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Project details

Problem Statement

Current cognitive architectures face several critical challenges in attention allocation and resource management:

Resource Management Challenges

• Excessive resource consumption in neural-symbolic systems

• Inefficient sharing of processing resources across modules

• Poor balance between specialization and flexibility

• Lack of biologically-inspired optimization mechanisms

• Absence of effective pre-allocation strategies

Module Organization Difficulties

• Inefficient module communication patterns

• Suboptimal hierarchical structures

• Poor management of information encoding units

• Limited support for information synthesis in context

• Inadequate monitoring of information flow management

Attention Control Issues

• Ineffective dynamic resource allocation

• Limited support for different representations of recallable information types

• Poor integration of symbolic and neural processing

• Inadequate novelty detection and prioritization

• Lack of optimization phases of resources allocated to architecture supporting accurate context retrieval

Solution Overview

SYNAPSE addresses these challenges through a comprehensive, biologically-inspired framework. The framework establishes a perspective of viewing attention allocation techniques in terms of resource allocation. The framework includes a methodology of evaluation and measurement of attention allocation in any system capable of attention, focusing on AI and AGI models. The framework also embodies a exempliary archetype of how to manage attention in terms of resource allocation mechanisms for optimal balance between encoding useful information and retreiving information for the current task within the appropriate context.

SYNAPSE allows for effective attention to be measurable by monitoring and assesing the resource allocation protocols of a attention-enabled system. For attention to be effective, the system attends to all available data in any given condition and chooses which information will be encoded -- or stored -- in the systems. Discovering which information is pertinent relies in part on what information will be useful in another condition. Therefore, we describe effective attention as not only the ability to effectively take information into the system but also to recall pertinant information based on the context of the condition in which the information is useful to executing some task (internal or external).

In nature, evolutionary preasure of metabolic processes and the kluge nature of relying on previous genetic expressions as a basis for the next innovation of biological constraints creates a constraint on resources. This evolutionary pressure is exhibited in the human brain as the number of neurons, the shape, connections between them, and general morphology being limited. Brains cannot get bigger and bigger infinetly because the metabolic processes cannot support the maintence of the system. In L. Andrew Coward's theory of understanding higher order cognition in terms of anatomy and physiology -- The Recommendation Architecure -- this constraint dictates how brains were pressured into evolving to be adaptable to a breadth of tasks, learning new domain specific information without forgetting previously learned informaton, synthesising knowledge quickly in appropriate contexts, utilizing information to form behiavors quickly. Most importantly in this proposals context, the presure of having limited biological units forced brains to devlope mechanisms to make information encoding and recalling information to synthesize and utilize information in a mannor somewhat optimally useful to it's cognition and behavior. The afformentioned brain properties are still challenges for AI models and desirable properties in most visions of AGI.

The premis of SYNAPSE relies on viewing information stored in some discrete unit (e.g. weights in a DNN). The number of units available to store information is thus a resource. Further, the amount of information allowed to be stored by each unit is a property of the resource allocation protocol for gaining information. How information is represented in a collection of units is thus a function of how the resources are active during encoding information and retreival.

We first develop a literature review suitble for peer review of prior attention mechanisms implementations and measurement tools in AI/AGI followed by a relevant review of Recommendation Architecture and related neuroscince to motivate the SYNAPSE approach.

Then we develop the SYNAPSE framework with a user considerate roll-out plan to increase communication effectiveness and open source peer-review. The SYNAPSE framework includes documentation, tutorials, resources, and tools for information about the SYNAPSE core framework, how to use it to evaluate attention in AI/AGI, and how developers can use it as a guide to building more effective attention mechanisms.

We will develop SYNAPSE by relating biological processes for attention from a resource allocation perspective to a equivalent components and mechanisms in AI attention mechanism, which is then extended to a general systems architecture framework. The mapping from biological to generic complex systems is based on rigorous research and established literature.

Aspects of SYNAPSE

Module Efficiency Metrics

• Information exchange ratio optimization (targeting >99.7% internal like cortical systems)

• Resource sharing effectiveness across hierarchical levels (cortical column model)

• Processing time for cross-module communications

• Module specialization efficiency based on process types

• Sleep-phase optimization cycles

Attention Allocation Metrics

• STI variation measurements aligned with hippocampal novelty detection

• Tononi Phi coefficient calculations for consciousness integration

• Receptive field adaptation monitoring

• Temporal correlation tracking across multiple timescales

• Dynamic resource pre-allocation metrics

Integration Metrics

• Cross-module information flow rates (following cortical column patterns)

• Resource utilization efficiency during active and preparation phases

• Learning transfer effectiveness across memory types

• Cognitive synergy measurements between symbolic and neural processes

• Provisional connection utilization rates

Modular Hierarchy

• Implementation of strict biological ratios for internal vs external connectivity

• Hierarchical organization following brain structure patterns

• Process-type based module organization (not feature-based)

• Dynamic connection creation based on usage patterns

• Sleep-like preparation phases for resource optimization

Memory Integration

• Support for five memory types (semantic, episodic, priming, working, procedural)

• Temporal correlation-based connection management

• Interference minimization through careful change management

• Break periods for connection optimization

• Novel experience prioritization

Resource Management

• Provisional resource allocation during optimization phases

• Dynamic resource sharing based on attention requirements

• Careful management of receptive field changes

• Pre-allocation of connections based on usage patterns

• Resource conservation through modular specialization

Implementation Approach

Phase 1: Core Architecture (Month 1)

• Establish modular hierarchy based on process types

• Implement internal/external connectivity ratios

• Create basic memory integration systems

• Set up resource management framework

Phase 2: Optimization Systems (Month 2)

• Develop sleep-like optimization phases

• Implement temporal correlation tracking

• Create provisional connection management

• Establish interference minimization systems

Phase 3: Integration (Month 3)

• Connect memory subsystems

• Implement cross-module communication

• Develop resource sharing mechanisms

• Create evaluation metrics

Phase 4: Testing and Refinement (Month 4)

• Comprehensive system testing

• Performance optimization

• Documentation completion

• Delivery preparation

Success Metrics

• Comprehensive documentation for different technical audience levels with a focus on organization and clarity

• Successful examples peer-reviewed and aligned with existing literature

• Easy to interact with and learn about the system set-up for users (i.e. user-interface)
  

• Tutorials work and have received positive community feedback

• Hyperon team verification that evaluation of attention in Hyperon with SYNAPSE is accurate, how to solve challenges in DAS with SYNAPSE, and integration plan on future hyperon attention mechanisms with SYNAPSE.

Competitive Advantages

• Based directly on proven biological principles

• Comprehensive understanding of resource allocation involved in effective memory at different levels of understanding (hardware, logic, architecture, etc.)

• Able to apply to any attention enabled system

• Scalable modular framework and evaluation across centralized or decentralized systems

• Attention to organization and clarity of documention with ability to revise and update in open-source fashion

• Strong theoretical foundation in neuroscience and AGI

This framework represents a significant advance in cognitive architecture design, incorporating key biological principles while maintaining practical implementation considerations. It allows a highly informed method of evaluating attention in such a way that evaluates the effectiveness as it relates to human-like cognition.

Open Source Licensing

Links and references

http://www.landrewcoward.com/blog-8.html

http://www.landrewcoward.com/blog-10.html

http://www.landrewcoward.com/blog-13.html

http://www.landrewcoward.com/blog-two.html