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Von Economo神经元快速通道假说 - 生物速度-准确性权衡的计算模型。VENs作为快速稀疏投射通路，在复杂社会认知中实现快速决策。首次建立VENs的计算模型，解释其在快速社会决策中的功能。Activation: Von Economo neurons, VEN, speed-accuracy tradeoff, fast lane hypothesis, social cognition, spiking neural network, biological decision making.
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| name | von-economo-fast-lane-hypothesis |
| version | v1.0.0 |
| last_updated | 2026-04-17T00:00:00.000Z |
| description | Von Economo神经元快速通道假说 - 生物速度-准确性权衡的计算模型。VENs作为快速稀疏投射通路，在复杂社会认知中实现快速决策。首次建立VENs的计算模型，解释其在快速社会决策中的功能。Activation: Von Economo neurons, VEN, speed-accuracy tradeoff, fast lane hypothesis, social cognition, spiking neural network, biological decision making. |
The first computational model of Von Economo Neurons (VENs), introducing the Fast Lane Hypothesis: VENs implement a biological speed-accuracy tradeoff by providing a sparse, fast projection pathway for rapid social decisions.
Von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species with complex social cognition (humans, great apes, cetaceans). This methodology provides the first computational model of VEN function.
Key Innovation: VENs as fast leaky integrate-and-fire neurons with 5 ms time constant and sparse dendritic fan-in, enabling rapid social decisions at the cost of accuracy.
Anatomical Features:
Distribution:
Frontotemporal Dementia (FTD):
Autism:
VEN Model:
ven_params = {
"type": "LIF",
"tau_m": 5, # ms - fast membrane time constant
"v_thresh": -50, # mV
"v_reset": -70, # mV
"n_afferents": 8, # Sparse fan-in
"conduction_delay": 1 # ms
}
Pyramidal Neuron (Comparison):
pyramidal_params = {
"type": "LIF",
"tau_m": 20, # ms - standard time constant
"v_thresh": -50,
"v_reset": -70,
"n_afferents": 80, # Dense fan-in
"conduction_delay": 5 # ms
}
Cortical Circuit:
Social Discrimination Task:
Speed-Accuracy Tradeoff:
Temporal Dynamics:
Asymptotic Accuracy:
Clinical Conditions:
Typical vs FTD-like:
Autism-like:
First-Spike Latencies:
Species Comparison:
Model-Brain Correspondence:
network = SpikingCircuit(n_neurons=2000)
# Add pyramidal neurons (98%)
for i in range(1960):
network.add_neuron(LIFNeuron(**pyramidal_params))
# Add VENs (2%)
for i in range(40):
network.add_neuron(LIFNeuron(**ven_params), type='VEN')
trainer = SocialDiscriminationTrainer(network)
trainer.train(stimuli=social_stimuli, epochs=100)
# Typical condition
network.set_ven_fraction(0.02)
results_typical = network.evaluate(test_stimuli)
# Autism-like condition
network.set_ven_fraction(0.004)
results_autism = network.evaluate(test_stimuli)
# FTD-like condition
network.set_ven_fraction(0.0)
results_ftd = network.evaluate(test_stimuli)
plot_speed_accuracy_tradeoff([
('Typical', results_typical),
('Autism-like', results_autism),
('FTD-like', results_ftd)
])
Implementation:
Social Discrimination:
VEN Density:
Reaction Times:
@article{keskin2026ven,
title={The Fast Lane Hypothesis: Von Economo Neurons Implement a Biological Speed-Accuracy Tradeoff},
author={Keskin, Esila},
journal={arXiv preprint arXiv:2604.09229},
year={2026}
}