The readings first.
A concrete case at the end.

Before class: prepare the finale

1. Download the offline demo and test it on the presentation computer. It includes the experiment, prepared results, this guide and the case-study page. Calculations require no GPU or internet connection.
2. Choose Start again, then Load prepared session. Leave six rounds and both mechanisms enabled. With lesson mode on, the calculated outcomes stay hidden until you choose Show model results.
3. Keep that tab ready while presenting the readings. If browser storage is restricted, keep it open or reload the prepared session at the end.
4. The prepared results come from exactly the same model code as a live calculation. You can also compute live at the end: the simplified model is fast. There is no need to imply that it has been training throughout the talk.

Main presentation: Buckner on what memory contributes

Chapter 4, §4.4, pp. 160–168; Figure 4.2, p. 167: explain the computational division of labor between fast-learning medial-temporal and slower-learning cortical systems. Repeated, interleaved learning helps integrate new experience with existing knowledge and limit catastrophic interference.

§4.6: distinguish replaying stored experience to improve learning from consulting remembered events in episodic control. §4.8, pp. 188–189: emphasize how components with different architectures can compensate for one another’s limitations. The organizing question is what memory adds to the capacities of the whole system.

Main presentation: Boyle & Blomkvist on what models establish

§§2–3: distinguish event-memory mechanisms in AI from the richer concept of biological episodic memory. Explain why success on a benchmark does not isolate the causal contribution of memory: other architectural differences may matter.

§3.6: introduce ablations and fine-grained cross-system comparisons. §4 and Box 1: distinguish how-possibly explanations, which identify candidate mechanisms worth investigating, from how-actually explanations, which need justified correspondence to the target in relevant respects. Relevant resemblance depends on the explanatory question.

These locators use the supplied accepted manuscript: §3.6 is on printed pp. 11–12; §4 on pp. 13–15; Box 1 on p. 15. Its repository cover sheet makes the PDF page numbers one higher.

Transition: why finish with a fly?

We have discussed what memory contributes to artificial agents, and when those agents might explain biological memory. To finish, let us look at a case where researchers can constrain a model using an actual brain’s wiring and activity, then test its predictions against further biological experiments.

Open How the model was made. Give the short route: preserved tissue → microscopy → reconstructed wiring; then combine that anatomy with live-neuron recordings and explicit learning rules. It is a model built from evidence, not a scanned brain brought to life.

Keep the scope precise: this example models selected fruit-fly mushroom-body circuits. We are not demonstrating a whole-brain simulation, human episodic memory or consciousness. Its value here is as a case of mechanistic modeling and empirical testing.

Closing demonstration: about 3–5 minutes

1. Orient the audience. In the experiment overview, point from the fly to the mushroom body, the three modules, and the α3 output. Explain the task in one sentence: “Odor A predicts punishment; odor B does not.”
2. Ask a quick prediction. All three schedules receive six rounds; only the pauses differ: one, six or fifteen minutes. Ask which will leave the strongest 24-hour trace. These are simulated durations.
3. Reveal the model. Click Show model results. Read the graph as the α3 response to A minus the response to B. A more negative value means a lower response to the trained odor relative to B. It is a neural measurement, not an avoidance percentage.
4. Reveal the evidence. Click Reveal measurements from real flies. The orange points are published measurements from Huang and colleagues. Explain that the pattern was predicted and experimentally tested in their study; our class is revisiting that comparison.
5. Make one intervention. If the mechanism needs explaining, use the three stages in the memory walkthrough: γ1 reduces its brake on learning in α3; later, the α3 trace can persist after the γ1 trace fades. The illustration describes the intact circuit, independently of the experiment controls. Then disable feedback. The altered result shows the contribution of those connections within this model. The biological overlay disappears because the app does not provide matching data for this intervention. Ask what biological experiment could test the altered prediction.

If time is short, use the prepared session and show just the intact model and measurements. The purpose is to illustrate the explanatory method, not to survey every control or teach the full neuroscience model.

Close with the methodological point

Detailed biological measurements now let us build and test models of specific brain circuits. A simulation makes a possible mechanism explicit and manipulable. Evidence linking it to the real circuit helps determine whether it explains how that circuit actually works. The same demand for relevant correspondence matters when we use artificial agents to reason about human memory.

This is our synthesis of the readings and the fly study. The fly model’s interacting modules offer a useful comparison with Buckner, but they do not implement hippocampal replay, abstraction learning or a catastrophic-forgetting task. Greater biological detail alone does not settle which explanation is correct.

A final question for the class

Which evidence would turn a convincing demonstration of a possible mechanism into a well-supported explanation of the actual organism?

Possible answers include more discriminating predictions, matched interventions in the model and animal, comparisons with rival models, and explicit checks that the modeled details matter to the specific claim.

Sources

Cameron J. Buckner, From Deep Learning to Rational Machines, Chapter 4, “Memory”, pp. 142–189. Chapter.

Alexandria Boyle & Andrea Blomkvist (2024), “Elements of episodic memory: insights from artificial agents”. Article. The reading locators refer to the supplied accepted manuscript.

Cheng Huang, Junjie Luo et al. (2024), “Dopamine-mediated interactions between short- and long-term memory dynamics”, Nature 634, 1141–1149. Article and Figure 5. See the case-study page for the connectome and imaging sources. Supplied course PDFs are not included in the demo.