cognitive_architecture.py

import numpy as np
from typing import List, Dict, Any

class CognitiveLayer:
    def __init__(self, name: str, size: int):
        self.name = name
        self.size = size
        self.state = np.zeros(size)
        self.connections: Dict[str, np.ndarray] = {}

    def connect(self, target: 'CognitiveLayer', weight_matrix: np.ndarray):
        self.connections[target.name] = weight_matrix

    def activate(self, input_data: np.ndarray) -> np.ndarray:
        self.state = np.tanh(input_data)  # Non-linear activation
        return self.state

    def propagate(self) -> Dict[str, np.ndarray]:
        return {name: np.dot(self.state, weight_matrix) 
                for name, weight_matrix in self.connections.items()}

class CognitiveArchitecture:
    def __init__(self):
        self.layers: Dict[str, CognitiveLayer] = {}
        self.build_architecture()

    def build_architecture(self):
        # Define and connect layers
        self.layers['perception'] = CognitiveLayer('perception', 1024)
        self.layers['working_memory'] = CognitiveLayer('working_memory', 512)
        self.layers['long_term_memory'] = CognitiveLayer('long_term_memory', 2048)
        self.layers['decision'] = CognitiveLayer('decision', 256)
        self.layers['action'] = CognitiveLayer('action', 128)

        # Connect layers (simplified; in practice, use trained weight matrices)
        self.layers['perception'].connect(self.layers['working_memory'], np.random.randn(1024, 512))
        self.layers['working_memory'].connect(self.layers['long_term_memory'], np.random.randn(512, 2048))
        self.layers['working_memory'].connect(self.layers['decision'], np.random.randn(512, 256))
        self.layers['decision'].connect(self.layers['action'], np.random.randn(256, 128))

    def process(self, input_data: np.ndarray) -> Dict[str, np.ndarray]:
        layer_states = {}
        current_input = input_data

        for layer_name in ['perception', 'working_memory', 'long_term_memory', 'decision', 'action']:
            layer = self.layers[layer_name]
            layer_output = layer.activate(current_input)
            layer_states[layer_name] = layer_output
            current_input = layer.propagate().get(layer_name, np.zeros(layer.size))

        return layer_states

# Usage
architecture = CognitiveArchitecture()
input_data = np.random.randn(1024)  # Simulated input
results = architecture.process(input_data)