Kundu et al recorded EEG from humans during a short-term memory task. They found fronto-parietal coherence in different frequencies were associated with different memory functions. Alpha coherence was associated with maintenance of the information in memory. By contrast, the top-down filtering of distractions was associated with beta coherence. This adds to mounting evidence that specific frequency bands are associated with specific types of cortical processing like, for example, beta and top-down control.
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Earl Miller is quoted in a Time article about the dangers of multitasking:
You Asked: Are My Devices Messing With My Brain? Time (May 13, 2015)
http://time.com/3855911/phone-addiction-digital-distraction/““Every time you switch your focus from one thing to another, there’s something called a switch-cost,” says Dr. Earl Miller, a professor of neuroscience at Massachusetts Institute of Technology. “Your brain stumbles a bit, and it requires time to get back to where it was before it was distracted.” ““You’re not able to think as deeply on something when you’re being distracted every few minutes,” Miller adds. “And thinking deeply is where real insights come from.”
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Miller Lab alumnus, Andreas Nieder, continues his epic investigations into the neural basis of number sense. Here, Viswanathan and Nieder show that training to make numerosity judgments sharpens neural selectivity in frontal cortex but not in parietal cortex. It seems that the number representations in parietal cortex are innate whereas in the frontal cortex, they are learned.
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A new review by Sprague et al provides an interesting take on cognitive capacity, information loss and attention.
Visual attention mitigates information loss in small- and large-scale neural codes
Thomas C. Sprague, , Sameer Saproo, John T. Serences -
Miller Lab alumnus David Freedman and colleagues present a model that shows how categorical neural activity can develop through learning. As a result of top-down influences from decision neurons, categorical representations develop in neurons that show choice-correlated activity fluctuations. They test the model via recordings from parietal cortex.
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Ardid et al use spike shape and firing variability to identify different classes in the primate prefrontal cortex. They ID four classes of broad spiking neurons and three classes of narrow spiking (inhibitory) neurons. These cell classes show different strength of synchrony to local field potential oscillations at specific frequencies. The authors suggest this reflects canonical cortical circuits with different functions.
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Georgia Gregoriou and colleagues review the role of oscillations in the focusing of attention. They suggest that different frequencies reflect the biophysical properties of different cell types and that synchrony allows selective routing of information through these cell populations.
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Frequency-specific hippocampal-prefrontal interactions during associative learning
Brincat, S.L. and Miller, E.K. (2015) Nature Neuroscience, advanced online publicationAbstract:
Much of our knowledge of the world depends on learning associations (for example, face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive and mnemonic abilities are akin to those of humans. We found functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, whereas HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC and increased with learning. Rapid object associative learning may occur in PFC, whereas HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands. -
MIT News Office: Neurons hum at different frequencies to tell the brain which memories it should store.
New discovery from the Miller LabAnne Trafton | MIT News Office
February 23, 2015
Our brains generate a constant hum of activity: As neurons fire, they produce brain waves that oscillate at different frequencies. Long thought to be merely a byproduct of neuron activity, recent studies suggest that these waves may play a critical role in communication between different parts of the brain.A new study from MIT neuroscientists adds to that evidence. The researchers found that two brain regions that are key to learning — the hippocampus and the prefrontal cortex — use two different brain-wave frequencies to communicate as the brain learns to associate unrelated objects. Whenever the brain correctly links the objects, the waves oscillate at a higher frequency, called “beta,” and when the guess is incorrect, the waves oscillate at a lower “theta” frequency. Read more
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MIT News Office: Neurons hum at different frequencies to tell the brain which memories it should store.
New discovery from the Miller LabAnne Trafton | MIT News Office
February 23, 2015
Our brains generate a constant hum of activity: As neurons fire, they produce brain waves that oscillate at different frequencies. Long thought to be merely a byproduct of neuron activity, recent studies suggest that these waves may play a critical role in communication between different parts of the brain.A new study from MIT neuroscientists adds to that evidence. The researchers found that two brain regions that are key to learning — the hippocampus and the prefrontal cortex — use two different brain-wave frequencies to communicate as the brain learns to associate unrelated objects. Whenever the brain correctly links the objects, the waves oscillate at a higher frequency, called “beta,” and when the guess is incorrect, the waves oscillate at a lower “theta” frequency. Read more