frontier research deep techloihineuromorphicsnntorchsurrogate gradients
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Theoretical command
35% weight
Probe command of LIF, Izhikevich and adaptive neuron dynamics, STDP versus surrogate gradient training, rate versus temporal coding, and why ANN-to-SNN conversion loses accuracy at low timestep counts.
Evidence to listen for
Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
Knows which results are established and which are contested
Distinguishes their own contribution from the field's
Comfortable saying where the theory runs out
Five-point scoring guide
1
Poor
Recites terminology without understanding; cannot go one layer deeper.
2
Needs Improvement
Surface familiarity; conflates established results with speculation.
3
Satisfactory
Solid grasp of the core theory; thin at the frontier.
4
Very Good
Strong command; separates settled results from open questions.
5
Excellent
Derives surrogate gradient behaviour and membrane dynamics from first principles, and states clearly where spike-based coding beats dense activations.
02
Evaluation factor
From theory to hardware or code
30% weight
Ask what they built in snnTorch, Norse, Brian2, NEST or Lava, and whether it ran on Loihi 2, SpiNNaker or Akida silicon rather than GPU simulation.
Evidence to listen for
Has built, simulated, or run something real, not only published about it
Knows the gap between the idealised model and the actual apparatus or system
Names the practical constraint that dominates in real conditions
Can describe a result that did not match prediction
Five-point scoring guide
1
Poor
Purely theoretical; no contact with implementation.
2
Needs Improvement
Some exposure but unaware of practical constraints.
3
Satisfactory
Has implemented work; understands the main real-world limits.
4
Very Good
Strong practical record; articulate about theory-versus-reality gaps.
5
Excellent
Names deployed models with measured energy per inference, spikes per sample and latency on real neuromorphic hardware, not simulator-only results.
03
Evaluation factor
Research judgement
20% weight
Test how they chose timestep counts, neuron sparsity targets and datasets (DVS Gesture, N-MNIST, SHD), and when they abandoned a spiking approach for a conventional net.
Evidence to listen for
Chooses problems by tractability and value, not novelty alone
Knows when to abandon a line of work
Reads and evaluates others' results critically
Can say what would falsify their own approach
Five-point scoring guide
1
Poor
Chases novelty; no sense of tractability or when to stop.
2
Needs Improvement
Weak problem selection; persists past the point of value.
3
Satisfactory
Reasonable judgement within a defined programme.
4
Very Good
Selects problems well and knows when to abandon a line.
5
Excellent
Sets accuracy and energy budgets upfront, kills unpromising encodings early, and honestly states where SNNs offered no advantage.
04
Evaluation factor
Explaining it to non-specialists
15% weight
Judge how they explain event-driven computation and sparsity savings to hardware leads, product managers or grant reviewers who do not know membrane potentials.
Evidence to listen for
Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
Writes clearly
Collaborates across disciplines
Makes the case for resources in terms the audience cares about
Five-point scoring guide
1
Poor
Cannot communicate outside their specialism.
2
Needs Improvement
Explanation is either impenetrable or hollow.
3
Satisfactory
Adequate with technical peers; less effective with lay audiences.
4
Very Good
Explains clearly to specialists and non-specialists alike.
5
Excellent
Translates spike sparsity into power, latency and cost figures a non-specialist can act on, without diluting the technical claim.
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