Neurons in LIP contribute to two distinct stages of processing during a search task. The working memory for the sought-after feature and then the focusing of attention on target location.
Levichkina, E., Saalmann, Y. B., & Vidyasagar, T. R. (2017). Coding of spatial attention priorities and object features in the macaque lateral intraparietal cortex. Physiological Reports, 5(5), e13136.
A review on issues to consider when studying the brain by perturbing (and measuring) it. Very thoughtful.
Navigating the Neural Space in Search of the Neural Code
Mehrdad Jazayer and Arash Afraz
An excellent and comprehensive review of the brain basis of visual attention. Worthy of yours.
Moore, T., & Zirnsak, M. (2017). Neural Mechanisms of Selective Visual Attention. Annual Review of Psychology, 68, 47-72.
A model of the collaboration and distribution of function between the prefrontal cortex and parietal cortex.
Working memory and decision making in a fronto-parietal circuit model
John D Murray, Jorge H Jaramillo, Xiao-Jing Wang
The anterior cingulate and FEF coordinate through theta and beta phase synchronization between spikes in one and local field potential in the other.
Babapoor-Farrokhran, S., Vinck, M., Womelsdorf, T., & Everling, S. (2017). Theta and beta synchrony coordinate frontal eye fields and anterior cingulate cortex during sensorimotor mapping. Nature Communications, 8, 13967.
The brain monitors simultaneous sensory input by “time division multiplexing”, a rhythmic juggling of the two streams of information.
Evidence for time division multiplexing of multiple simultaneous items in a sensory coding bottleneck
Valeria C Caruso, Jeffrey T Mohl, Chris Glynn, JungAh Lee, Shawn M Willett, Azeem Zaman, Rolando Estrada, Surya Tokdar, Jennifer M Groh
Still think that single neurons with specific functions rule the brain? Let us persuade you otherwise. We argue that cognitive control stems from dynamic, context-dependent population coding.
Stokes, M., Buschman, T.J., and Miller, E.K. (2017) Dynamic coding for flexible cognitive control. The Wiley Handbook of Cognitive Control, The Wiley Handbook of Cognitive Control, Edited by Tobias Egner, John Wiley & Sons, 2017(Chichester, West Sussex, UK). View PDF
New Miller Lab paper:
Jia, N., Brincat, S.L., Salazar-Gomez, A., Panko, M., Guenther, F. and Miller, E.K. (2017) Decoding of intended saccade direction in an oculomotor brain-computer interface. Journal of Neural Engineering, 2017. https://doi.org/10.1088/1741-2552/aa5a3e
Objective. To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from of hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for point and click computer operation as used in most current AAC systems.
The title says it all. A comprehensive review on how salience is determined and used by the brain.
Veale, R., Hafed, Z. M., & Yoshida, M. (2017). How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling. Phil. Trans. R. Soc. B, 372(1714), 20160113.
This review of the neural basis of working memory argues that working memory is a property of many brain areas working in concert. Prefrontal vs sensory cortical areas differ in their degrees of abstraction and how they are tied to action. They argue that the persistent activity that seems to underlie working memory is a general product of cortical networks.
Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J. D. (2017). The Distributed Nature of Working Memory. Trends in Cognitive Sciences.
I would add that persistent activity may not be so persistent:
Lunqvist, M., Rose, J., Herman, P, Brincat, S.L, Buschman, T.J., and Miller, E.K. (2016) Gamma and beta bursts underlie working memory. Neuron, published online March 17, 2016. View PDF »
Stokes, M., & Spaak, E. (2016). The Importance of Single-Trial Analyses in Cognitive Neuroscience. Trends in cognitive sciences.
Stokes, M. G. (2015). ‘Activity-silent’working memory in prefrontal cortex: a dynamic coding framework. Trends in cognitive sciences, 19(7), 394-405.
Stokes, M., Buschman, T.J., and Miller, E.K. (in press) Dynamic coding for flexible cognitive control. Wiley Handbook of Cognitive Control.