Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

Midbrain dopamine signals are widely thought to report reward prediction errors that drive
learning in the basal ganglia. However, dopamine has also been implicated in various …

Recent progress in artificial intelligence has renewed interest in building systems that learn
and think like people. Many advances have come from using deep neural networks trained …

Artificial neuromorphic systems based on populations of spiking neurons are an
indispensable tool in understanding the human brain and in constructing neuromimetic …

Neuroscientists have long criticised deep learning algorithms as incompatible with current
knowledge of neurobiology. We explore more biologically plausible versions of deep …

Conventional neuro-computing architectures and artificial neural networks have often been
developed with no or loose connections to neuroscience. As a consequence, they have …

A single dendritic neuron model (DNM) that owns the nonlinear information processing
ability of dendrites has been widely used for classification and prediction. Complex-valued …

The principles by which networks of neurons compute, and how spike-timing dependent
plasticity (STDP) of synaptic weights generates and maintains their computational function …

We address two main challenges facing systems neuroscience today: understanding the
nature and function of cortical feedback between sensory areas and of correlated variability …

Neuronal computations rely upon local interactions across synapses. For a neuronal
network to perform inference, it must integrate information from locally computed messages …