Ventral Visual Pathway Learns Invariant Object Representations

Zhang, Rolls, and Feng publish on Feb 11, 2026 a four-layer biologically plausible network that learns transform-invariant object and face representations in the ventral visual pathway. The model uses a trace-based local synaptic learning rule, strength-dependent long-term depression, synaptic weight clipping, and an NMDA-like nonlinearity to increase storage capacity and scalability. Results demonstrate hundreds of objects can be trained in modest-sized networks, informing neuroscience and biologically inspired AI.