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Over 200 scientists have signed a letter to the New York Times (below) supporting Sue and condemning the article. It is a biased and unfair attack on someone who is no longer here to defend herself.
Detailed response from MIT.
MIT News: Faculty at MIT and beyond respond forcefully to an article critical of Suzanne CorkinNews article
STAT: MIT challenges New York Times over book on famous brain patientOriginal statement (signed by over 200 neuroscientists):
We are a community of scientists who are disturbed by a recent New York Times Magazine article (“The Brain That Couldn’t Remember”), which describes Professor Suzanne Corkin’s research in what we believe are biased and misleading ways. A number of complex issues that occur in research with humans, from differing interpretations of data among collaborators to the proper disposition of confidential data, are presented in a way so as to call into question Professor Suzanne Corkin’s integrity. These assertions are contrary to everything we have known about her as a scientist, colleague, and friend. Professor Corkin dedicated her life to using the methods of neuropsychology to illuminate how the brain gives rise to the mind, especially how different regions of the human brain support different aspects of memory. Her scientific contributions went far beyond her work with the amnesic patient HM (whose well being she protected for decades), with major contributions to understanding clinical disorders such as Alzheimer’s and Parkinson’s disease. She was a highly accomplished scientist, an inspiring teacher, a beloved mentor to students and faculty, and a champion of women in science. While her recent passing is a great loss to our field, her passion and commitment continue to inspire all of us. We only regret that she is not able to respond herself.
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Nice review and test of a hypothesis about the role of transient beta oscillations in cortical processing.
Sherman et al (2016) Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice
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For much of the history of modern neuroscience, it has been a assumed that the neuron is the functional unit of the brain. But now there is increasing evidence that ensembles of neurons, not individuals, are the functional units. One line of evidence is that many neurons in higher cortical areas have “mixed selectivity” , responses to diverse combinations of variables; they don’t signal one “message”. Thus, their activity only makes sense when simultaneously considering the activity of other neurons. In fact, we (Rigotti et al., 2013; Fusi et al., 2016) have shown that mixed selectivity gives the brain the computational horsepower needed for complex behavior.
In this paper, Dehaqani et al show that simultaneously recorded prefrontal cortex neurons have high-dimensional, mixed-selectivity, representations and convey more information as a population than even individuals. This was especially true for parts of visual space that were weakly encoded by single neurons. Less-informative neurons were recruited into ensemble to fully encode visual space.
Prefrontal neurons expand their representation of space by increase in dimensionality and decrease in noise correlation. Mohammad-Reza Dehaqani, Abdol-Hossein Vahabie, Mohammadbagher Parsa, Behrad Noudoost, Alireza Soltani
doi: http://dx.doi.org/10.1101/065581Further 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 »
Yuste, Rafael. “From the neuron doctrine to neural networks.” Nature Reviews Neuroscience 16.8 (2015): 487-497.
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Woolgar et al provide a meta-analysis of experiments using multivoxel pattern analysis in FMRI. They show that cortical areas traditionally though to be visual, auditory or motor, primarily (though not exclusively) code visual, auditory, and motor information. However, the frontoparietal cortex is hypothesized to a multiple-demand network and it shows domain generality, coding multisensory and rule information.
Woolgar, Alexandra, Jade Jackson, and John Duncan. “Coding of visual, auditory, rule, and response information in the brain: 10 years of multivoxel pattern analysis.” Journal of cognitive neuroscience (2016).
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LIP has been the area for studying motion direction discrimination as model of decision-making. In this paper, Katz et al show that deactivation of LIP has little effect on that model task. Deactivating an upstream area, MT, where decision signals are weaker, however, caused a big deficit.
Dissociated functional significance of decision-related activity in the primate dorsal stream. Leor N. Katz, Jacob L. Yates, Jonathan W. Pillow & Alexander C. Huk Nature.
Sure, this is a cautionary tale of correlates does not equal causation. But it is important not to over-interpret the results of lesions/deactivations. They identify *bottlenecks* in neural processing, not contributions. Just because there is no effect of deactivation doesn’t mean that a given area doesn’t contribute. MT could be providing the raw materials that a number of downstream areas, including LIP, use for decision-making. This doesn’t mean that LIP doesn’t contribute to decisions, it just means that it is not the only area that contributes.
This is in line with recent work showing that neural processing is more distributed than previously thought. For example, see:
Siegel, M., Buschman, T.J., and Miller, E.K. (2015) Cortical information flow during flexible sensorimotor decisions. Science. 19 June 2015: 1352-1355. View PDF » -
An excellent, comprehensive review of the neurobiology of decision-making by David Freedman and John Asaad.
Neuronal Mechanisms of Visual Categorization: An Abstract View on Decision Making
David J. Freedman and John A. Assad, Annual Review of Neuroscience, 2016
DOI: 10.1146/annurev-neuro-071714-033919 -
This study shows the role of alpha and beta oscillations in the prefrontal cortex and frontal eye fields in a classic test of cognitive control: anti-saccades. It also shows how these oscillatory patterns develop with adulthood.
Hwang, Kai, et al. “Frontal preparatory neural oscillations associated with cognitive control: A developmental study comparing young adults and adolescents.” NeuroImage (2016).
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Miller Lab Alumnus Andreas Nieder tells you everything you need to know about the brain substrates of the sense of number:
Nieder, Andreas. “The neuronal code for number.” Nature Reviews Neuroscience (2016).
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Nice review of past work on the neurobiology of working memory and capacity limits:
Constantinidis, Christos, and Torkel Klingberg. “The neuroscience of working memory capacity and training.” Nature Reviews Neuroscience (2016).
Although there is a caveat: More recent work suggests that the substrate of working memory is *not* sustained spiking activity. That is an artifact of cross-trial averaging. “Delay activity” is more sparse and bursty on single trials. This suggests a different memory substrate.
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, Mark G. “‘Activity-silent’working memory in prefrontal cortex: a dynamic coding framework.” Trends in cognitive sciences 19.7 (2015): 394-405.