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Curriculum catalogue

The catalogue contains 80 short modules across 14 families. Each entry records audience, duration, prerequisites, implementation mode, learning outcomes, reusable outputs and a specific interpretive boundary.

Specifications, not released handouts

The catalogue is a curriculum architecture. Do not treat nominal durations, settings or activities as validated classroom protocols until the module has passed the development and validation process described in Development status.

Browse by family

Family Scope Modules
FND — Foundations and orientation Foundations and orientation 4
NPH — Neurophysiology Neurophysiology 7
EPH — Electrophysiology and instrumentation Electrophysiology and instrumentation 6
SYN — Synapses and integration Synapses and integration 6
SEN — Sensory neuroscience Sensory neuroscience 5
NET — Networks and neural computation Networks and neural computation 7
CMP — Computational neuroscience Computational neuroscience 9
DAT — Neural-data analysis Neural-data analysis 8
STA — Statistics Statistics 6
IMG — Calcium imaging Calcium imaging 5
EXT — Extracellular recording Extracellular recording 5
MET — Experimental methodology Experimental methodology 6
EPI — Epistemology and scientific reasoning Epistemology and scientific reasoning 4
OSC — Outreach and educator practice Outreach and educator practice 2

Implementation relationship codes

Code Relationship Use
1 Direct physical implementation Board controls and observable electronic behaviour are the main learning object.
2 Direct implementation using board and GUI Board behaviour is recorded, visualised or controlled through the GUI.
3 Hybrid board and Jupyter implementation Students acquire Spikeling data and analyse it in a notebook.
4 Multi-board implementation Two or more boards are physically connected into a circuit.
5 Recorded-dataset analysis A prepared or previously acquired dataset is the primary input.
6 GUI simulation workflow The imaging, extracellular or emulator workflow supplies synthetic data.
7 Conceptual analogy using Spikeling The platform supports a bounded analogy rather than direct biological implementation.
8 External computational comparison grounded in Spikeling data Recorded data are compared with external equations, simulations or fitted models.
9 Not presently suitable The concept should not be represented as supported by the current platform.

Curriculum taxonomy and module contract

ID Family Scope
FND Foundations and orientation First encounter, representation, signals and platform literacy.
NPH Neurophysiology Baseline, polarity, threshold, excitability and firing patterns.
EPH Electrophysiology and instrumentation Clamp modes, acquisition, noise, sampling, calibration and artefacts.
SYN Synapses and integration Signed inputs, summation, timing, decay and plasticity comparisons.
SEN Sensory neuroscience Photodiode transduction, thresholds, adaptation, tuning and detection.
NET Networks and neural computation Feedforward/recurrent motifs, inhibition, oscillation, synchrony and logic.
CMP Computational neuroscience Izhikevich equations, parameter space, dynamical systems and inference.
DAT Neural-data analysis Import, QC, events, rates, temporal features, trial analysis and decoding.
STA Statistics Variability, uncertainty, comparisons, regression, power and multiplicity.
IMG Calcium imaging Forward models, sampling, ΔF/F, indicators, ROI concepts and deconvolution.
EXT Extracellular recording Geometry, recording chain, detection, sorting and quality metrics.
MET Experimental methodology Questions, operationalisation, controls, uncertainty and reproducible workflow.
EPI Epistemology and scientific reasoning Evidence, model validity, causality, falsification and transparency.
OSC Outreach and educator practice Public explanation, demonstrations and facilitation.

Standard module contract

  • Identity: ID, title, family, concept statement and principal pedagogical theme.

  • Audience: level, prior knowledge, required modules and suitable context.

  • Logistics: 30/45/60 minutes, group size, boards, equipment, software and dataset requirement.

  • Learning outcomes: three to five observable outcomes.

  • Learning mode and stages: activity format plus Explore-to-Reproduce stages.

  • Spikeling relationship: controls, interfaces, preset, variables, relationship code and explicit limit.

  • Roadmap: prediction, configuration, manipulation, recording, analysis, interpretation and limitation.

  • Inputs/outputs: artefacts designed for reuse by subsequent modules.

  • Sources/connections/priority: evidence base, prerequisites, follow-ons, bundle compatibility and development priority.

Complete module index

ID Title Min Mode Priority
FND-01 First contact: make a neuron respond 30 Exploratory board activity Core module
FND-02 Neuron, model and instrument 45 Methodology exercise Core module
FND-03 The stimulus–response chain 30 Guided exploration Core module
FND-04 Signals, variables and units 45 Instructor-LED practical Core module
NPH-01 Baseline and resting state 45 Board and GUI experiment Core module
NPH-02 Depolarisation and hyperpolarisation 45 Guided experiment Core module
NPH-03 Threshold and operational rheobase 60 Board + GUI experiment Core module
NPH-04 Input–output and firing-rate curve 60 Hybrid board + Jupyter Core module
NPH-05 Refractory and recovery behaviour 60 Hybrid experiment High-priority extension
NPH-06 Latency and adaptation 60 Hybrid board + Jupyter Core module
NPH-07 Atlas of neuronal firing diversity 60 Board + GUI experiment Core module
EPH-01 Current clamp: command and response 45 Guided experiment Core module
EPH-02 Model voltage clamp and controller current 60 Board + GUI experiment Advanced specialised module
EPH-03 Analogue, digital and event signals 45 Instructor-LED practical Core module
EPH-04 Sampling and timebase validation 60 Methodology + acquisition exercise Core module
EPH-05 Noise, filtering and signal-to-noise 60 Hybrid experiment Core module
EPH-06 Calibration, dynamic range and artefacts 60 Methodology experiment High-priority extension
SYN-01 From presynaptic event to postsynaptic current 45 Multi-board guided experiment Core module
SYN-02 Excitation, inhibition and signed gain 60 Board + GUI experiment Core module
SYN-03 Temporal summation 60 Hybrid multi-board experiment Core module
SYN-04 Spatial summation with two inputs 60 Multi-board experiment High-priority extension
SYN-05 Coincidence detection and input timing 60 Multi-board + Jupyter Advanced specialised module
SYN-06 Static synapses versus short-term plasticity 60 Simulation comparison Dataset-only/high-priority extension
SEN-01 Photodiode sensory transduction 45 Guided exploration Core module
SEN-02 Sensory threshold and dynamic range 60 Hybrid experiment Core module
SEN-03 ON and OFF response analogies 45 Board + GUI experiment High-priority extension
SEN-04 Sensory adaptation and recovery 60 Hybrid experiment High-priority extension
SEN-05 Reliability, tuning and signal detection 60 Hybrid experiment/Jupyter Advanced specialised module
NET-01 Feedforward excitation 45 Multi-board circuit Core module
NET-02 Feedforward inhibition 60 Multi-board experiment High-priority extension
NET-03 Recurrent excitation and persistence 60 Multi-board circuit Advanced specialised module
NET-04 Reciprocal and lateral inhibition 60 Multi-board experiment High-priority extension
NET-05 Disinhibition 60 Multi-board circuit Advanced specialised module
NET-06 Oscillation, central-pattern-generation analogy and synchrony 60 Multi-board + Jupyter Advanced specialised module
NET-07 Neural logic and temporal computation 60 Multi-board challenge High-priority extension
CMP-01 The Izhikevich equations as an executable model 60 Simulation comparison Core module
CMP-02 Meaning of a, b, c and d 60 Board + simulation Core module
CMP-03 Preset phenotype atlas 60 Hybrid experiment High-priority extension
CMP-04 Parameter sweeps and sensitivity 60 Jupyter + board validation Advanced specialised module
CMP-05 Class 1 and Class 2 excitability 60 Hybrid experiment Advanced specialised module
CMP-06 Integrators and resonators 60 Hybrid experiment Advanced specialised module
CMP-07 Rebound, bistability and bursting regimes 60 Board + simulation Advanced specialised module
CMP-08 Nullclines, phase portraits and dynamical interpretation 60 External computational comparison Advanced specialised module
CMP-09 Mystery neuron: parameter inference and model comparison 60 Jupyter inference module Advanced specialised module
DAT-01 Import, data structure and metadata 45 Jupyter analysis Core module
DAT-02 Plotting and quality control 45 Jupyter analysis Core module
DAT-03 Spike detection and validation 60 Jupyter analysis Core module
DAT-04 Firing rate and interspike intervals 60 Jupyter analysis Core module
DAT-05 Latency, adaptation and burst features 60 Jupyter analysis High-priority extension
DAT-06 Trial alignment, rasters and PSTHs 60 Jupyter analysis High-priority extension
DAT-07 Correlation, cross-correlation, phase and synchrony 60 Jupyter analysis Advanced specialised module
DAT-08 Encoding, decoding and reproducible analysis pipeline 60 Jupyter analysis Advanced specialised module
STA-01 Distributions, variability and experimental units 45 Jupyter statistics Core module
STA-02 Confidence intervals and bootstrap uncertainty 60 Jupyter statistics Core module
STA-03 Paired comparisons and effect sizes 60 Jupyter statistics High-priority extension
STA-04 Independent groups and non-parametric comparisons 60 Jupyter statistics Advanced specialised module
STA-05 Regression and repeated-measures designs 60 Jupyter statistics Advanced specialised module
STA-06 Multiplicity, power and statistical versus scientific significance 60 Jupyter methodology Advanced specialised module
IMG-01 Spikes to calcium to fluorescence 45 GUI simulation workflow Core module
IMG-02 Frame rate and temporal filtering 60 GUI + Jupyter High-priority extension
IMG-03 Baseline, ΔF/F, noise and bleaching 60 GUI simulation + analysis High-priority extension
IMG-04 Indicator kinetics, affinity and saturation 60 GUI simulation Advanced specialised module
IMG-05 ROI, neuropil and spike inference against ground truth 60 Prepared-dataset/Jupyter Advanced specialised module
EXT-01 Intracellular versus extracellular signals 45 GUI simulation Core module
EXT-02 Electrode geometry and tetrode projection 60 GUI simulation High-priority extension
EXT-03 Recording chain: noise, hum, reference and filtering 60 GUI simulation + analysis Advanced specialised module
EXT-04 Threshold detection and waveform features 60 GUI + Jupyter High-priority extension
EXT-05 Clustering, spike sorting and quality metrics 60 Prepared-dataset/Jupyter Advanced specialised module
MET-01 From observation to a testable question 30 Methodology exercise Core module
MET-02 Operational definitions and measurement 45 Methodology exercise Core module
MET-03 Protocol design, controls and confounding 60 Methodology exercise Core module
MET-04 Calibration, repeatability and uncertainty budget 60 Methodology experiment High-priority extension
MET-05 Exploratory versus confirmatory analysis 60 Methodology seminar + analysis Advanced specialised module
MET-06 Reproducible data, code and reporting 60 Reproducibility exercise Core module
EPI-01 Observation, measurement and inference 45 Conceptual seminar Core module
EPI-02 Model validity, analogy and underdetermination 60 Epistemological seminar Core module
EPI-03 Causality, falsification and negative results 60 Conceptual/methodology seminar Advanced specialised module
EPI-04 Open hardware, transparency and responsible communication 45 Seminar/workshop Core module
OSC-01 Explain a spike without a black box 30 Outreach demonstration Outreach module
OSC-02 Facilitate challenge cards and teacher-LED inquiry 60 Educator workshop Outreach module