Emily L. Mackevicius

The paper develops a biophysical model of chromatophore arrays to generate dynamic skin patterns similar to those in cephalopods, showing that minimal central control can produce complex behaviors.

The paper studies urban rats in New York City using thermal imaging and AI to analyze movement and social communication patterns.

The paper identifies unique neural activity patterns in chickadees during caching events, suggesting they represent episodic memories.

The paper identifies an entorhinal-like structure in chickadees that exhibits similar anatomical and physiological features to the mammalian entorhinal cortex, suggesting conserved functions in hippocampal processing.

The paper introduces a method to track neurons across multiple in-vivo imaging sessions without requiring full field-of-view registration, validated on simulated and experimental datasets.

This study shows that neural sequences in songbirds can form without tutor exposure but become tightly associated with new syllables after tutoring, with some birds failing to learn due to pre-existing crystallized sequences.

This paper shows that a recurrent neural network can compute the successor representation when synaptic weights match transition probabilities, and demonstrates this with hippocampal data from random foraging.

The paper introduces seqNMF, a software toolbox for extracting and analyzing temporal sequences in high-dimensional neural data, showing its effectiveness on simulated and real neural datasets.