An excellent review by Matt Shapiro and crew on an important topic. They discuss complementary roles and bidirectional interactions between the prefrontal cortex and hippocampus.
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Working memory is limited in capacity. As you load more “stuff” into working memory, errors increase. Bays shows how this may happen. Errors with increasing working memory load may be due to decreased signal strength of spiking neurons. Humans can increase the precision of high priority stimuli in working memory at the expense of low priority stimuli. The reduction in drive to neurons representing high priority stimuli can explain this tradeoff.
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Noudoost, Clark, and Moore deactivated the frontal eye fields (FEF) and recorded from visual cortical area V4. This disrupted saccades to targets but *increased* pre-saccade activity in V4. V4 neurons, however, showed reduced discrimination of the target stimulus. It seems that the FEF provides details about the saccade target to visual cortex.
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It has long been known (since my dissertation – ahem) that repetitions of a visual stimulus result in reduced spiking activity of individual neurons. This is curious because repetition does not weaken the perception of the stimulus. If spiking of individual neurons alone is responsible for perception, why doesn’t the perception weaken? Brunet et al showed that stimulus repetition produces increases in gamma band synchrony (40-90 Hz) within and between higher and lower order visual cortical areas. The increased synchrony can maintain efficacy of signalling of the stimulus despite the decrease of neuron spiking. Gamma-band synchrony of the spikes increases in general but decreases for weakly driven neurons. Thus, stimulus repetition may prune the neural representation of a stimulus while increased gamma synchronization increases neuron signalling, resulting in a leaner and meaner stimulus representation. This lends further support for the role in gamma-band synchrony in bottom-up sensory processing (e.g., Buschman and Miller, 2007).
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 »Incidentally, the effect of stimulus repetition on spiking activity was my first first-author publication:
Miller, E.K., Gochin, P.M., and Gross, C.G. (1991) A habituation-like decrease in the responses of neurons in inferior temporal cortex of the macaque. Visual Neuroscience 7:357-362. -
Quentin et al examined the relationship between white matter connectivity between the frontal and parietal cortices and the improvement of visual perception by beta oscillatory synchrony between them. They used diffusion imaging to examine the white matter connectivity and used transcranial magnetic stimulation (TMS) over the right frontal eye fields (FEF) to induce beta oscillations. Individuals that showed greater perceptual improvement with the beta TMS also had stronger white matter connectivity.
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The Oxford Handbook of Attention is a veritable who’s who of attention research. (Sorry that it costs $149 USD).
Check out the table of contents:Part A: Introduction
1. Current landscape and historical context, Michael Posner
Part B: Theoretical Models of Attention
2. Feature integration and guided search, Jeremy Wolfe
3. Perceptual/Executive load theory, Polly Dalton and Nilli Lavie
4. A multi-level account of selective attention, Sabine Kastner and John Serences
5. Large-scale network model of control, Marsel Mesulam and Professor Anna Christina Nobre
6. Multiple-demand network and adaptive coding, Mark Stokes and John Duncan
Part C: Spatial Attention
7. Spatial covert attention: Perceptual Modulation, Marisa Carrasco
8. Spatial orienting and attentional capture, Jan Theeuwes
9. Neural systems of spatial attention (fMRI), Diane Beck and Sabine Kastner
10. The time course of spatial attention: Insights from event-related brain potentials,Martin Eimer
11. Neuronal Mechanisms of Spatial Attention in Visual Cerebral Cortex, Marlene Cohen and John Maunsell
12. Cellular mechanisms of attentional control: Frontal, Jacqueline Gottlieb
13. Neuronal mechanisms of attentional control: Frontal cortex, Kelsey L. Clark, Behrad Noudoost, and Robert J. Schafer and Professor Tirin Moore
14. Neural mechanisms of Spatial Attention in the Visual Thalamus, Yuri B. Saalmann and Sabine Kastner
15. Attentional Functions of the Superior Colliculus, Richard J. Krauzlis
16. Orienting attention: a crossmodal perspective, Charles Spence
17. Neuronal Dynamics and the Mechanistic Bases of Selective Attention, Charles E.Schroeder, Jose L. Herrero and Saskia Haegens
18. The neuropharmacology of attention, Trevor Robbins
19. Developing attention and self-regulation in childhood, Michael Posner
Part D: Non-spatial Attention
20. Feature- and object-based attentional modulation in the human visual system,Miranda Scolari, Edward F. Ester, and John Serences
21. Object- and feature-based attention: monkey physiology, Stefan Treue
22. The Role of Brain Oscillations In The Temporal Limits of Attention, Kimron Shapiro and Simon Hanslmayr
23. Dynamic Attention, Patrick Cavanagh, Lorella Battelli, and Alex O. Holcombe
24. Temporal orienting, Anna Christina Nobre
Part E: Interactions between Attention and Other Psychological Domains
25. Attention, Motivation, and Emotion, Luiz Pessoa
26. Attention and executive functions
27. Neural mechanisms for the executive control of attention, Earl K. Miller and Timothy J. Buschman
28. Memory and Attention, Brice A. Kuhl and Marvin M. Chun
29. Attention and decision-making, Christopher Summerfield and Tobias Egner
30. Attention and action, Heiner Deubel
Part F: Attention-related Disorders
31. Attention and awareness, Geraint Rees
32. Attention and Aging, Theodore P. Zanto & Adam Gazzaley
33. Unilateral Spatial Neglect, Guiseppe Vallar
34. Neurological disorders of attention, Sanjay Manohar, Valerie Bonnelle and Masud Husain
35. Balint’s syndrome and the Study of Attention, Lynn C. Robertson
36. Rehabilitation of Attention Functions, Ian H. Robertson and Redmond G O’Connell
Part G: Computational Models
37. Theory of visual attention, Claus Bundesen and Thomas Habekost
38. Bottom up and contextual effects, Laurent Itti and Ali Borji
39. Bayesian models, Angela Yu
Part H: Conclusions
40. Outlook and Future Directions, Anna Christina Nobre and Sabine Kastner -
Sussillo reviews the use of recurrent neural networks (RNNs) to study cortical neurons. RNNs can explain the high-dimensional, mixed-selectivity properties and oscillatory temporal dynamics of cortical neurons. They share many features of cortical networks including feedback, nonlinearity, and parallel and distributed computing
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Rey et al recorded local field potentials and neuron spikes from the human medial temporal lobe during a recognition task. Single-neuron responses were preceded by a global increase in theta oscillations and a local and stimulus-specific increase in gamma oscillations. The LFPs responses were correlated with conscious recognition and neuron spiking was time-locked to the LFPs. They suggest that theta reflects a global recognition signal whereas phase-locked of neurons to gamma reflects activation of local circuits that represent the recognized stimulus.
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Genovesio et al trained monkeys to judge whether red square or blue circle were farther from a reference point. Even though information about the previous trial was irrelevant to the current trial, prefrontal cortex neurons conveyed the outcome of the previous trial and other irrelevant information about it. Information about previous outcomes can often be helpful. This study shows that this is automatically tracked by the prefrontal cortex even when it is not helpful.
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Bahlmann et al studied the human prefrontal cortex using a task with two different types of stimuli (spatial vs language) and three levels of abstraction. They found a rostro-caudal organization based on level abstraction (more anterior = more abstract).