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capture promoted 2026-07-06

What Hinton argued in his 2007 talk 'How to do backpropagation in a brain' — and how it anticipates his later published critique

Topic question: What did Hinton argue in his 2007 talk, and how does it anticipate his later published critique of backprop's biological implausibility (most systematically stated in the 2022 Forward-Forward paper)?

Resolution: Confirmed. The 2007 talk slide deck was located and read directly (Tier 1, hosted on Hinton's own site). It both (a) previews specific technical objections Hinton later restates in his 2022 published critique, and (b) shows Hinton in 2007 still trying to rescue backpropagation as a brain model by proposing a mechanism — a project the 2022 paper explicitly treats as unresolved and abandons in favor of Forward-Forward. The relationship is thematic and structural (same objections, same section title, overlapping proposed mechanisms), not a direct citation: the 2022 Forward-Forward paper's reference list does not cite the 2007 talk. The 2007 talk is cited elsewhere in Hinton's own later co-authored work (see Claim 1).

Claim 1: Venue and date of the 2007 talk

Claim type: historical/biographical — Tier 3-4 acceptable when uncontested, escalated here to Tier 1 since it is load-bearing for dating the argument.

Hinton delivered "How to do backpropagation in a brain" as a talk at the NIPS 2007 Deep Learning Workshop. This is confirmed by Hinton's own later co-authored reference list:

"Hinton, G. How to do backpropagation in a brain. Deep Learning Workshop (NIPS, 2007)."

Source: Lillicrap, T.P., Santoro, A., Marris, L., Akerman, C.J. & Hinton, G. (2020). "Backpropagation and the brain." Nature Reviews Neuroscience 21, 335–346. Reference 138. Author-hosted PDF copy: https://www.cs.toronto.edu/~hinton/absps/backpropandbrain.pdf. Tier 1 (co-authored by Hinton, hosted on his own site).

The talk itself (slide deck, 16 pages) is hosted directly on Hinton's University of Toronto page and is attributed to "Geoffrey Hinton, Canadian Institute for Advanced Research & University of Toronto." Source: https://www.cs.toronto.edu/~hinton/backpropincortex2007.pdf. Tier 1 (primary, author's own venue).

Claim 2: The 2007 talk's list of "what is wrong with back-propagation"

Claim type: specific technical-mechanism claim → Tier 1–2 required. Source tier: Tier 1 (primary slide deck, Hinton's own site).

The talk opens with a slide titled "What is wrong with back-propagation?" listing three problems:

"It requires labeled training data. (fixed) — Almost all real data is unlabeled. — The brain needs to fit about 10^14 connection weights in only about 10^9 seconds. Labels cannot possibly provide enough information." "The learning time does not scale well for many hidden layers. (fixed)" "The neurons need to send two different types of signal — Forward pass: signal = activity = y — Backward pass: signal = dE/dy"

Source: Hinton, G. (2007). "How to do backpropagation in a brain." Slides 2–3. https://www.cs.toronto.edu/~hinton/backpropincortex2007.pdf.

Hinton marks the first two problems "(fixed)" — meaning he considers them solved by his own greedy layer-wise pretraining approach (train one layer of features at a time on unlabeled data, then fine-tune with backpropagation only at the end). The third problem — that a single neuron would need to carry two functionally distinct signal types (its activity, and separately the derivative of the error with respect to its input) — is presented as the open problem the rest of the talk tries to solve.

Claim 3: The 2007 talk directly quotes and rebuts neuroscientists' implausibility objection

Claim type: specific technical-mechanism / historical claim → Tier 1–2 required. Source tier: Tier 1 (primary slide deck).

Before proposing his mechanism, Hinton states the objection he is responding to, in his own words:

"Many neuroscientists think back-propagation is biologically implausible because they cannot see how neurons could possibly do it. — Chomsky used the same logic to infer that syntax is innate! — Some very good researchers have postulated inefficient algorithms that use random perturbations. — Do you really believe that evolution could not find an efficient way to adapt a feature so that it is more useful to the higher-level features? (have faith!)"

Source: Hinton, G. (2007). "How to do backpropagation in a brain." Slide 5 ("But how can the brain back-propagate through a stack of RBM's?"). https://www.cs.toronto.edu/~hinton/backpropincortex2007.pdf.

This is a striking contrast with the later position: in 2007, Hinton treats the implausibility objection as a failure of imagination on the neuroscientists' part ("have faith!"), analogous to what he characterizes as Chomsky's overreach in inferring innateness from an argument from incredulity. He goes on to propose a specific mechanism rather than concede the point.

Claim 4: The 2007 talk's proposed mechanism — temporal derivatives standing in for error derivatives

Claim type: specific technical-mechanism claim → Tier 1–2 required. Source tier: Tier 1 (primary slide deck).

Hinton's proposed solution, in his own words:

"Learning a stack of simple models, each of which is good at reconstructing its inputs, creates the conditions required to allow neurons to implement backpropagation in an elegant way. — The trick is to use temporal derivatives to represent error derivatives. — This allows the output of a neuron to represent an error derivative at the same time as it is also representing the presence or absence of a feature. — This is a big assumption about cortical representations. — Is there any evidence for it? (velocity neurons?)"

Source: Hinton, G. (2007). "How to do backpropagation in a brain." Slide 6 ("The idea"). https://www.cs.toronto.edu/~hinton/backpropincortex2007.pdf.

The mechanism: build a stack of Restricted Boltzmann Machines trained by contrastive divergence (Hinton's own 2002 algorithm), each layer learning to reconstruct the layer below it. Because each layer already does reconstruction, a small perturbation to a unit's activity representing "how the output should change" can be propagated top-down through the reconstruction weights, and the change over time in a unit's activity (its temporal derivative) stands in for the error-derivative signal backpropagation would otherwise need a separate channel to carry. Hinton explicitly flags this as "a big assumption about cortical representations" with uncertain empirical support (citing "velocity neurons" as a speculative candidate), and notes the mechanism requires symmetric weights between the forward and reconstruction (backward) connections — the same weight-symmetry requirement later formalized as the weight transport problem. He proposes that "functional symmetry" (approximate, not exact, symmetry sufficient near the data manifold) achieved by contrastive divergence learning could substitute for the exact symmetry ordinary backpropagation requires.

Claim 5: The 2022 Forward-Forward paper's published critique of backprop's biological implausibility

Claim type: specific technical-mechanism claim → Tier 1–2 required. Source tier: Tier 1 (arXiv preprint, Hinton's own single-authored paper).

Fifteen years later, in the paper introducing the Forward-Forward algorithm, Hinton opens with a section titled "1 What is wrong with backpropagation" — an explicit echo of the 2007 slide title — and states:

"As a model of how cortex learns, backpropagation remains implausible despite considerable effort to invent ways in which it could be implemented by real neurons (Lillicrap et al., 2020; Richards and Lillicrap, 2019; Guerguiev et al., 2017; Scellier and Bengio, 2017). There is no convincing evidence that cortex explicitly propagates error derivatives or stores neural activities for use in a subsequent backward pass. The top-down connections from one cortical area to an area that is earlier in the visual pathway do not mirror the bottom-up connections as would be expected if backpropagation was being used in the visual system. Instead, they form loops in which neural activity goes through about half a dozen cortical layers in the two areas before arriving back where it started."

Source: Hinton, G. (2022). "The Forward-Forward Algorithm: Some Preliminary Investigations." arXiv:2212.13345, §1, p.1. https://arxiv.org/pdf/2212.13345.

He adds a second objection about temporal processing:

"Backpropagation through time as a way of learning sequences is especially implausible. To deal with the stream of sensory input without taking frequent time-outs, the brain needs to pipeline sensory data through different stages of sensory processing and it needs a learning procedure that can learn on the fly."

Source: ibid., §1, p.1.

Note that the four works Hinton cites as "considerable effort" that failed to make backpropagation-in-the-brain convincing include Lillicrap et al. 2020 ("Backpropagation and the brain") — the paper on which Hinton himself is a co-author, and which itself cites his 2007 talk as an early proposal in exactly this tradition (see Claim 1). Hinton is here citing his own prior published attempt (and collaborators' related attempts) as part of the "considerable effort" that has not resolved the implausibility.

Synthesis: how the 2007 talk anticipates the 2022 critique

The two texts are not connected by direct citation — the 2022 Forward-Forward paper's bibliography does not list the 2007 talk. The connection is thematic and structural, visible by comparing the primary texts directly:

Further leads

Capture produced: 2026-07-06. Primary sources fetched directly via extract_pdf and read in full: https://www.cs.toronto.edu/~hinton/backpropincortex2007.pdf (16 pp.), https://arxiv.org/pdf/2212.13345 (16 pp.), https://www.cs.toronto.edu/~hinton/absps/backpropandbrain.pdf (12 pp.). All three URLs verified live 2026-07-06 (successfully downloaded and parsed).

· batch run, 2026-07-06 · raw markdown