@article {3331,
	title = {A computational perspective of the role of Thalamus in cognition},
	journal = {arxiv},
	year = {2018},
	abstract = {<pre>
Thalamus has traditionally been considered as only a relay source of cortical inputs, with hierarchically organized cortical circuits serially transforming thalamic signals to cognitively-relevant representations. Given the absence of local excitatory connections within the thalamus, the notion of thalamic {\textquoteleft}relay{\textquoteright} seemed like a reasonable description over the last several decades. Recent advances in experimental approaches and theory provide a broader perspective on the role of the thalamus in cognitively-relevant cortical computations, and suggest that only a subset of thalamic circuit motifs fit the relay description. Here, we discuss this perspective and highlight the potential role for the thalamus in dynamic selection of cortical representations through a combination of intrinsic thalamic computations and output signals that change cortical network functional parameters. We suggest that through the contextual modulation of cortical computation, thalamus and cortex jointly optimize the information/cost tradeoff in an emergent fashion. We emphasize that coordinated experimental and theoretical efforts will provide a path to understanding the role of the thalamus in cognition, along with an understanding to augment cognitive capacity in health and disease. </pre>
},
	keywords = {Artificial Intelligence, Cognitive Computing, Multi-objective Optimization, Recurrent Neural Network, Reservoir Computing, Thalamo-cortical system},
	url = {https://arxiv.org/abs/1803.00997},
	author = {Nima Dehghani and Ralf Wimmer}
}
@article {3764,
	title = {Recurrent computations for visual pattern completion},
	journal = {Proceedings of the National Academy of Sciences},
	year = {2018},
	month = {08/2018},
	abstract = {<p>Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology, and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when they were rendered \&lt;15\% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared with whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. The recurrent model was able to predict which images of heavily occluded objects were easier or harder for humans to recognize, could capture the effect of introducing a backward mask on recognition behavior, and was consistent with the physiological delays along the human ventral visual stream. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.</p>
},
	keywords = {Artificial Intelligence, computational neuroscience, Machine Learning, pattern completion, Visual object recognition},
	issn = {0027-8424},
	doi = {10.1073/pnas.1719397115},
	url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1719397115},
	author = {Hanlin Tang and Martin Schrimpf and William Lotter and Moerman, Charlotte and Paredes, Ana and Ortega Caro, Josue and Hardesty, Walter and David Cox and Gabriel Kreiman}
}
