Intelligent behavior requires experience-dependent internal representations of relevant information. We examined representational learning in telencephalic area pDp of adult zebrafish, the homolog of ...

Forgotten memories in Drosophila persist as silent memory traces and can be recovered by reminder cues. Manipulating reminders can create false memories, showing that memory reconstruction flexibly in...

Neurons in the brain are often many synapses away from motoneurons, yet if a movement results in error, each distant neuron needs a teacher that considers its specific contribution to production of th...

Spiking neurons underlie the brain’s extreme energy efficiency, and therefore have great potential in neuromorphic computing, although realising this efficiency in practice has proven challenging. We ...

A central approach in neuroscience is to analyze neural representations as a means to understand a system's function, through the use of methods like principal component analysis, regression, and repr...

Spiking Neural networks as Universal Function Approximators

Sosa et al. find that hippocampal neural activity in mice encodes both environmental location and experience relative to rewards, spanning distances far from reward, through parallel and flexible popu...

Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of str...

Basal ganglia output neurons fire dynamically in bidirectional and movement-specific patterns to license forelimb movements. 

Deep neural networks have achieved impressive performance through carefully engineered training strategies. Nonetheless, such methods lack parallels in biological neural circuits, relying heavily on n...

Merging ideas from reinforcement learning theory with recent insights into the filtering properties of the dorsal raphe nucleus, a unifying perspective is found explaining why serotonin neurons are ac...