Matsushima and Tanaka examined the neural correlates of spatial working memory for one vs two locations. When the two locations were in the same (right or left) hemifield, the level neural activity was intermediate between that elicited from either cue alone. By contrast, when the cues were presented in opposite hemifields, neural activity to each cue was as if the cue was presented alone. This lends support to other observations (e.g., Buschman et al 2011) that there are independent capacities for working memory in the right and left visual hemifields, as if the brain was split down the middle.
Further reading:
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 »
See lectures from the Cognitive Rhythms Collaborative conference on Rhythmic Dynamics and Cognition, which took place on June 4, 2013 at MIT.
Talks:
Elizabeth Buffalo: Neural Signals for Memory and Space in the Primate Medial Temporal Lobe
Earl K. Miller: Cognition is Rhythmic
Robert Knight: Oscillations and Human PFC
Peter Ulhaas: Neural Oscillations in Schizophrenia: Perspectives from MEG
Charles Schroeder: Neural Substrates of Temporal Prediction in Active Sensing
Peter Brown: Beta Oscillations in the Human Basal Ganglia
Christa van Dort: Optogenetic Activation of Cholinergic Neurons in the PPT Induces REM Sleep
Rosalyn Doran: Dynamic Causal Modeling and Neurophysiology
Liam Paninski: Statistical Neuroscience
Astrid Prinz: How do rhythmically active circuits “analyze” their own activity?
Cognitive Rhythms Collaborative and Center for
Computational Neuroscience and Neural Technology
Spring 2014 Mini-Symposium
Frontiers in Non-Invasive Brain Stimulation
Wednesday, April 16, 2014 at 1 pm
Boston University Photonics Center 206
8 Saint Mary Street
Boston, MA 02215
1:00 – 1:15 Registration
1:15 – 2:00 Dr. Lucas Parra, City College of New York
“Transcranial electrical stimulation: mechanisms and targeting”
2:00 – 2:45 Dr. Tommi Raij, Massachusetts General Hospital
“Transcranial magnetic stimulation: mechanisms and navigation”
2:45 – 3:00 Break
3:00 – 3:45 Dr. Noah Philip, Alpert Medical School of Brown University
“Clinical implications of frequency dependent neuromodulation”
3:45 – 4:30 Dr. Alvaro Pascual-Leone, Harvard Medical School
“Characterizing and guiding brain networks with noninvasive brain stimulation”
4:30 Discussion / Reception
Registration free, but required. Email xiaoshi@bu.edu.
The title pretty much says it all. Attentional effects in honeybee brains. Neural responding to an object was enhanced relative to a competing object when the former object was chosen.
Song et al examined oscillatory activity in humans performing an attention task. They found that phase-locking between theta and alpha bands. An uninformative cue initiated alpha pulse at a theta rhythm that seemed to reflect alternating sampling of the uninformative and informative cues.
Tirin Moore and colleagues challenge the idea that neuron receptive fields shift in anticipation of eye movements, remapping from the pre-movement location to the post-movement location before the eye actually moves. They used multiple-electrode recording to provide a detailed maps of the receptive fields before and after movements. The receptive fields did not remap to reflect the post-movement location. Instead, all the receptive fields converged toward movement target. This suggest that the receptive field do not remap, they reflect attention to the movement target.
Recent studies have suggested that beta-band oscillatory synchrony plays a role in cognition. For example, different networks of neurons in the prefrontal cortex dynamically synchronize at beta as animals switch between two different task rules (Buschman et al., 2012) suggesting that beta synchrony is forming the neural ensembles for the rules. Different items simultaneously held in working memory line-up on different phases of beta/low-gamma oscillations, as if the brain is juggling the two items 30 times a second (Siegel et al., 2009). Hanslmayr et al disrupted these fine temporal relations by stimulating the human with beta-band TMS pulses. Beta stimulation of the left inferior frontal gyrus impaired memory formation while stimulation at other frequencies did not. There was a beta “echo” that outlasted the stimulation. Subjects with better beta entrainment showed more memory impairment. This lends support for the role of beta rhythms in cognition by showing a causal relationship between beta desynchrony and memory.
This paper:
Simon Hanslmayr, Jonas Matuschek, Marie-Christin Fellner, Entrainment of Prefrontal Beta Oscillations Induces an Endogenous Echo and Impairs Memory Formation, Current Biology, Available online 27 March 2014, ISSN 0960-9822
References
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
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
Mike Hasselmo and colleagues examined how the brain generalizes and infers new behaviors from previous experience. They trained different styles of neural network models to learn context-dependent behaviors (i.e., the response to four stimuli, A B C D, mapped onto two different responses X Y differently in different contexts). There were previously unseen stimuli whose response could be inferred from the other stimuli. They analyzed a Deep Belief Network, a Multi-Layer Perceptron, and the combination of a Deep Belief Network with a Linear Perceptron. The combination of the Deep Belief Network with Linear Perceptron worked best.
A Deep Belief Network has multiple layers of hidden units with connections between, but not within, the layers.
Peelen and Kastner extend studies of attention in the lab (using simple, neutral displays) to the real world (complex, meaningful scenes). They discuss interactions between what and where templates shaped by object familiarity, scene context, and memory
Think you can multitask well? Watanabe and Funahasi show that task information signaled by neurons in the prefrontal cortex degrade when animals perform a competing, concurrent task.
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.
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.
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.
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.
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
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.
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.
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).
Wutz et al used a visual forward-masking paradigm (mask then target) to study the neural basis of visual perception. The mask sometimes interfered with perception of the target. Higher beta power before the mask was associated with incorrect perception of the target. Evoked alpha phase reset was associated with correct target perception. This shows how oscillatory dynamics may play a role in carving successive visual inputs into separate perceptions.
This review examines evidence for a neurobiological explanation of executive functions of working memory. We suggest that executive control stems from information about task rules acquired by mixed selective, adaptive coding, multifunction neurons in the prefrontal cortex. Their output dynamically links the cortical-wide networks needed to complete the task. The linking may occur via synchronizing of neural rhythms, which may explain why we have a limited capacity for simultaneous thought.
Is conscious perception continuous or discrete? Asplund et al use the attentional blink paradigm to demonstrate that conscious perception is discrete and quantal. Attention increases the probability that a representation will reach awareness.
We have argued that cognition is discrete and quantal because the backbone of neural communication used for cognition is oscillatory. For this discussion see:
- Buschman,T.J. and Miller, E.K. (2010) Shifting the Spotlight of Attention: Evidence for Discrete Computations in Cognition. Frontiers in Human Neuroscience. 4(194): 1-9. View PDF »
The modal model of working memory (WM) is that of sustained activity in the prefrontal cortex. Sreenivasan et al argue for a more complex model. High-fidelity WM representations are maintained in sensory cortex while the prefrontal cortex instead maintains representations of multiple goal-related variables. These PFC representations serve to bias stimulus-specific activity in sensory cortex.
Roy et al show that the activity of neurons in the prefrontal cortex (pFC) are linked to categorical decisions. Monkeys were trained to categorize a set of computer-generated images as “cats” vs “dogs”. Then, they were shown ambiguous images were centered on a category boundary, that is, they were a mix of 50% of cats and dogs and therefore had no category information. The monkeys guessed at their category membership. Activity to the same ambiguous image differed significantly, depending on the monkey’s decision about the image’s category. Thus, pFC activity reflects categorical decisions.