Develop a detailed theoretical framework translating biological hippocampus functionality into computational principles (memory encoding, retrieval, associative linking).

Functional blueprint and report, where individual features are analyzed, tested and expanded with living code. Create initial prototypes using cellular automata for self-organizing and evolving memory structures. Utilize genetic algorithms to optimize information encoding and recall mechanisms, mimicking synaptic plasticity and neurogenesis. Preliminary Integration with Knowledge Graphs: Demonstrate basic integration with large-scale knowledge graphs, enabling dynamic, associative memory functionality inspired by connectomic principles. Validate via proof-of-concept experiments, demonstrating significant memory retrieval efficiency and adaptation compared to standard graph-based models.

$50,000 USD

Be able to successfully break down Hippocampus, formalize its features and prove the basis for further development.

Integrate the hippocampal module into a preliminary large model framework, validating its novel memory capabilities in complex, evolving environments.

Expand the hippocampal system from prototype to a scalable component, integrating fractal and attractor-based dynamic structures that enable continuous learning and adaptation. Refine cellular automata and genetic algorithms for efficiency and stability in realistic cognitive tasks. Demonstrate real-time associative learning, context-aware memory encoding and retrieval, closely resembling hippocampal episodic memory and cognitive map capabilities. Validate advanced memory capabilities (pattern completion, predictive recall, memory consolidation) in complex cognitive experiments.

$70,000 USD

Successfully validate each feature and run the framework on a number of higher cognitive tasks.