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V1 Six-layer Column

Open in Colab

A laminar model inspired by primate V1 with six layers and depth-specific readouts.

Configuration

import jaxfne as jtfne

cfg = (
    jtfne.configuration()
    .network(
        n=600,
        layers=["L1", "L2/3", "L4", "L5", "L6"],
        cell_types={"E": 0.8, "PV": 0.1, "SST": 0.07, "VIP": 0.03},
        connectivity="layer_structured"
    )
    .emitter(family="izhikevich", preset="cortical_eig")
    .field(
        domain="laminar_column",
        conductivity="proxy",
        depths=[0.0, 0.15, 0.3, 0.5, 0.7, 1.0]
    )
    .probe(
        name="v1_column",
        n_contacts=6,
        modes=["spikes", "V_m", "LFP", "CSD"]
    )
)

model = jtfne.construct(cfg)

Multimodal readouts

signals = model.simulate(...)

# Layer-specific rates
readouts = model.compute_readout(signals, [
    jtfne.readout_spec("L4_rate", "spike_rate_hz"),
    jtfne.readout_spec("LFP_L4", "lfp_abs_mean"),
    jtfne.readout_spec("CSD_L4", "csd_abs_mean"),
])

Laminar features

  • L4 receives thalamocortical input (configurable)
  • L2/3, L5, L6 have inter-laminar projections
  • LFP-proxy reflects population summed current
  • CSD-proxy reflects current source densities per layer

Next step

Progress to V1-PFC dual column for multi-areal networks.