An assembly of neurons encodes information in a sequence of spikes. Axons from this assembly deliver its spike sequence to a short stretch of dendrite. How this stretch decodes the encoded information is unknown. This project will mine a microscale reconstruction of a millimeter-cube of brain tissue for anatomical signatures of sequence-decoding. These signatures were predicted by a computational model of a dendrite developed by us. It responds only when a sequence’s spikes activate its synapses consecutively, from one end of the stretch to the other. It makes a testable prediction: When branches of axons carrying a sequence contact two stretches of dendrite, they will synapse onto those stretches in the same order. Confirming this prediction will unravel how axon branches and dendrite stretches are organized at the microscale. That would reveal how biological neural nets operate with far fewer signals than artificial neural nets. This sparse signaling saves energy. That would enable AI chips to become 3D—like the brain.