Ott and Nieder show that stimulating dopamine D2 receptors enhancing working memory related activity in the prefrontal cortex.
Ott, Torben, and Andreas Nieder. “Dopamine D2 Receptors Enhance Population Dynamics in Primate Prefrontal Working Memory Circuits.”Cerebral Cortex (2016).
Nice paper showing that different task demands in different task stages engage different oscillatory bands in the prefrontal cortex.
Wimmer, Klaus, et al. “Transitions between Multiband Oscillatory Patterns Characterize Memory-Guided Perceptual Decisions in Prefrontal Circuits.”The Journal of Neuroscience 36.2 (2016): 489-505.
Sustained activity has long been thought to be the neural substrate of working memory. But the evidence is based on averaging neural activity across trials. A closer examination reveals that something more complex is happening and supports a very different model of working memory.
Gamma and Beta Bursts Underlie Working Memory
Mikael Lundqvist, Jonas Rose, Pawel Herman, Scott L. Brincat, Timothy J. Buschman, Earl K. Miller
Neuron, published online March 17, 2016
DOI: http://dx.doi.org/10.1016/j.neuron.2016.02.028
Summary
Working memory is thought to result from sustained neuron spiking. However, computational models suggest complex dynamics with discrete oscillatory bursts. We analyzed local field potential (LFP) and spiking from the prefrontal cortex (PFC) of monkeys performing a working memory task. There were brief bursts of narrow-band gamma oscillations (45–100 Hz), varied in time and frequency, accompanying encoding and re-activation of sensory information. They appeared at a minority of recording sites associated with spiking reflecting the to-be-remembered items. Beta oscillations (20–35 Hz) also occurred in brief, variable bursts but reflected a default state interrupted by encoding and decoding. Only activity of neurons reflecting encoding/decoding correlated with changes in gamma burst rate. Thus, gamma bursts could gate access to, and prevent sensory interference with, working memory. This supports the hypothesis that working memory is manifested by discrete oscillatory dynamics and spiking, not sustained activity.
Nice review of our putative neural correlate of one of the most studied cognitive functions: Working memory.
Riley, Mitchell R., and Christos Constantinidis. “Role of Prefrontal Persistent Activity in Working Memory.” Frontiers in systems neuroscience 9 (2015).
Eriksson et al discuss working memory, not as an isolated function, but as an interaction between component processes such as attention, propsection, perception and long-term memory.
Eriksson, Johan, et al. “Neurocognitive Architecture of Working Memory.”Neuron 88.1 (2015): 33-46.
Ester et al use human imaging to show that the parietal and frontal cortices maintain information about specific visual stimuli held in memory. This shows that top-down control of working memory and storage functions are not so separate. We kind of knew that from the neuron level, but very nice demo in humans.
Ester, Edward F., Thomas C. Sprague, and John T. Serences. “Parietal and Frontal Cortex Encode Stimulus-Specific Mnemonic Representations during Visual Working Memory.” Neuron (2015).
Advance copy of our new paper:
Kornblith, S. Buschman, T.J., and Miller, E.K. (2015) Stimulus Load and Oscillatory Activity in Higher Cortex. Cerebral Cortex. doi: 10.1093/cercor/bhv182 Journal link
Abstract:
Exploring and exploiting a rich visual environment requires perceiving, attending, and remembering multiple objects simultaneously. Recent studies have suggested that this mental “juggling” of multiple objects may depend on oscillatory neural dynamics. We recorded local field potentials from the lateral intraparietal area, frontal eye fields, and lateral prefrontal cortex while monkeys maintained variable numbers of visual stimuli in working memory. Behavior suggested independent processing of stimuli in each hemifield. During stimulus presentation, higher-frequency power (50–100 Hz) increased with the number of stimuli (load) in the contralateral hemifield, whereas lower-frequency power (8–50 Hz) decreased with the total number of stimuli in both hemifields. During the memory delay, lower-frequency power increased with contralateral load. Load effects on higher frequencies during stimulus encoding and lower frequencies during the memory delay were stronger when neural activity also signaled the location of the stimuli. Like power, higher-frequency synchrony increased with load, but beta synchrony (16–30 Hz) showed the opposite effect, increasing when power decreased (stimulus presentation) and decreasing when power increased (memory delay). Our results suggest roles for lower-frequency oscillations in top-down processing and higher-frequency oscillations in bottom-up processing.
The limited capacity of working memory has sometimes been explained as a limited number of memory “slots”. Paul Bays argues that working memory capacity is due to sharing of a continuous resource, namely a fixed amount of neural activity. Noise in this activity is the limiting factor.
Working memory has long been thought to depend on sustained firing of cortical neurons. However, single neurons showing unbroken sustained activity is rare and average population activity is often only strong near the end of a memory delay. Mark Stokes presents the intriguing hypothesis for activity-silent working memory. He suggests that working memory depends on patterns of functional connectivity between neurons, not sustained activity.
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.