@article {66023,
	title = {Mapping Low-Dimensional Dynamics to High-Dimensional Neural Activity: A Derivation of the Ring Model from the Neural Engineering Framework.},
	journal = {Neural Computation},
	volume = {33},
	year = {2021},
	month = {2021 Mar 01},
	pages = {827-52},
	abstract = {<p>Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity-the neural engineering framework. We analytically solve the framework for the classic ring model-a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.</p>
},
	issn = {1530-888X},
	doi = {10.1162/neco_a_01361},
	author = {Barak, Omri and Romani, Sandro}
}
@article {65361,
	title = {Cell-type specific outcome representation in primary motor cortex.},
	journal = {Neuron},
	volume = {107},
	year = {2020},
	month = {2020 Sep 9},
	pages = {954-71},
	abstract = {<p>Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, {\textquotedblleft}success{\textquotedblright}- and {\textquotedblleft}failure{\textquotedblright} related neurons that develop with training and report end-result of trials. In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trial{\textquoteright}s outcome. Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.</p>
},
	doi = {10.1016/j.neuron.2020.06.006},
	author = {Lavzin, Maria and Levy, Shahar and Benisty, Hadas and Dubin, Uri and Brosh, Zohar and Aeed, Fadi and Mensh, Brett D. and Schiller, Yitzhak and Meir, Ron and Barak, Omri and Talmon, Ronen and Hantman, Adam W. and Schiller, Jackie}
}
