---
title: "Timothy P. Lillicrap"
type: "entity"
entity_kind: "person"
status: "hub"
canonical_name: "Timothy P. Lillicrap"
aliases: ["Tim Lillicrap","Timothy Lillicrap","T. P. Lillicrap"]
first_seen: "2026-07-08T00:00:00.000Z"
writer_model: "claude-opus-4-8"
connects_to: ["backpropagation","biological plausibility","NGRAD","deep reinforcement learning","Geoffrey Hinton"]
seek_code_commit: "21947c9"
---


Neuroscientist and machine-learning researcher, lead author of the 2020 Nature
Reviews Neuroscience review "Backpropagation and the brain" (with Adam Santoro,
Luke Marris, Colin Akerman, and [[entity-geoffrey-hinton|Geoffrey Hinton]]) — the
most careful published statement of the position that cortex may implement
backpropagation's *core principles*, not strict backprop, via NGRAD (Neural
Gradient Representation by Activity Differences). His broader machine-learning
work includes foundational contributions to deep reinforcement learning.

Matters to this vault as the bridge figure of Hinton's 2007→2022
backprop-in-the-brain arc: his co-authored 2020 paper is the mature "capacity to
approximate" pole ([[claim-brain-approximates-backprop-core-principles-ngrad]]),
cited by name at both endpoints of the arc — the 2022 Forward-Forward paper
enters "Lillicrap et al. 2020" into evidence as part of the effort it judges to
have failed ([[claim-hinton-backprop-in-brain-2007-to-2022-arc]]).

## References
- [[claim-brain-approximates-backprop-core-principles-ngrad]] — Hinton's mature position (Lillicrap et al. 2020): the brain can implement backprop's core principles, not strict backpropagation, via NGRAD
- [[claim-hinton-backprop-in-brain-2007-to-2022-arc]] — the 2007 rescue → 2022 abandonment arc, with the 2020 NGRAD paper at its midpoint
- [[moc-backpropagation-origins]]

- 2026-09-19: page created during the 2007→2022 / 1986 bridge-check promotion, which named Lillicrap "the actual bridge figure between Hinton's 2007 rescue attempt and the 2022 abandonment" ([[observation-hinton-arc-rhw-1986-cosine-bridge-is-thematic-not-causal]]). An unchecked synthesis lead from that capture — whether Lillicrap et al. 2020 cites the 1986 Nature paper specifically for its "gradient-following works so well in artificial nets" premise — would test a candidate through-line the vault has flagged but not confirmed.