Learning

Training paradigms beyond single-node STDP: BPTT, truncated BPTT, eligibility traces, reward-modulated learning, meta-learning, homeostatic scaling, short-term plasticity, structural plasticity, federated learning, lifelong/continual learning, neuroevolution, learning-rate schedulers, and training callbacks.

BPTT Learner

::: sc_neurocore.learning.advanced.BPTTLearner

Truncated BPTT (Williams & Peng 1990)

Chunks long sequences into windows of k timesteps, backpropagating gradients within each chunk while carrying membrane state forward. Memory O(k) instead of O(T).

::: sc_neurocore.learning.advanced.TBPTTLearner

Eligibility Traces (e-prop, Bellec et al. 2020)

::: sc_neurocore.learning.advanced.EligibilityTrace

Reward-Modulated STDP

::: sc_neurocore.learning.advanced.RewardModulatedLearner

Learning-Rate Schedulers

Import these schedulers from either sc_neurocore.learning or sc_neurocore.learning.schedulers. They keep only deterministic local state and return the current learning rate from each step() call.

::: sc_neurocore.learning.schedulers.StepScheduler

::: sc_neurocore.learning.schedulers.ExponentialScheduler

::: sc_neurocore.learning.schedulers.CosineScheduler

::: sc_neurocore.learning.schedulers.WarmupCosineScheduler

Training Callbacks

Import callbacks from either sc_neurocore.learning or sc_neurocore.learning.callbacks. CSVCallback has no optional dependencies; TensorBoardCallback and WandBCallback fail closed with SCDependencyError when their optional runtime packages are not installed.

::: sc_neurocore.learning.callbacks.TrainingCallback

::: sc_neurocore.learning.callbacks.CSVCallback

::: sc_neurocore.learning.callbacks.TensorBoardCallback

::: sc_neurocore.learning.callbacks.WandBCallback

Bounded O(1) Online Learning

sc_neurocore.learning.online_o1 defines the hardware-facing local-learning contract for streamed online updates. One synapse stores only four bounded state fields: current weight, pre trace, post trace, and eligibility trace. The memory proof emitted by build_online_o1_memory_proof(...) is independent of sequence length and reports the exact per-synapse bit count used by the HDL emitter.

The first supported rule family is reward-modulated STDP with fixed-point saturation. OnlineO1Config.to_scnir_annotation(...) emits deterministic SC-NIR metadata for online-learning-capable synapses, and sc_neurocore.hdl_gen.OnlineO1LearningEmitter emits a one-lane Verilog update block with the same bounded state fields and saturation policy. This software and RTL contract is local handoff evidence; it does not claim board synthesis or physical FPGA learning evidence until those runs are attached separately. When the optional native library is present, RustOnlineO1Synapse provides the same bounded fixed-point step contract through the Rust C-FFI bridge.

OnlineO1LearningEmitter.estimate_resources(...) reports deterministic pre-synthesis BRAM/LUT/DSP planning estimates for a chosen synapse count. The reproducible adaptation benchmark command is:

PYTHONPATH=src python tools/online_o1_adaptation_benchmark.py \
  --output benchmarks/results/online_o1_adaptation_benchmark.json

The default local report is deterministic simulation evidence: it measures pre/post reward-pairing adaptation speed, records Python/Rust parity when the native library is present, and marks hardware_measurement_claimed=false.

::: sc_neurocore.learning.online_o1.OnlineO1Config

::: sc_neurocore.learning.online_o1.OnlineO1Synapse

::: sc_neurocore.learning.online_o1.build_online_o1_memory_proof

::: sc_neurocore._native.learning_bridge.RustOnlineO1Synapse

Meta-Learning (MAML, Finn et al. 2017)

::: sc_neurocore.learning.advanced.MetaLearner

Homeostatic Plasticity (Turrigiano 2008)

::: sc_neurocore.learning.advanced.HomeostaticPlasticity

Short-Term Plasticity (Tsodyks-Markram 1997)

::: sc_neurocore.learning.advanced.ShortTermPlasticity

Structural Plasticity

::: sc_neurocore.learning.advanced.StructuralPlasticity

Federated

::: sc_neurocore.learning.federated

Lifelong (EWC)

Elastic Weight Consolidation with active penalty: pushes drifted weights back toward consolidated values, weighted by Fisher information.

::: sc_neurocore.learning.lifelong

Neuroevolution

::: sc_neurocore.learning.neuroevolution