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.
Due to a disruption of top-down attentional amplification.
Berkovitch, L., Dehaene, S., & Gaillard, R. (2017). Disruption of Conscious Access in Schizophrenia. Trends in Cognitive Sciences.
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.
The multidemand network is a set of frontoparietal areas in humans that are recruited for a wide range of cognitive-demanding tasks. Mitchell et al use FMRI connectivity analysis to identify a putative homolog in monkeys.
Mitchell, Daniel J., et al. “A Putative Multiple-Demand System in the Macaque Brain.” The Journal of Neuroscience 36.33 (2016): 8574-8585.
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
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.
The viewpoint that single neurons are the functional units of the brain rests on the hypothesis that each neuron has a single function or “message”. This notion has eroded under observations that cortical neurons do not seem to do one thing. Instead, neurons often respond to diverse combinations of task relevant variables, and often a variety of different variables with no apparent single function. Why would the brain evolve neurons with this “mixed selectivity”? In short, they add computational power. How? Read this paper and we”ll tell you.
Why neurons mix: high dimensionality for higher cognition,
Stefano Fusi, Earl K Miller, Mattia Rigotti,
Current Opinion in Neurobiology, Volume 37, April 2016, Pages 66-74, ISSN 0959-4388, http://dx.doi.org/10.1016/j.conb.2016.01.010.
Pascal Fries walks us through the latest in the communication through coherence theory.
Fries, Pascal. “Rhythms for Cognition: Communication through Coherence.”Neuron 88.1 (2015): 220-235.
Ranganath and Jacob walk us through the role that prefrontal cortex dopamine plays in cognition.
Ranganath, Ajit, and Simon N. Jacob. “Doping the Mind Dopaminergic Modulation of Prefrontal Cortical Cognition.” The Neuroscientist (2015): 1073858415602850.
One person’s (John Lisman) take on the state of the art of neuroscience in 2015.
The Challenge of Understanding the Brain: Where We Stand in 2015
Woolgar et al show preferential engagement of human frontoparietal networks with an increase in the complexity of task rules. Plus, the frontoparietal cortex adjusts representations to make rules that are more behavioral confusable easier to discriminate.
Miller, E.K. and Buschman, T.J. (2015) Working memory capacity: Limits on the bandwidth of cognition. Daedalus, Vol. 144, No. 1, Pages 112-122. View PDF
Why can your brain store a lifetime of experiences but process only a few thoughts at once? In this article we discuss “cognitive capacity” (the number of items that can be held “in mind” simultaneously) and suggest that the limit is inherent to processing based on oscillatory brain rhythms, or “brain waves,” which may regulate neural communication. Neurons that “hum” together temporarily “wire” together, allowing the brain to form and re-form networks on the fly, which may explain a hallmark of intelligence and cognition: mental flexibility. But this comes at a cost; only a small number of thoughts can fit into each wave. This explains why you should never talk on a mobile phone when driving.
Bressler and Richter review evidence that top-down processing in the cortex depends on synchronization of oscillatory rhythms between brain areas. More specifically, they hypothesize that beta band (13-30 Hz) synchrony conveys information about behavioral context (task information) to neurons in sensory cortex.
Braunlich et al compared stimulus identity vs categorization tasks using fMRI in humans. They applied a Constrained Principal Components Analysis. They found evidence for two distinct frontoparietal networks. One that rapidly analyzes the stimuli and a second one that more slowly categorizes them.
Virtually all studies of the neural basis of attention to date average effects across independently recorded neurons and across multiple trials. This is obviously artificial because attention has to be allocated on-the-fly, from moment-to-moment, not averaged across time. Trembly et al show that the current locus of attention can be decoded from ensembles of simultaneously recorded prefrontal cortex neurons from single trials. Decoding of these ensembles was stable over weeks. Nice.
Andre Bastos and colleagues review an update the communication-through-coherence (CTC) hypothesis. They propose that bi-directional cortical communication involves separate feedforward and feedback mechanisms that are separate both anatomically and spectrally.
Task Dependence of Visual and Category Representations in Prefrontal and Inferior Temporal Cortices
Jillian L. McKee, Maximilian Riesenhuber, Earl K. Miller, and David J. Freedman
Visual categorization is an essential perceptual and cognitive process for assigning behavioral significance to incoming stimuli. Categorization depends on sensory processing of stimulus features as well as flexible cognitive processing for classifying stimuli according to the current behavioral context. Neurophysiological studies suggest that the prefrontal cortex (PFC) and the inferior temporal cortex (ITC) are involved in visual shape categorization. However, their precise roles in the perceptual and cognitive aspects of the categorization process are unclear, as the two areas have not been directly compared during changing task contexts. To address this, we examined the impact of task relevance on categorization-related activity in PFC and ITC by recording from both areas as monkeys alternated between a shape categorization and passive viewing tasks. As monkeys viewed the same stimuli in both tasks, the impact of task relevance on encoding in each area could be compared. While both areas showed task-dependent modulations of neuronal activity, the patterns of results differed markedly. PFC, but not ITC, neurons showed a modest increase in firing rates when stimuli were task relevant. PFC also showed significantly stronger category selectivity during the task compared with passive viewing, while task-dependent modulations of category selectivity in ITC were weak and occurred with a long latency. Finally, both areas showed an enhancement of stimulus selectivity during the task compared with passive viewing. Together, this suggests that the ITC and PFC show differing degrees of task-dependent flexibility and are preferentially involved in the perceptual and cognitive aspects of the categorization process, respectively.
Dotson et al recorded neural activity in the prefrontal and parietal cortex during a working memory task. As previous studies have reported (e.g., Buschman and Miller, 2007) they found long range synchronization of 8-25 Hz oscillations between the areas. Interestingly, there found both phase synchronization at 0 and 180 degrees suggesting that the 0 deg phase synchrony helped form networks between the areas whereas the 180 deg (anti-phase) synchrony helped segregate different networks.
For further reading:
Buschman, T.J. and Miller, E.K. (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 315: 1860-1862 View PDF »
Womelsdorf et al found that bursts of neural activity in the prefrontal cortex and anterior cingulate synchronize at gamma and beta frequencies during focused attention. Non-burst activity did not show long-range synchronization. Burst synchronization may underlie the formation of long-range networks.
Gamma-band oscillations have been associated with holding information in working memory. Is it just a general increase in gamma or do gamma oscillations actually maintain and convey specific information? A new study by Honkanen et al suggests that it does contain information. The strength and topography of gamma oscillations reflected memorized visual features as well as the amount of information in working memory.
We’ve also shown that information about two different objects can be carried in different phases of gamma band oscillations:
Siegel, M., Warden, M.R., and Miller, E.K. (2009) Phase-dependent neuronal coding of objects in short-term memory. Proceedings of the National Academy of Sciences, 106: 21341-21346. View PDF »
Read commentary by Vogel and Fukuda
They got my experiment wrong, but spelled my name right:
Biology of Consciousness: Bridging the Mind-Body Gap?
The Huffington Post 10/30/14
Goal-direction and top-down control
Timothy J. Buschman and Earl K. Miller
We review the neural mechanisms that support top-down control of behavior. We suggest that goal-directed behavior utilizes two systems that work in concert. A basal ganglia-centered system quickly learns simple, fixed goal-directed behaviors while a prefrontal cortex-centered system gradually learns more complex (abstract or long-term) goal-directed behaviors. Interactions between these two systems allows top-down control mechanisms to learn how to direct behavior towards a goal but also how to guide behavior when faced with a novel situation.
Kopell et al provide an excellent review of the role of neural rhythms in brain function and argue that we need to know more than anatomy, no matter how detailed. We also need to connect it to an understanding of brain dynamics. They review our current knowledge of brain rhythms and identify (many) open questions.
At this risk of kvelling, in 2011 we published a paper (Buschman et al., 2011) showing independent visual working memory capacities in the right vs left visual hemifields. We were told “no way” and “that’s impossible”. Since then, a bunch of papers have supported this. Here’s another one.
Wang et al used FMRI and found that brain networks primarily interact with ipsilateral, not contralateral networks. Thus, the brain emphasizes processing within each hemisphere (visual hemifield) and minimizes across-hemisphere processing.
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
New Miller Lab paper in press and online at Neuron:
Antzoulatos EG and Miller EK (in press) Increases in Functional Connectivity between Prefrontal Cortex and Striatum during Category Learning. Neuron, in press.
Animals were trained to learn new category groupings by trial and error. Once they started to “get” the categories, there was an increase in beta-band synchrony between the prefrontal cortex and striatum, two brain areas critical for learning. By the time the categories were well-learned, the beta synchrony between the areas became category-specific, that is, unique sets of sites in the prefrontal cortex and striatum showed increased beta synchrony for the two different categories. This suggests that synchronization of brain rhythms can quickly establish new functional brain circuits and thus support cognitive flexibility, a hallmark of intelligence.
MIT Press release:
Synchronized brain waves enable rapid learning
MIT study finds neurons that hum together encode new information.
A well-known correlate of working memory is sustained neural activity that bridges short gaps in time. It is well-established in the primate brain, but what about birds? They have working memory. (In fact, there is a lot of classic work that detailed the behavioral characteristics of working memory in pigeons).
Miller Lab alumnus Andreas Nieder and crew trained crows to perform a working memory task and found sustained activity in the nidopallium caudolaterale (NCL). This is presumably a neural correlate of the crow’s visual working memory.
Now if crows could only pass that causality test.