Purkinje Multi-Compartment Layer
Purkinje cells in the biological cerebellum have complex dendritic trees with distinct computational compartments. MH-FLOCKE models this with a 3-compartment neuron: soma, apical dendrite, and basal dendrite.
Three Compartments
- Soma (τ = 15ms) — Output compartment. Fires simple spikes for DCN inhibition.
- Apical Dendrite (τ = 80ms) — Slow integration of parallel fiber (GrC) input. The main plastic site for LTD/LTP learning.
- Basal Dendrite (τ = 5ms) — Fast response to climbing fiber input. Triggers calcium spikes that gate LTD in the apical compartment.
Calcium Dynamics
When a climbing fiber fires (basal input), calcium floods the apical compartment. Calcium persists for ~12 steps (vs 1 step for the CF itself), creating a wider learning window. This is biologically more accurate than binary CF gating.
calcium += cf_input × 0.8 calcium *= 0.85 (decay per step) LTD gate = calcium (used for PF→PkC weight update)
Complex Spikes
When calcium exceeds threshold (1.5), a complex spike is generated — a burst of output that strongly inhibits the DCN. This is the Purkinje cell’s strongest signal.
References
- Rancz et al. (2007). Dendritic spikes in Purkinje cells. Neuron
- D’Angelo (2025). Linking cellular phenomena to brain architecture. Trends in Neurosciences
API Reference
PurkinjeCompartmentLayer(config: PurkinjeConfig)
step(pf_input, cf_input)
Update all compartments: apical integrates PF, basal responds to CF, soma outputs activity. Calcium dynamics for LTD gating.
get_ltd_gate() → Tensor[n_pkc]
Current calcium levels for LTD/LTP decision (0 = LTP window, >0.05 = LTD active).
get_compartment_state() → dict
Returns soma, apical, basal voltages, calcium, complex_spike flags.
PurkinjeConfig
n_neurons: auto (2 × n_actuators) tau_soma: 15ms tau_apical: 80ms tau_basal: 5ms