Synapses and integration¶
Curriculum status
These entries are architecture specifications, not final lesson plans. They define teaching intent, logistics, outputs and scientific boundaries; release-ready settings, worksheets, notebooks and answer keys still require empirical validation.
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Family source cluster: [T2 Chs.11–15; T3 Ch.5; T4 Chs.5,7; T5 Chs.5–6; T6 Ch.3; R1; G3]
SYN-01 — From presynaptic event to postsynaptic current¶
| Concept / theme | A rising presynaptic event launches a decaying signed current that influences the postsynaptic model. |
|---|---|
| Audience / context | Undergraduate. Formal practical, workshop or modular course. Prior modules: EPH-03, NPH-02. |
| Logistics | 45 min; 3–5; boards: 1–3; software: GUI; Jupyter for quantitative/ plasticity modules; prepared dataset: Optional or generated. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Multi-board guided experiment. Stages: 1–8, 10–11. |
| Spikeling relationship | 2 — Direct board + GUI; 4 — Multi-board implementation; 7 — Conceptual analogy |
Learning outcomes.
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Identify and predict the principal behaviour described in a rising presynaptic event launches a decaying signed current that influences the postsynaptic model.
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Configure or document connect one board or pulse source to one synaptic input and inspect pre-Vm, synaptic current and post-Vm and record presynaptic Vm/event, synaptic current, postsynaptic Vm.
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Measure, calculate or compare connect one board or pulse source to one synaptic input and inspect pre-Vm, synaptic current and post-Vm using an explicit operational rule.
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Interpret the result and state why this is not direct evidence of transmitter release, receptor activation or conductance change
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Connect one board or pulse source to one synaptic input and inspect pre-Vm, synaptic current and post-Vm.
4. Acquire or inspect presynaptic Vm/event, synaptic current, postsynaptic Vm.
5. Produce annotated transmission trace.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: This is not direct evidence of transmitter release, receptor activation or conductance change.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate connect one board or pulse source to one synaptic input and inspect pre-Vm, synaptic current and post-Vm. The reusable output is annotated transmission trace.
Sources / connections / priority. Sources: [T2 Chs.11–15; T3 Ch.5; T4 Chs.5,7; T5 Chs.5–6; T6 Ch.3; R1; G3]. Natural follow-ons: SYN-02, SYN-03, NET-01. Development priority: Core module. Boundary: This is not direct evidence of transmitter release, receptor activation or conductance change.
SYN-02 — Excitation, inhibition and signed gain¶
| Concept / theme | The sign and magnitude of synaptic gain determine functional excitatory or inhibitory influence. |
|---|---|
| Audience / context | School to undergraduate. Formal practical, workshop or modular course. Prior modules: SYN-01. |
| Logistics | 60 min; 3–5; boards: 1–3; software: GUI; Jupyter for quantitative/ plasticity modules; prepared dataset: Optional or generated. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Board + GUI experiment. Stages: 1–8, 10–11. |
| Spikeling relationship | 2 — Direct board + GUI; 3 — Hybrid board + Jupyter; 4 — Multi-board implementation; 7 — Conceptual analogy |
Learning outcomes.
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Identify and predict the principal behaviour described in the sign and magnitude of synaptic gain determine functional excitatory or inhibitory influence.
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Configure or document sweep gain across negative, zero and positive settings and record synaptic current sign, post-Vm, spike probability.
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Measure, calculate or compare sweep gain across negative, zero and positive settings using an explicit operational rule.
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Interpret the result and state why functional sign does not identify receptor type or reversal potential
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Sweep gain across negative, zero and positive settings.
4. Acquire or inspect synaptic current sign, post-Vm, spike probability.
5. Produce gain–response plot and functional e/i definition.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Functional sign does not identify receptor type or reversal potential.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate sweep gain across negative, zero and positive settings. The reusable output is gain–response plot and functional e/i definition.
Sources / connections / priority. Sources: [T2 Chs.11–15; T3 Ch.5; T4 Chs.5,7; T5 Chs.5–6; T6 Ch.3; R1; G3]. Natural follow-ons: SYN-03, NET-02, NET-04. Development priority: Core module. Boundary: Functional sign does not identify receptor type or reversal potential.
SYN-03 — Temporal summation¶
| Concept / theme | Inputs arriving within the integration window combine more strongly than widely separated inputs. |
|---|---|
| Audience / context | Undergraduate. Formal practical, workshop or modular course. Prior modules: SYN-01, EPH-04. |
| Logistics | 60 min; 3–5; boards: 1–3; software: GUI; Jupyter for quantitative/ plasticity modules; prepared dataset: Optional or generated. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Hybrid multi-board experiment. Stages: 1–8, 10–11. |
| Spikeling relationship | 2 — Direct board + GUI; 3 — Hybrid board + Jupyter; 4 — Multi-board implementation |
Learning outcomes.
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Identify and predict the principal behaviour described in inputs arriving within the integration window combine more strongly than widely separated inputs.
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Configure or document vary the interval between repeated events on one input and record peak post-Vm/current, output spike, interval.
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Measure, calculate or compare vary the interval between repeated events on one input using an explicit operational rule.
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Interpret the result and state why observed decay/integration reflects model and firmware parameters, not a measured membrane/synaptic time constant
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Vary the interval between repeated events on one input.
4. Acquire or inspect peak post-Vm/current, output spike, interval.
5. Produce summation-vs-interval curve.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Observed decay/integration reflects model and firmware parameters, not a measured membrane/synaptic time constant.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate vary the interval between repeated events on one input. The reusable output is summation-vs-interval curve.
Sources / connections / priority. Sources: [T2 Chs.11–15; T3 Ch.5; T4 Chs.5,7; T5 Chs.5–6; T6 Ch.3; R1; G3]. Natural follow-ons: SYN-05, SYN-06, DAT-06. Development priority: Core module. Boundary: Observed decay/integration reflects model and firmware parameters, not a measured membrane/synaptic time constant.
SYN-04 — Spatial summation with two inputs¶
| Concept / theme | Two independently controlled inputs can sum, cancel or gate one another. |
|---|---|
| Audience / context | Undergraduate. Formal practical, workshop or modular course. Prior modules: SYN-02. |
| Logistics | 60 min; 3–5; boards: 1–3; software: GUI; Jupyter for quantitative/ plasticity modules; prepared dataset: Optional or generated. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Multi-board experiment. Stages: 1–8, 10–11. |
| Spikeling relationship | 2 — Direct board + GUI; 3 — Hybrid board + Jupyter; 4 — Multi-board implementation |
Learning outcomes.
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Identify and predict the principal behaviour described in two independently controlled inputs can sum, cancel or gate one another.
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Configure or document present two-input combinations with varied sign, amplitude and simultaneity and record syn1/syn2 currents, post-Vm and spikes.
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Measure, calculate or compare present two-input combinations with varied sign, amplitude and simultaneity using an explicit operational rule.
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Interpret the result and state why “spatial” denotes separate input channels, not dendritic location or cable filtering
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Present two-input combinations with varied sign, amplitude and simultaneity.
4. Acquire or inspect syn1/syn2 currents, post-Vm and spikes.
5. Produce two-factor response matrix.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: “Spatial” denotes separate input channels, not dendritic location or cable filtering.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate present two-input combinations with varied sign, amplitude and simultaneity. The reusable output is two-factor response matrix.
Sources / connections / priority. Sources: [T2 Chs.11–15; T3 Ch.5; T4 Chs.5,7; T5 Chs.5–6; T6 Ch.3; R1; G3]. Natural follow-ons: NET-02, NET-05, STA-05. Development priority: High-priority extension. Boundary: “Spatial” denotes separate input channels, not dendritic location or cable filtering.
SYN-05 — Coincidence detection and input timing¶
| Concept / theme | Output can depend on relative timing rather than only total input magnitude. |
|---|---|
| Audience / context | Advanced undergraduate. Formal practical, workshop or modular course. Prior modules: SYN-03, SYN-04, EPH-04. |
| Logistics | 60 min; 3–5; boards: 1–3; software: GUI; Jupyter for quantitative/ plasticity modules; prepared dataset: Optional or generated. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Multi-board + Jupyter. Stages: 1–8, 10–11. |
| Spikeling relationship | 3 — Hybrid board + Jupyter; 4 — Multi-board implementation; 8 — External computational comparison |
Learning outcomes.
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Identify and predict the principal behaviour described in output can depend on relative timing rather than only total input magnitude.
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Configure or document shift two input trains across positive and negative delays and record output probability/rate versus relative delay.
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Measure, calculate or compare shift two input trains across positive and negative delays using an explicit operational rule.
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Interpret the result and state why delay precision and network latency require validation; no dendritic mechanism is implied
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Shift two input trains across positive and negative delays.
4. Acquire or inspect output probability/rate versus relative delay.
5. Produce coincidence window plot.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Delay precision and network latency require validation; no dendritic mechanism is implied.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate shift two input trains across positive and negative delays. The reusable output is coincidence window plot.
Sources / connections / priority. Sources: [T2 Chs.11–15; T3 Ch.5; T4 Chs.5,7; T5 Chs.5–6; T6 Ch.3; R1; G3]. Natural follow-ons: NET-07, DAT-07. Development priority: Advanced specialised module. Boundary: Delay precision and network latency require validation; no dendritic mechanism is implied.
SYN-06 — Static synapses versus short-term plasticity¶
| Concept / theme | History-dependent facilitation/depression can be distinguished from a static decaying synapse through controlled trains and model comparison. |
|---|---|
| Audience / context | Advanced undergraduate to master’s. Formal practical, workshop or modular course. Prior modules: SYN-03, DAT-05. |
| Logistics | 60 min; 3–5; boards: 1–3; software: GUI; Jupyter for quantitative/ plasticity modules; prepared dataset: Optional or generated. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Simulation comparison. Stages: 1–8, 10–11. |
| Spikeling relationship | 3 — Hybrid board + Jupyter; 5 — Recorded-dataset analysis; 8 — External computational comparison |
Learning outcomes.
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Identify and predict the principal behaviour described in history-dependent facilitation/depression can be distinguished from a static decaying synapse through controlled trains and model comparison.
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Configure or document record the current static response, then compare with a notebook stp/std simulation or prepared dataset and record event times, response amplitudes, fitted dynamic-synapse variables.
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Measure, calculate or compare record the current static response, then compare with a notebook stp/std simulation or prepared dataset using an explicit operational rule.
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Interpret the result and state why current v3 repository does not establish board-level stp/std; do not present the simulation as implemented hardware behaviour
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Record the current static response, then compare with a notebook STP/STD simulation or prepared dataset.
4. Acquire or inspect event times, response amplitudes, fitted dynamic-synapse variables.
5. Produce static-vs-dynamic comparison and model-selection statement.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Current v3 repository does not establish board-level STP/STD; do not present the simulation as implemented hardware behaviour.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate record the current static response, then compare with a notebook stp/std simulation or prepared dataset. The reusable output is static-vs-dynamic comparison and model-selection statement.
Sources / connections / priority. Sources: [T2 Chs.11–15; T3 Ch.5; T4 Chs.5,7; T5 Chs.5–6; T6 Ch.3; R1; G3]. Natural follow-ons: CMP-09, STA-05. Development priority: Dataset-only/high-priority extension. Boundary: Current v3 repository does not establish board-level STP/STD; do not present the simulation as implemented hardware behaviour.