Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

Despite great efforts over several decades, our best models of primary visual cortex (V1) still
predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited …

Despite enormous progress in machine learning, artificial neural networks still lag behind
brains in their ability to generalize to new situations. Given identical training data …

Finding sensory stimuli that drive neurons optimally is central to understanding information
processing in the brain. However, optimizing sensory input is difficult due to the …

Deep neural networks (DNNs) optimized for visual tasks learn representations that align
layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this …

Neuroscientists classify neurons into different types that perform similar computations at
different locations in the visual field. Traditional methods for neural system identification do …

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal
activity have become an established method for studying tuning properties of biological and …

Highlights•Modern recording technologies are creating data at a scale and complexity that
demand rigorous data analytical approaches.•Neural data science is an essential bridge …

Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to
the overall input-output transformation of single neurons. We developed statistically …