167 citations · 643 across the 22 of their papers we have counts for
6 papers · 1 filter
Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain
Trinity Chung, Yuchen Shen, Nathan C. L. Kong +1
Tactile sensing remains far less understood in neuroscience and less effective in artificial systems compared to more mature modalities such as vision and language. We bridge these…
Identifying Learning Rules From Neural Network Observables
Aran Nayebi, Sanjana Srivastava, Surya Ganguli +1
The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Ma…
Two Routes to Scalable Credit Assignment without Weight Symmetry
Daniel Kunin, Aran Nayebi, Javier Sagastuy-Brena +3
The neural plausibility of backpropagation has long been disputed, primarily for its use of non-local weight transport the biologically dubious requirement that one neuron inst…
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan +3
Recently, deep feedforward neural networks have achieved considerable success in modeling biological sensory processing, in terms of reproducing the input-output map of sensory neu…
Task-Driven Convolutional Recurrent Models of the Visual System
Aran Nayebi, Daniel Bear, Jonas Kubilius +5
Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models…
Deep Learning Models of the Retinal Response to Natural Scenes
Lane T. McIntosh, Niru Maheswaranathan, Aran Nayebi +2
A central challenge in neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayer…