Braincraft challenge – 1000 neurons, 100 seconds, 10 runs, 2 choices, no reward

Summary

Table of Contents Introduction The computational neuroscience literature abounds with models of individual brain structures, such as the hippocampus, basal ganglia, thalamus, and various cortical areas — from visual to prefrontal. These models typically aim to explain specific functions attributed to each structure. For instance, the basal ganglia are often modeled in the context of decision-making, while the hippocampus is associated with episodic memory and spatial navigation through place cells. However, such models are usually highly abstract and simplified, often relying on a small number of over-engineered neurons and synapses, dealing mostly with abstract inputs and outputs. Consequently, despite decades of work, we still lack an integrated, functional mini-brain — a synthetic neural system capable of performing even simple, continuous embodied tasks in a simulated environment. The BrainCraft Challenge seeks to address this gap by promoting the development of such systems. Specifically, the challenge invites participants to design a biologically inspired, rate-based neural network capable of solving simple decision tasks. The network must control an agent (or "bot") situated in a continuous, low-complexity environment. The agent’s sole objective is to locate and reach an energy source in order to maintain its viability. Tasks Timeline The whole challenge is made of 5 different tasks with increasing complexity. Each new task will be introduced at specific dates and participants will have 2 months to try to complete them. # Name Start End Status 1 Simple decision JUL 01, 2025 AUG 31, 2025 Active 2 Cued decision SEP 01, 2025 OCT 31, 2025 Active 3 NOV 01, 2025 DEC 31, 2025 Inactive 4 JAN 01, 2026 FEB 28, 2026 Inactive 5 MAR 01, 2026 APR 30, 2026 Inactive Task 1: Simple decision The environment is a 10x10 square maze with three parallels vertical path as illustrated on Figure 1. The cartesian coordinates inside the maze are normalized such that any position (x,y) ...