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.
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.”
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.
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.
Choice-correlated activity fluctuations underlie learning of neuronal category representation
Tatiana A. Engel, Warasinee Chaisangmongkon, David J. Freedman & Xiao-Jing Wang
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.
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.
Frequency-specific hippocampal-prefrontal interactions during associative learning
Brincat, S.L. and Miller, E.K. (2015) Nature Neuroscience, advanced online publication
Abstract:
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 Lab
Anne 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
MIT News Office: Neurons hum at different frequencies to tell the brain which memories it should store.
New discovery from the Miller Lab
Anne 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
Sacchet et al find that synchronization between the prefrontal and somatosensory cortex may underlie the disengagement of attention. When a cue signaled that a forthcoming tactile stimulus should be ignored, there was first an increase in alpha (7-14 Hz) synchrony between representations of the unattended stimulus, followed by an increase in beta (15-29 Hz) synchrony. This study shows how frequency specific interactions between frontal cortex and sensory cortex may underlie the focusing of attention.
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.
Several lines of evidence suggests that searching a visual scene depends on an intrinsic periodicity. We scan the scene by moving the spotlight of attention at regular intervals. For example, Buschman and Miller (2009) found neurophysiological evidence in the frontal eye fields for regular shifts of attention at 25 Hz (i.e., every 40 ms). Dugue et al (2014) have now found evidence in humans using EEG recording and TMS stimulation in humans. They found successful search was associated with oscillations and phase resetting at 6 Hz. TMS applied at different intervals found disruption of search at a periodicity corresponding to 6 Hz. This was slower than reported by Buschman and Miller (2009), but that could be because Dugue et al used a more difficult search task.
This paper:
Theta Oscillations Modulate Attentional Search Performance Periodically
Laura Dugué, Philippe Marque, and Rufin VanRullen Journal of Cognitive Neuroscience, 2014
For further reading:
Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF »
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
Ruff and Cohen report evidence that attention can increase or decrease neural correlations depending on whether the neurons have the same or different functions.
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.
Hwang et al report increased alpha/beta power in the frontal cortex during a fundamental test of cognitive control, the anti-saccade task. There was increased cross-frequency coupling between alpha and beta bands and alpha, specifically, was predictive of trial-by-trial success. This adds to the growing body of evidence that beta oscillations are associated with cognition and that alpha is important for inhibitory control.
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 The Scientist’s “Hot Paper” for October 2009. View PDF »
Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF »
Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron. 76: 838-846. View PDF »
Dotson et al report both 0 and 180 deg phase synchrony between the prefrontal and parietal cortices during a working memory task, suggestion both formation and segregation of different functional networks by neural synchrony.