Bouchacourt and Buschman describe a two-layer model of working memory. A sensory layer feeds into an unstructured layer of neurons with random connections (i.e., “mixed-selectivity” type neurons). It is flexible but interference between representations results in a capacity limit. Sounds like working memory to me.
Bouchacourt, F., & Buschman, T. J. (2018). A Flexible Model of Working Memory. bioRxiv, 407700.
More about mixed-selectivity:
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 »
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 »
Super-cool paper by Andreas Nieder and crew. Frontal-parietal beta synchrony encodes the most recent numerical input. Theta synchrony distinguishes between different numerosities held in working memory. The spiking of mixed-selectivity neurons multiplexed both task-relevant and irrelevant stimuli but they were separated in different phases of theta oscillations. Powerful support that neural oscillations functionally organize spiking activty.
Jacob, S. N., Hähnke, D., & Nieder, A. (2018). Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate Cortex. Neuron, 99(3), 588-597.
More evidence for mixed-selectivity in the cortex. This time with voxels in the human brain.
Jackson, J., & Woolgar, A. (2018). Adaptive coding in the human brain: Distinct object features are encoded by overlapping voxels in frontoparietal cortex. Cortex.
Read more about 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 »
Interesting new work from Ito and Cole showing how network connectivity patterns is associated with representational flexibility.
Ito, T., & Cole, M. W. (2018). Network dimensionality underlies flexible representation of cognitive information. bioRxiv, 262626.
A model in which local parallel processors assemble to produce goal-directed behavior. A performance bottleneck comes from the routing stage, which learns to map inputs onto motor representations. This is very much like mixed-selectivity models of cortex.
Zylberberg, A., Slezak, D. F., Roelfsema, P. R., Dehaene, S., & Sigman, M. (2010). The brain’s router: a cortical network model of serial processing in the primate brain. PLoS computational biology, 6(4), e1000765.
For more about mixed selectivity see:
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 »
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 »
Enel et al use reservoir computing to understand how mixed selectivity dynamic in the prefrontal cortex support complex, flexible behavior. Reservoir computing (like mixed selectivity) involves inputs fed to a dynamical system that learns only at the output stage. They argue that this approach is good framework for understanding how cortical dynamics produce higher cognitive functions.
Enel, P., Procyk, E., Quilodran, R., & Dominey, P. F. (2016). Reservoir computing properties of neural dynamics in prefrontal cortex. PLoS computational biology, 12(6), e1004967.
For more about mixed selectivity see:
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 »
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 »
Lindsay, G.W., Rigotti, M., Warden, M.R., Miller, E.K., and Fusi, S. (2017) Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal Cortex. Journal of Neuroscience. 6 October 2017, 1222-17; DOI: https://doi.org/10.1523/JNEUROSCI.1222-17.2017 View PDF
Parthasarathy et al found that a distractor stimulus caused neural representations in the prefrontal cortex to morph into a different pattern but while still retaining information about the item in memory. This was due to mixed selectivity neurons. By contrast, the FEF had less mixed selectivity and the distractor caused it to lose information. Nice.
Mixed selectivity morphs population codes in prefrontal cortex
Aishwarya Parthasarathy, Roger Herikstad, Jit Hon Bong, Felipe Salvador Medina, Camilo Libedinsky & Shih-Cheng Yen
Nature Neuroscience (2017)
For further reading about mixed selectivity:
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 »
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 »
Driscoll et al tracked parietal cortex neurons over one month after mice learned and practiced a navigation task. The activity of individual neurons changed but information on the population level was stable. This is a nice demonstration of “mixed selectivity” in individual neurons and further evidence that the functional unit of the brain is neural ensembles, not individual neurons.
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N., & Harvey, C. D. (2017). Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell.
For further reading:
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 »
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 »
More evidence for mixed selectivity. Mixed selectivity is “a neural encoding scheme in which different task variables and behavioral choices are combined indiscriminately in a non-linear fashion within the same population of neurons. This scheme generates a high-dimensional non-linear representational code that allows for a simple linear readout of multiple variables from the same network of neurons” (Fusi et al., 2016). It adds computational horsepower to the brain. In this case, the evidence is from human parietal cortex.
Zhang, C. Y., Aflalo, T., Revechkis, B., Rosario, E. R., Ouellette, D., Pouratian, N., & Andersen, R. A. (2017). Partially Mixed Selectivity in Human Posterior Parietal Association Cortex. Neuron.
For further reading:
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 »
Well said, Howard Eichenbaum. Could agree more. The time is nigh.
Eichenbaum, H. (2017). Barlow versus Hebb: When is it time to abandon the notion of feature detectors and adopt the cell assembly as the unit of cognition?. Neuroscience Letters.