Gamma and beta bursts during working memory readout suggest roles in its volitional control
Lundqvist et al Nature Communications, in press.
Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory
Bastos et al PNAS, in press
Different levels of category abstraction by different dynamics in different prefrontal areas
Wutz et al Neuron, in press
Stay tuned for what they are about and what they mean. They add up to a new model of working memory.
The authors suggest a hybrid model of working memory. The current focus of attention is encoded by spiking activity. Other items held in the working memory that are not the current focus of attention are held by temporary changes in synaptic weights per the activity-silent models of Lundqvist and Stokes.
Manohar, S. G., Zokaei, N., Fallon, S. J., Vogels, T., & Husain, M. (2017). A neural model of working memory. bioRxiv, 233007.
For more on activity-silent models, see:
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., 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, (Chichester, West Sussex, UK). View PDF
Wasmuht, D. F., Spaak, E., Buschman, T. J., Miller, E. K., & Stokes, M. G. (2017). Intrinsic neuronal dynamics predict distinct functional roles during working memory. bioRxiv, 233171.
Stokes, M. G. (2015). ‘Activity-silent’working memory in prefrontal cortex: a dynamic coding framework. Trends in Cognitive Sciences, 19(7), 394-405.
Cavanagh et al test the sustained vs dynamic activity models of working memory. They find that sustained activity does not maintain the memory of a cue location/response past a distractor. Instead of sustained activity, they conclude that a dynamic population-level process underlies working memory.
Cavanagh, S. E., Towers, J. P., Wallis, J. D., Hunt, L. T., & Kennerley, S. W. (2017). Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex. bioRxiv, 231506.
Intrinsic neuronal dynamics predict distinct functional roles during working memory
Dante Francisco Wasmuht, Eelke Spaak, Timothy J. Buschman, Earl K. Miller, Mark G. Stokes
Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF) and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We found that cells with short timescales carried memory information relatively early during memory encoding in lPFC; whereas long timescale cells played a greater role later during processing, dominating coding in the delay period. We also observed a link between functional connectivity at rest and intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predicts complex neuronal dynamics during WM; ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.
Low-frequency synchrony between the anterior cingulate and orbitofrontal cortex is diminished when errors are made.
Fatahi, Z., Haghparast, A., Khani, A., & Kermani, M. (2017). Functional connectivity between Anterior Cingulate cortex and Orbitofrontal cortex during value-based decision making. Neurobiology of Learning and Memory.
An interesting contrast between the prefrontal cortex (PFC) and medial temporal lobe (MTL) in encoding temporal order. PFC neurons showed stronger “mixed selectivity” type encoding. They responded to a combination of an item and the order in which in appeared, only responding to specific items at specific times. By contrast, MTL neurons were mainly item-selective. They typically responded to an item, regardless of its order, but their firing rate was modulated by order.
Naya, Y., Chen, H., Yang, C., & Suzuki, W. A. (2017). Contributions of primate prefrontal cortex and medial temporal lobe to temporal-order memory. Proceedings of the National Academy of Sciences, 201712711.
Further reading on mixed selectivity:
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (2013) The importance of mixed selectivity in complex cognitive tasks. Nature, 497, 585-590, doi:10.1038/nature12160. View PDF »
Fusi, S., Miller, E.K., and Rigotti, M. (2016) Why neurons mix: High dimensionality for higher cognition. Current Opinion in Neurobiology. 37:66-74 doi:10.1016/j.conb.2016.01.010. View PDF »
A review by Mike Halassa and Sabine Kastner about our emerging understanding of the role of the thalamus in cognitive control.
Halassa, M. M., & Kastner, S. (2017). Thalamic functions in distributed cognitive control. Nature Neuroscience, 20(12), 1669.
The Dean of Prefrontal Cortex, Joaquin Fuster, breaks down prefrontal function along three lines: Executive attention, working memory, and decision-making.
Fuster, J. M. (2017). Prefrontal Executive Functions Predict and Preadapt. In Executive Functions in Health and Disease(pp. 3-19).
A thoughtful review and discussion of the issues involved in analyzing brain rhythms.
Jones, S. R. (2016). When brain rhythms aren’t ‘rhythmic’: implication for their mechanisms and meaning. Current opinion in neurobiology, 40, 72-80.