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, including humans, great apes, cetaceans, and elephants. Their selective depletion in frontotemporal dementia (FTD) and altered development in autism implicate them in rapid social decision-making, yet no computational model of VEN function has previously existed. We introduce the Fast Lane Hypothesis: VENs implement a biological speed-accuracy tradeoff (SAT) by providing a sparse, fast projection pathway that enables rapid social decisions at the cost of deliberate processing accuracy. We model VENs as fast leaky integrate-and-fire (LIF) neurons (tau=5ms, fan-in=8) alongside standard pyramidal neurons (tau=20ms, fan-in=80) within a spiking cortical circuit (N_pyr=2,000) trained on a social discrimination task. Networks are evaluated under three clinically motivated conditions across 10 independent random seeds: typical (2% VENs), autism-like (0.4% VENs), and FTD-like (post-training VEN ablation). All configurations achieve equivalent asymptotic classification accuracy (~99.4%), consistent with the prediction that VENs modulate decision speed rather than representational capacity. VENs produce median first-spike latencies 4ms earlier than pyramidal neurons, providing direct evidence of a fast signalling pathway. At a fixed decision threshold of theta=3, the typical condition is significantly faster than FTD-like (t=-23.31, p<0.0001), while the autism-like condition is intermediate (mean RT=26.91+/-9.01ms vs. typical 20.70+/-2.02ms; p=0.078, trending). To our knowledge, this is the first computational model that asks what a von Economo neuron actually computes.

翻译：暂无翻译