Yanbo Lian

Distinct cortical spatial representations learned along disparate visual pathways

The paper builds a learning model to show how diverse spatial properties in the postrhinal cortex emerge from visual input processing through the superior colliculus pathway.

Emergence of Sparse Coding, Balance and Decorrelation from a Biologically-Grounded Spiking Neural Network Model of Learning in the Primary Visual Cortex

This study shows that a biologically grounded spiking neural network model of V1 learning exhibits sparse coding, balance, and decorrelation as emergent properties.

Relating sparse/predictive coding to divisive normalization

This paper connects sparse coding, predictive coding, and divisive normalization within a unified learning framework to explain neural response nonlinearities and brain computations.

Learning spatio-temporal V1 cells from diverse LGN inputs

This study uses a sparse coding model to show that spatio-temporal properties of V1 cells emerge from learning diverse LGN inputs through synaptic plasticity.

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data

This paper shows that egocentric boundary cells can be generated with a simple synaptic learning rule that creates sparse representations of visual input, matching observed neural activity in retrosplenial cortex.

Learning spatio-temporal properties of hippocampal place cells

This paper presents a learning model that explains how hippocampal place cells develop both spatial and temporal response properties from entorhinal cortex input.

Learning an efficient hippocampal place map from entorhinal inputs using non-negative sparse coding

This paper proposes a model using non-negative sparse coding to learn hippocampal place maps from entorhinal inputs, showing that grid cell diversity and sparse coding enable efficient spatial tiling.

Learning receptive field properties of complex cells in V1

This paper proposes a biologically plausible model for learning complex cells in V1 using BCM rule-based plasticity, showing it can replicate experimental diversity in receptive field properties.

Learning receptive field properties of complex cells in V1

The paper was withdrawn due to a duplicate posting and directs readers to the current version on bioRxiv.