Highlights•New generations of recording and computing technologies have enabled
neuroscience at the level of the neural population.•Landmark scientific findings suggest the …

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 …

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 …

Understanding how biological visual systems process information is challenging because of
the nonlinear relationship between visual input and neuronal responses. Artificial neural …

Navigation engages many cortical areas, including visual, parietal, and retrosplenial
cortices. These regions have been mapped anatomically and with sensory stimuli 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 …

Modern recording techniques enable large-scale measurements of neural activity in a
variety of model organisms. The dynamics of neural activity shed light on how organisms …