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Untrained fly connectome enables learned behavioral reversals

By PulseAugur Editorial · [1 sources] ·

Researchers have transformed an adult fly's brain and nerve cord connectome into a spiking neural network, utilizing published brain models with untuned parameters. When simulated within a body, this network demonstrated obstacle navigation towards 95% of odor sources, driven by simple rules connecting sensory input to motor neuron output. The study found that dopamine-gated synaptic weakening in the mushroom bodies enabled the fly to reverse its behavior from approaching an odor to avoiding it, suggesting that even an untrained connectome can support learned behavioral reversals. AI

IMPACT Demonstrates how biological connectomes can be computationally modeled to understand behavior, potentially informing future AI architectures.

RANK_REASON The cluster contains an academic paper detailing a new computational model and experimental findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Untrained fly connectome enables learned behavioral reversals

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The cluster contains an academic paper detailing a new computational model and experimental findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Mircea Teodorescu ·

    Sensorimotor behaviour from an untrained brain-and-nerve-cord connectome

    Connectomes now map the neurons and synapses of an adult fly's brain and nerve cord, but how much of behaviour follows from the wiring is unknown. Here we turn one such connectome into a spiking network of 172,433 neurons with a published brain model's untuned parameters, and run…

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