Evolutionary learning in the brain by heterosynaptic plasticity
The way in which the brain modifies synapses to improve the performance of complicated networks remains one of the biggest mysteries in neuroscience. Existing proposals lack sufficient experimental support, and neglect inter-cellular signaling pathways ubiquitous in the brain. Here we show that the heterosynaptic plasticity between hippocampal or cortical pyramidal cells mediated by diffusive nitric oxide and astrocyte calcium wave, together with flexible dendritic gating of somatostatin interneurons, implies an evolutionary algorithm (EA). In simulation, this EA is able to train deep networks with biologically plausible binary weights in MNIST classification and Atari-game playing tasks up to performance comparable with continuous-weight networks trained by gradient-based methods. Our work leads paradigmatically fresh understanding of the brain learning mechanism.