Physical learning systems require a means to store information about the past. In the brain, information is thought to be stored in synaptic connections between neurons whose weights change in respons...

Many biological learning systems such as the mushroom body, hippocampus, and cerebellum are built from sparsely connected networks of neurons. For a new understanding of such networks, we study the fu...

Difficult decisions require gathering evidence over extended periods, placing demands on working memory. Yet, how working memory limitations affect decision-making remains largely unknown. We trained ...

When preparing a scientific paper, we typically write with coauthors who have different backgrounds and styles, and we target readers who have little time and patience. To help both readers and writer...

Today’s computers need safeguards against random energy fluctuations. Thermodynamic computers would put those fluctuations to use.

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 ...

Abstract. Decades of research have established the hippocampus as central to episodic memory, but growing evidence suggests that it also contributes to str

Large nonlinear recurrent neural networks with random couplings generate high-dimensional, potentially chaotic activity whose structure is of interest in neuroscience, machine learning, ecology, and o...

Galloni et al. introduce “dendritic target propagation”: a Dale’s law-compliant learning algorithm for cortical microcircuits with soma- and dendrite-targeting inhibition and realistic connectivity co...

Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to chang...

The Neural Dynamics (CATNIP) Lab, led by Memming Park at the Champalimaud Centre for the Unknown in Lisbon, Portugal, is seeking a Postdoctoral Fellow to extract organizing principles of neural comput...

Neural population activity can be described either by low-dimensional dynamics on neural manifolds or by single-neuron selectivities. Using a theoretical approach, Pezon et al. relate these two statistical descriptions to circuit structure in recurrent networks. Their results reveal both degeneracies and specific constraints in how circuit structure shapes neural activity.

No two neurons are the same, yet models often treat neural populations as pools of identical and interchangeable elements. Here, Dahmen et al. highlight recent theoretical advances that reveal the imp...

Nature - The brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations.

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

A context-driven memory model simulates a wide range of characteristics of waking and sleeping hippocampal replay, providing a new account of how and why replay occurs.

The structure of brain connectivity predicts collective neural activity, with a small number of connectivity features determining activity dimensionality, linking circuit architecture to network-level...

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

Nature Communications - The neural processes involved in memory formation for realistic experiences remain poorly understood. Here, the authors found that ripple-like activity in the human...

Social communication between animals is often mediated by sequences of acoustic signals, sometimes spanning long timescales. How auditory neural ci...