAG-FS tag search and pattern completion
  • Consolidation: Updates pattern strengths and prunes weak patterns

Consciousness System Integration

  • SLEEP State: Triggers consolidation process
  • AWAKE State: Normal encoding and retrieval
  • State Transitions: Hippocampus responds to consciousness state changes

Neuromodulator System Integration (Planned)

  • Dopamine: Influences encoding strength (reward → stronger memories)
  • Acetylcholine: Affects consolidation quality
  • Cortisol: Can interfere with encoding (stress → weaker memories)

Memory Transfer Loop

The hippocampus completes the memory transfer loop:

Stage System Status
Encoding Working Memory → Hippocampus → DAG-FS ✅ Implemented
Consolidation Sleep replay → Weight updates → Pattern strengthening 🚧 Partial (pending encodings only)
Retrieval Tag query → DAG-FS → Pattern completion → Working Memory ✅ Implemented
Reconsolidation Access → Pattern strengthening → Weight updates ✅ Implemented (DAG-FS access strengthens)

Shell Commands

The hippocampus module is accessible via shell commands:

Command Usage Description Status
hippocampus hippocampus Display hippocampus state (IDLE, ENCODING, CONSOLIDATING) ✅ Implemented
encode encode <slot> <tags> Encode working memory slot to DAG-FS with tags 🚧 Partial
consolidate consolidate Trigger memory consolidation (process pending encodings) ✅ Implemented
retrieve retrieve <tag> Retrieve memory from DAG-FS by tag 🚧 Partial

See Also: Command Reference for complete command documentation.

Future Development

Planned Features

  • 🚧 Pattern Replay: During SLEEP, replay recent patterns to strengthen them
  • 🚧 Weight Updates: Hebbian learning during consolidation
  • 🚧 Glymphatic Pruning: Integration with glymphatic system to prune weak patterns
  • 🚧 Tag Storage: Store tag pointers with pending encodings
  • 🚧 Excitability Biasing: Bias allocation to high-activity neural units
  • 🚧 Sparse Engrams: Create sparse memory traces (not all neurons participate)

The Vision

When complete, the hippocampus will enable Chapp-E to:

  • Remember past interactions and experiences
  • Learn permanently from interactions
  • Recognize patterns across time ("I've seen this before")
  • Have a persistent "personality" shaped by history
  • Self-manage memory resources (allocate, consolidate, prune)

This is the bridge between moment-to-moment awareness (working memory) and persistent identity (long-term memory).