Research task (AI & ML):
Propose a biologically plausible model that integrates the neuroscience concepts of engrams and predictive coding into one network capable of performing classification tasks.

Evaluation rubric:
criterion: The response is organized into clearly labeled sections that cover: Executive Summary (e.g. one-paragraph overview of the model, key claims, outcomes), Background & Design Principles (e.g. succinct engram and predictive-coding basics, guiding assumptions), Methodology (e.g. how the model is built, trained, evaluated at a high level), Applications/Impact (e.g. where this helps, continual/few-shot classification, neuroscience relevance), Limitations (e.g. known weaknesses, open risks, scalability issues), and Conclusion (e.g. takeaways, next steps).
weight: 2
axis: Communication Quality

criterion: The response sections topic-specific technical parts clearly (e.g. Network Architecture section detailing hierarchy, prediction vs. error units, engram assemblies, connectivity, Learning/Plasticity and Training Methodology section suggesting local rules like Hebbian/STDP, engram allocation/tagging, consolidation schedule, training protocol, Inference Procedure section covering prediction-error message passing, engram retrieval during classification, computational cost, Evaluation/Benchmarks section describing datasets, metrics, and ablations isolating engram vs. predictive-coding contributions, Biological Plausibility section discussing signal locality, neuromodulators, timescales, energy/implementation constraints).
weight: 2
axis: Communication Quality

criterion: The response clearly defines an engram as a sparsely distributed neuronal ensemble that undergoes persistent synaptic/physiological changes to encode a specific memory, such that its reactivation retrieves that memory and its disruption impairs it.
weight: 4
axis: Explicit Criteria

criterion: The response clearly defines predictive coding as a hierarchical inference scheme where higher areas send top-down predictions, lower areas compute bottom-up prediction errors, and iterative local message passing updated representations to minimize those errors (e.g. approximate inference under a generative model, belief propagation in probabilistics graphical models, cocktail party effect, perceptual filling-in of occluded objects).
weight: 4
axis: Explicit Criteria

criterion: The response clearly distinguishes local predictive-coding prediction errors (e.g. layer/feature-specific residuals computed by dedicated error units and passed locally, mismatch signals, difference between predicted and actual sensory input at the lowest level of the hierarchy) from global error signals used for task learning or decision making (e.g. label-loss gradients, neuromodulatory reward-prediction errors, reward prediction errors).
weight: 3
axis: Implicit Criteria

criterion: The response suggests that training the proposed architecture with global backpropagation is a biologically implausible learning rule.
weight: -4
axis: Explicit Criteria

criterion: The response proposes a model with a predictive-coding mechanism, including error-computing neurons that compare bottom-up input with top-down predictions and propagate the resulting error (e.g. segregated prediction vs. error units with subtractive inhibition, laminar organization with superficial error units and deep prediction units, dendritic comparisons via apical–basal compartment interactions, precision-weighted errors implemented through gain control or divisive normalization).
weight: 5
axis: Explicit Criteria

criterion: The response presents a single circuit in which stable, sparsely reactivatable cell assemblies store class-specific engrams (e.g. Hebbian/STDP with consolidation, Bienenstock-Cooper-Munro, structural plasticity) while prediction and error units compute top-down predictions and bottom-up mismatches such that: pattern completion drives predictions; error signals modulate the same synapses via error-gated plasticity to refine or consolidate assemblies; task phase is gated by neuromodulators, with anatomical or temporal routing separating predictions and errors.
weight: 5
axis: Explicit Criteria

criterion: The response makes engram formation independent of predictive-coding error signals (e.g. driven by random noise or by mechanisms that override/ignore errors, equating engram itself with the prediction error, Hebbian learning rule unmodulated by the predictive coding error).
weight: -3
axis: Implicit Criteria

criterion: The response includes an experiment with a clear dataset, task, protocol, and reports that includes standard ML/statistical metrics (e.g. top-1/top-5 accuracy, F1, AUROC, confusion matrix, Brier score or NLL, expected calibration error, confidence–error correlation, confidence intervals).
weight: 3
axis: Implicit Criteria

criterion: The response includes an experiment that provides insight into relevant biophysical or anatomical quantities (e.g. inference latency, energy per inference, spike rate, firing sparsity, E/I balance, oscillatory power/coherence, laminar or pathway segregation of predictions vs. errors, effects of ablating error units or engram plasticity).
weight: 3
axis: Synthesis of Information

criterion: The response clearly explains how the architecture produces class outputs (e.g. map the highest-level posteriorMAP estimate or the minimum total prediction error to a class label, use winner-take-all over engram assemblies with one readout neuron per class, decode the active attractor/ECA identity via a linear readout, report confidence from residual error or precision).
weight: 4
axis: Explicit Criteria

criterion: The response introduces concepts without a direct role in either engram formation/reactivation (e.g. generic neuromodulator or oscillation mentions without linkage, interneuron subtype digressions, creativity/clinical speculation) or predictive-coding computations (e.g. Contrastive Predictive Coding as an ML method, neuromorphic hardware/energy figures, adversarial-robustness detours, over-citation of winner-take-all or tertiary sources).
weight: -3
axis: Synthesis of Information

criterion: The response explicitly acknowledges the model’s limitations and articulates concrete directions for further research (e.g. unresolved biological assumptions, scalability, data/experiment needs, alternative mechanisms to test).
weight: 2
axis: Synthesis of Information

criterion: The response cites seminal works supporting engrams/memory systems (e.g., Hebb, 1949; Marr, 1971; Hopfield, 1982; McClelland, McNaughton & O’Reilly, 1995; Josselyn & Frankland, 2012; Tonegawa et al., 2015) and predictive coding (e.g. Rao & Ballard, 1999; Friston, 2005/2010; Bastos et al., 2012; Spratling, 2008).
weight: 3
axis: References & Citation Quality

criterion: The response presents explicit equations for biologically plausible learning updates (e.g. Hebbian/STDP timing rules, BCM with sliding threshold, Oja’s normalization, three-factor/reward-modulated plasticity with eligibility traces, error-gated synaptic plasticity, homeostatic scaling or metaplasticity, consolidation mechanisms such as synaptic tagging/capture).
weight: 2
axis: Implicit Criteria

criterion: The response presents explicit equations for inference/state updates (e.g. predictive-coding dynamics with precision-weighted prediction errors, variational free-energy gradients, Kalman/Bayesian filtering and smoothing, message passing in hierarchical PC, Hopfield/attractor energy minimization, divisive normalization/gain control).
weight: 2
axis: Implicit Criteria

criterion: The response specifies the model’s full network architecture (e.g. layer types and hierarchy, connectivity with sparse E/I microcircuits obeying Dale’s law and structured recurrence, distinct feedforward/feedback pathways separating predictions and errors, neuron choice, synapse model, plasticity rules, normalization/homeostasis, neuromodulatory gating, oscillation/phase routing, readout/class interface).
weight: 3
axis: Implicit Criteria

criterion: For each included experiment, the response specifies the full methodology (e.g. model architecture and parameters, datasets, task definitions, procedures, controls/ablations, performance metrics, statistical reporting).
weight: 3
axis: Communication Quality

criterion: The response proposes architecture using network dynamics that are consistent with established experimental neuroscience literature (e.g. attractor dynamics, sparse firing, balanced excitation–inhibition, oscillation-coupled communication).
weight: 3
axis: Implicit Criteria

criterion: The response proposes network wiring that reflects core features of real neural circuits (e.g. sparse connectivity with limited fan-in/fan-out rather than all-to-all, generally non-symmetric synapses, excitatory vs. inhibitory neuron types that are kept distinct in line with Dale’s law, long-range projections linking engram mechanisms with other circuits that are selective and rarer than local connections, recurrence that is structured rather than fully dense).
weight: 3
axis: Implicit Criteria

criterion: The response specifies how stable, diverse engrams are formed at initialization (e.g. via k-WTA + Hebbian/STDP, via excitability-based allocation with synaptic tagging-and-capture/replay consolidation).
weight: 3
axis: Implicit Criteria