A major shift is happening within neurophysiology: a population doctrine is drawing level
with the single-neuron doctrine that has long dominated the field. Population-level ideas …

Neurons often respond to diverse combinations of task-relevant variables. This form of
mixed selectivity plays an important computational role which is related to the dimensionality …

Understanding object representations requires a broad, comprehensive sampling of the
objects in our visual world with dense measurements of brain activity and behavior. Here …

Deep neural networks are powerful tools in learning sophisticated but fixed mapping rules
between inputs and outputs, thereby limiting their application in more complex and dynamic …

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core
representational principles of computational models in neuroscience. Here we examined the …

The classic approach to measure the spiking response of neurons involves the use of metal
electrodes to record extracellular potentials. Starting over 60 years ago with a single …

In the somatosensory nerves, the tactile perception of texture is driven by spatial and
temporal patterns of activation distributed across three populations of afferents. These …

Highlights•Object representation is intermediate between parts-based and holistic
descriptions.•Intrinsic dimensionality of object representations is approximately one …

If spikes are the medium, what is the message? Answering that question is driving the
development of large-scale, single neuron resolution recordings from behaving animals, on …

The application of machine learning methods to neuroimaging data has fundamentally
altered the field of cognitive neuroscience. Future progress in understanding brain function …