Bioplastics derived from biomass show promise as sustainable alternatives to petrochemical plastics, but their adoption is hindered by their inferior mechanical properties and processability. Here, th...

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...

Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to map the mechanisms of the brain's flexibility onto the dynam...

When we try to formalize a neuron computationally, we don't translate biology into code—we perform a violent collapse. We lock causation into fixed arrows when biology lives in causal ambiguity. We sy...

Where do objective functions come from? How do we select what goals to pursue? Human intelligence is adept at synthesizing new objective functions on the fly. How does this work, and can we endow arti...

By tracking brain activity as primates move freely in the wild, neuroethology could reshape what we think we know about our own minds.

Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has...

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...

What is compositionality? For those of us working in AI or cognitive neuroscience this question can appear easy at first, but becomes increasingly perplexing the more we think about it. We aren’t shor...

YouTube video by Gerstner Lab

Our understanding of neural computation is founded on the assumption that neurons fire in response to a linear summation of inputs. Yet experiments demonstrate that some neurons are capable of complex...

It is unclear how cognitive computations are performed on sensory information. Here, neural evidence from working memory tasks suggests that the physical dimensions of cortical networks are used to up...

Neural circuits comprise multiple interconnected regions, each with complex dynamics. The interplay between local and global activity is thought to...

Songs exhibit universal patterns across cultures.

Biological ensembles use collective intelligence to tackle challenges together, but suboptimal coordination can undermine the effectiveness of grou...

Calcium imaging of spontaneously behaving mice show increased intrinsic neural timescales during behavior. The behavioral state of mice can be predicted from the topography of timescales of the cortex...

In this work, we question the necessity of adaptive gradient methods for training deep neural networks. SGD-SaI is a simple yet effective enhancement to stochastic gradient descent with momentum (SGDM...

Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons’ input/output (I/O) mapping complexity. Deep…

To drive behavior, the cortex must bridge sensory cues with future outcomes. However, the principles by which cortical networks learn such sensory-behavioural transformations remain largely elusive. H...

How do brain circuits learn to generate behaviour? While significant strides have been made in understanding learning in artificial neural networks, applying this knowledge to biological networks...

The temporal order of neuronal firing within bursts of population spiking in the human anterior temporal lobe is dependent on the category as well as the identity of the individual stimulus, and this ...