NCS-FO: The biology and technology of online planning

Planning is centrally important for everyday life. Planning is challenging to study, as it involves an internal search through future possibilities for action, in the absence of any sign this is occurring from the outside. The project will investigate two synergistic aspects of real-time planning: its biology and technology.

Current artificial intelligence methods require vast amounts of power, a large amount of training, and examination of millions of possible futures to tackle simple problems such as the next move in a turn-based board game. In contrast, mammals require very little power, little to no training, and examination of few possible futures to tackle complex problems such as where to go to hide from a stalking predator.

This project will develop a new online planning agent—a robotic “predator”—that will interact with animals trained to evade it inside a complex habitat. The robot will interact with laboratory animals whose brain activity is being recorded while they are challenged—via specially-designed complex habitats—to employ strategic behaviors in avoiding the robot.

This will test and advance theory of neural mechanisms underlying the everyday ability to plan in real time in an energy-efficient manner.

The ability to plan actions can produce much larger rewards than reactive, reflexive, or habitual behaviors. Whereas humans exhibit great proficiency in planning and executing daily movements, poor response to long-term threats shows its limits. Research on multi-step planning is in its infancy, constrained in part by behavioral tasks with low ecological validity.

Theory has advanced due to rapid progress in artificial intelligence, but most formalizations require so much computing power that real-time planning is impossible. Animals seem likely to form real-time plans in some other way. In prior work, the PIs showed that a selective benefit of visually guided planning may have facilitated the transition onto land 380 million years ago because animals can see targets much farther in air than through water.

The benefit of planning in predator-prey engagements is maximized in habitats that afford long sightlines while also providing obstacles that can hide adversaries. In these conditions, such as savanna-like habitats where hominins first emerged, planning its peak advantage. In Aim 1 of the project, this idea is modeled to identify locations of maximal planning payoff (via a network connectivity measure) and used to predict neural computation in animals.

This initial algorithm is 10,000 times faster in achieving the same survival rate of simulated prey than a leading competitor in machine learning. This enables creation of a behavioral assay in which live animals are challenged by an adversary with similar planning abilities to their own. With the principle translated into hardware, a bidirectional benefit will emerge for Aim 2.

First, neural activity—using Neuropixels probes in freely behaving mice—will be compared to the team’s theory predictions in real-time; they predict that boundary detection cells in the hippocampus and delay interval cells in entorhinal cortex are important for trimming the neurocomputational burden of plans. Second, during recordings, animals will engage with a robot that plans in real time.

This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NCS), a multidisciplinary program jointly supported by the Directorates for Biology (BIO), Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.