@proceedings {2121,
	title = {A machine learning approach to predict episodic memory formation},
	year = {2016},
	pages = {539 - 544 },
	address = {Princeton, NJ },
	doi = {10.1109/CISS.2016.7460560},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7460560\&newsearch=true\&queryText=A\%20machine\%20learning\%20approach\%20to\%20predict\%20episodic\%20memory\%20formation},
	author = {Hanlin Tang and Jedediah Singer and Matias J. Ison and Gnel Pivazyan and Melissa Romaine and Elizabeth Meller and Victoria Perron and Marlise Arlellano and Gabriel Kreiman and Melissa Romaine and Adrianna Boulin and Rosa Frias and James Carroll and Sarah Dowcett}
}
@article {2565,
	title = {Predicting episodic memory formation for movie events},
	journal = {Scientific Reports},
	year = {2016},
	month = {10/2016},
	abstract = {<p>Episodic memories are long lasting and full of detail, yet imperfect and malleable. We quantitatively evaluated recollection of short audiovisual segments from movies as a proxy to real-life memory formation in 161 subjects at 15 minutes up to a year after encoding. Memories were reproducible within and across individuals, showed the typical decay with time elapsed between encoding and testing, were fallible yet accurate, and were insensitive to low-level stimulus manipulations but sensitive to high-level stimulus properties. Remarkably, memorability was also high for single movie frames, even one year post-encoding. To evaluate what determines the efficacy of long-term memory formation, we developed an extensive set of content annotations that included actions, emotional valence, visual cues and auditory cues. These annotations enabled us to document the content properties that showed a stronger correlation with recognition memory and to build a machine-learning computational model that accounted for episodic memory formation in single events for group averages and individual subjects with an accuracy of up to 80\%. These results provide initial steps towards the development of a quantitative computational theory capable of explaining the subjective filtering steps that lead to how humans learn and consolidate memories.</p>
},
	doi = {10.1038/srep30175},
	url = {http://www.nature.com/articles/srep30175},
	author = {Hanlin Tang and Jedediah Singer and Matias J. Ison and Gnel Pivazyan and Melissa Romaine and Rosa Frias and Elizabeth Meller and Adrianna Boulin and James Carroll and Victoria Perron and Sarah Dowcett and Arellano, Marlise and Gabriel Kreiman}
}
@inbook {1130,
	title = {Visual cognitive adventures of single neurons in the human medial temporal lobe},
	booktitle = {Single neuron studies of the human brain. Probing cognition},
	year = {2014},
	chapter = {8},
	author = {Mormann, F and Matias J. Ison and Quiroga, RQ and Christof Koch and Itzhak Fried and Gabriel Kreiman}
}
