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import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque
from typing import Dict, List, Optional, Tuple
class CognitiveMemory(nn.Module):
"""Differentiable memory system with consolidation and retrieval"""
def __init__(self, context_size: int, capacity: int = 100):
super().__init__()
self.context_size = context_size
self.capacity = capacity
self.memory_queue = deque(maxlen=capacity)
# Memory importance parameters
self.importance_decay = nn.Parameter(torch.tensor(0.95))
self.consolidation_threshold = 0.7
# Memory projection layers
self.key_proj = nn.Linear(context_size, 64)
self.value_proj = nn.Linear(context_size, 64)
def add_memory(self, context: torch.Tensor, activation: float):
"""Store new memory with adaptive importance"""
importance = torch.sigmoid(torch.tensor(activation * 0.5 + 0.2))
self.memory_queue.append({
'context': context.detach(),
'importance': importance,
'age': 0.0
})
def consolidate_memories(self):
"""Memory consolidation through importance reweighting"""
for mem in self.memory_queue:
mem['importance'] *= self.importance_decay
mem['age'] += 0.1
# Remove unimportant memories
self.memory_queue = deque(
[m for m in self.memory_queue if m['importance'] > 0.2],
maxlen=self.capacity
)
def retrieve(self, query: torch.Tensor) -> torch.Tensor:
"""Attention-based memory retrieval"""
if not self.memory_queue:
return torch.zeros_like(query)
keys = torch.stack([self.key_proj(m['context']) for m in self.memory_queue])
values = torch.stack([self.value_proj(m['context']) for m in self.memory_queue])
query_proj = self.key_proj(query)
scores = F.softmax(keys @ query_proj.t(), dim=0)
return (scores * values).sum(dim=0) |