Publications

The cognitive abilities that characterize humans are thought to emerge from unique features of the cortical circuit architecture of the human brain, which include increased cortico–cortical connectivity.

The observation of place cells has suggested that the hippocampus plays a special role in encoding spatial information. However, place cell responses are modulated by several nonspatial variables and reported to be rather unstable.

Many behavioural tasks require the manipulation of mathematical vectors, but, outside of computational models1,2,3,4,5,6,7, it is not known how brains perform vector operations.

Over the course of a lifetime, we process a continual stream of information. Extracted from this stream, memories must be efficiently encoded and stored in an addressable manner for retrieval.

The convergence of internal path integration and external sensory landmarks generates a cognitive spatial map in the hippocampus. We studied how localized odor cues are recognized as landmarks by recording the activity of neurons in CA1 during a virtual navigation task.

Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract a mechanistic understanding of sensory computations.

The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in artificial neural networks trained to perform olfactory tasks.

Neural circuits and artificial neural networks (ANNs) process information by constructing and manipulating highly distributed representations [1, 2, 3, 4].

We introduce a new video analysis tool that combines the output of supervised pose estimation algorithms with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information.