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31226fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | """Experimental multi-compartment neuron variant with separable NMDA plateau axis."""
from __future__ import annotations
from dataclasses import asdict, dataclass, field
from typing import Any
from .biophysical_contracts import (
DendriticNonlinearity,
ModelProvenance,
ModelTimescale,
NeuronStructure,
)
@dataclass(frozen=True, slots=True)
class CompartmentConfig:
enabled: bool = False
structure: NeuronStructure = NeuronStructure.MULTI_COMPARTMENT
compartments: int = 3
dt_ms: float = 1.0
soma_tau_ms: float = 20.0
dendrite_tau_ms: float = 40.0
coupling: float = 0.08
resting_potential: float = -65.0
spike_threshold: float = -50.0
reset_potential: float = -65.0
dendritic_nonlinearity: DendriticNonlinearity = DendriticNonlinearity.LINEAR
nmda_plateau_threshold: float = -35.0
nmda_plateau_gain: float = 4.0
enable_nmda_plateau: bool = False
def __post_init__(self) -> None:
if self.structure is not NeuronStructure.MULTI_COMPARTMENT:
raise ValueError("CompartmentNeuron requires MULTI_COMPARTMENT structure")
if self.compartments < 2:
raise ValueError("compartments must be >= 2")
if self.dt_ms <= 0.0:
raise ValueError("dt_ms must be > 0")
@dataclass(slots=True)
class CompartmentNeuron:
neuron_id: int
config: CompartmentConfig = CompartmentConfig()
soma_v: float = -65.0
spike_counter: int = 0
last_spike_tick: int = -1
dendrite_v: list[float] = field(default_factory=list[float])
def __post_init__(self) -> None:
expected = self.config.compartments - 1
if not self.dendrite_v:
self.dendrite_v = [self.config.resting_potential] * expected
elif len(self.dendrite_v) != expected:
raise ValueError("dendrite_v must match configured compartments")
@property
def provenance_tag(self) -> ModelProvenance:
payload = asdict(self.config)
payload["structure"] = self.config.structure.value
payload["dendritic_nonlinearity"] = self.config.dendritic_nonlinearity.value
return ModelProvenance(
model_id="multi-compartment-v1",
version="mhrn-experimental-1",
timescale=ModelTimescale.TICK,
enabled=self.config.enabled,
parameters=payload,
)
def step(
self,
soma_current: float,
dendritic_currents: tuple[float, ...],
tick: int,
) -> bool:
if not self.config.enabled:
return False
if len(dendritic_currents) != len(self.dendrite_v):
raise ValueError("dendritic_currents must match configured dendrites")
dt = self.config.dt_ms
old = list(self.dendrite_v)
for index, current in enumerate(dendritic_currents):
v = old[index]
neighbour = self.soma_v if index == 0 else old[index - 1]
coupling = self.config.coupling * (neighbour - v)
plateau = 0.0
if (
self.config.enable_nmda_plateau
and self.config.dendritic_nonlinearity
is DendriticNonlinearity.NMDA_PLATEAU
and v >= self.config.nmda_plateau_threshold
):
plateau = self.config.nmda_plateau_gain
dv = (
(self.config.resting_potential - v) + current + coupling + plateau
) * (dt / self.config.dendrite_tau_ms)
self.dendrite_v[index] = v + dv
dendritic_drive = sum(
self.config.coupling * (value - self.soma_v) for value in self.dendrite_v
)
self.soma_v += (
(self.config.resting_potential - self.soma_v)
+ soma_current
+ dendritic_drive
) * (dt / self.config.soma_tau_ms)
spiked = self.soma_v >= self.config.spike_threshold
if spiked:
self.soma_v = self.config.reset_potential
self.spike_counter += 1
self.last_spike_tick = tick
return spiked
def to_dict(self) -> dict[str, Any]:
return {
"schema_version": 1,
"neuron_id": self.neuron_id,
"soma_v": self.soma_v,
"dendrite_v": list(self.dendrite_v),
"spike_counter": self.spike_counter,
"last_spike_tick": self.last_spike_tick,
"provenance": self.provenance_tag.to_dict(),
}
__all__ = ["CompartmentConfig", "CompartmentNeuron"]
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