If you are interested in cognition, brain rhythms, and, especially, brain rhythms and cognition, this is the place to be.
http://cogrhythms.bu.edu/conference.htm
The Rhythmic Dynamics and Cognition Conference is a two-day event sponsored by the Cognitive Rhythms Collaborative (CRC). The program will be held at the Brain Building (Building 46) on the MIT campus (Room 3002) and will include lectures, a reception, and a poster session.
Speakers include:
- Pascal Fries, (Ernst Strungmann Institute (ESI), Frankfurt)
- Elizabeth Buffalo (Emery University)
- Charlie Schroeder (Nathan Kline Institute)
- Peter Brown (University College London)
- Fiona Le Beau (Newcastle University)
- Earl Miller (MIT)
- Charlie Wilson (University of Texas, San Antonio)
- Peter Uhlhaas (University of Glasgow)
- Christa van Dort (Mass. General Hospital)
- Markus Siegal (University of Tubingen)
- Robert Knight (UC Berkely)
It was recently reported that low-voltage, non-invasive brain stimulation improves mathematical abilities. Does it? Here’s a cautionary discussion:
Does Brain Stimulation Make You Better at Maths?
Visual attention modulates several aspects of neural coding. There is an increase in firing rate and changes in temporal dynamics: a reduction of neural variance and noise correlation as well as changes in oscillatory synchronization. The authors used glutamatergic receptor activation, combined with neurophysiological recording to show that the NMDA receptor is responsible for attention -related changes in neural temporal dynamics but not for increases in firing rate. Thus, different neurophysiological mechanisms that underlie attention can be dissociated at the receptor level. This supports the hypothesis that attention is mediated in part by the temporal dynamics of neural activity, not merely changes in the firing rate of neurons, and that the changes temporal dynamics are not simply a byproduct of changes in firing rate.
Herrero et al (2013) Neuron
For a further discussion of the role of temporal dynamics in attention see:
Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
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 »
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 »
This paper uses EEG to examine the timecourse of synchronization patterns across the brain during a simple cognitive task. First, there was low frequency (delta) synchrony, which may reflect global, long-range synchronization and may help organize the higher frequency synchrony that followed. Then, there was higher frequency (gamma) synchrony, which may reflect reorganization of local circuits for bottom-up processing of sensory inputs. Finally, there was beta synchrony, which may reflect the final stage of top-down processing in the task. Gamma and beta synchronization has been shown to be correlated with bottom-up vs top-down cortical processing (Buschman and Miller, 2007; Chanes et al, 2013; Ibos et al, 2013). This study identifies and confirms some of the proposed mechanisms of global information integration in the brain.
Brazdil et al (2013)
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 »
Chanes et al (2013) Journal of Neuroscience
Ibos et al (2013) Journal of Neuroscience
For years, neurophysiologists have observed that many neurons in higher-level cortex have “weird” properties. They activate across a wide range of seemingly unrelated conditions and thus don’t seem to fit into the traditional view of brain function in which each neuron has a single function or message. They were often considered a “complicating nuisance” at best or dismissed at worst. It turns out that these mixed selectivity neurons may be the most critical for complex behavior and cognition. They greatly expand the brain’s computational power.
Read MIT press release: Complex brain function depends on flexibility
The paper:
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (2013) The importance of mixed selectivity in complex cognitive tasks. Nature View PDF doi:10.1038/nature12160
In this week’s NY Times, Susana Martinez-Conde reminds us that our visual system works by detecting change.
http://www.nytimes.com/2013/05/19/opinion/sunday/vision-is-all-about-change.html
This study shows oscillatory synchrony between different frontal lobe areas during preparatory focusing of attention. Interestingly, the same neurons participated in attention and motor networks, only at different frequencies. This is further evidence that rhythmic synchrony may allow neurons to multiplex their functions.
Totah et al Cerebral Cortex 2013
Miller Lab work cited:
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 »
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 »
Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202. Designated a Current Classic by Thomson Scientific as among the most cited papers in Neuroscience and Behavior. View PDF »
Rainer, G., Rao, S.C., and Miller, E.K. (1999) Prospective coding for objects in the primate prefrontal cortex. Journal of Neuroscience, 19:5493-5505. View PDF »
The human prefrontal cortex may not be special in terms of its size relative to other primates, but it is still a pretty special.
http://blogs.scientificamerican.com/beautiful-minds/2013/05/16/gorillas-agree-human-frontal-cortex-is-nothing-special/?utm_source=feedly
Want to know what it does? Here’s a start:
Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202. Designated a Current Classic by Thomson Scientific as among the most cited papers in Neuroscience and Behavior. View PDF »
The title says it all. Oscillations are useful for all sorts of things.
Synchrony in 32 metronomes
A number of laboratories have been suggesting that top-down vs bottom-up attention signals may be transmitted across the cortex via neural synchronization at beta vs gamma frequencies, respectively (Buschman and Miller, 2007; Bosman et al, 2012; Gregoriou et al, 2009, see review by Wang 2010). Chanes et al (2013) tested this by entraining the human frontal cortex at those frequencies. This produced the predicted top-down vs bottom-up effects on behavior: Beta modulated (top-down) response criterion whereas gamma modulated (bottom-up) perceptual sensitivity. This supports observations that different frequencies of neural synchrony support feedback vs feedforward cortical processing. It also shows how neural synchrony supports multiplexing of function: Activity from the same neurons has different functional outcomes depending on their rhythmic dynamics.
Chanes et al (2013)
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 »
G.G. Gregoriou, S.J. Gotts, H. Zhou, R. Desimone (2009) High-frequency, long-range coupling between prefrontal and visual cortex during attention Science, 324 (2009), pp. 1207–1210.
C.A. Bosman, J.-M. Schoffelen, N. Brunet, R. Oostenveld, A.M. Bastos, T. Womelsdorf, B. Rubehn, T. Stieglitz, P. De Weerd, P. Fries (2012) Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron, 75 (2012), pp. 875–888
X.-J. Wang (2010) Neurophysiological and computational principles of cortical rhythms in cognition. Physiol. Rev., 90 (2010), pp. 1195–1268
Personally, my favorite is the 3rd prize winner: Look at Boston as if you are a giant with eyes 200 yards apart.
http://illusionoftheyear.com/
A nice review of the brain areas and neural mechanisms underlying attention and set-shifting.
http://www.sciencedirect.com/science/article/pii/S0166432813002453
MIT neuroscientist Suzanne Corkin, author of the new book “Permanent Present Tense,” tells of her nearly five decades studying a man whose memory loss transformed science.
Tirin Moore and Karl Deisseroth named Howard Hughes Medical Institute Investigators. Congrats to both.
The Miller Lab is proud to be in the same scientific family as Tirin. “Pappa” Charlie Gross must be also be proud. See: Neurotree
Rhythmic synchrony between neurons has been suggested as a mechanism for establishing communication channels between neurons. However, this hypothesis has been criticized because of observations that the exact frequency of gamma oscillations bounces around too much to provide a stable communication channel. (BTW, it doesn’t seem to bother anyone that single neuron activity also bounces around).
In this study, Roberts et al record from V1 and V2 simultaneously while presenting gratings of varying contrast. Even though the gamma frequencies changed with stimulus contrast and fluctuated over time, coherence remained stable between V1 and V2. Thus, rhythmic synchrony can provide a stable channel for neural communication.
http://www.cell.com/neuron/abstract/S0896-6273(13)00227-4?utm_source=feedly
For further reading on the role of rhythmic synchrony in neural communication see:
Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 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
Ibos et al examined the relative roles of the frontal eye fields (FEF) and lateral intraparietal area (LIP) in bottom-up vs top-down selection. They found that intrinsic salience (bottom-up) was signaled in LIP before the FEF whereas extrinsic salience (top-down) was signaled in FEF before LIP. The authors conclude that bottom-up vs top-down control of attention predominates in the parietal vs frontal cortex, respectively.
http://www.jneurosci.org/content/33/19/8359.abstract
As noted by the authors, this is highly consistent with our lab’s observations that attention signals for bottom-up capture by stimulus salience (pop-out) vs top-down search originate from parietal vs frontal cortex, respectively.
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 »
We also showed that rhythmicity of frontal cortical top-down signals may control the periodic shifts of attention during visual search that leads to eventual selection of a target:
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 »
Our very limited ability to hold multiple thoughts in mind is apparent to anyone who has tried to talk on the phone and email at the same time. Traditionally, this is thought of as a limitation in the capacity of working memory, the “mental scratchpad” used to keep important information “online” after it is no longer available. However, Ed Vogel and crew show that the bottleneck is not in working memory per se but instead present during processing of visual stimuli while they are still visible. Thus, the bottleneck is not (just) in memory but also in the processing of sensory inputs. In other words, capacity limitations seem to be a fundamental limit in neural processing related to consciousness in general, not a unique byproduct of working memory.
http://www.jneurosci.org/content/33/19/8257.abstract
As noted by the authors, this is consistent with our finding that when capacity is exceeded, information is lost in a bottom-up fashion during initial processing of visual stimuli:
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 »
Two different features of a vibrotactile stimulus are encoded by rate and temporal codes in primary sensory cortex. The amplitude was reflected in the overall firing rate of neurons whereas the frequency was reflected in the oscillatory phase-locking of spikes.
Harvey et al, 2013 PLOS Biology
The two coding schemes, rate vs temporal codes, are often debated as if they are in opposition and mutually exclusive. They are not and this paper elegantly demonstrates this important point. And this is not just limited to vibratory tactile stimuli. Schroeder and colleagues have argued that all sensory processing involves periodic sampling and rhythmic entrainment of cortical neurons. (We, for example, periodically sample vision via periodic eye movements and shifts of attention.)
Plus, rhythmic synchrony allows multiplexing, not only by adding another coding dimension as shown here, but also by allowing neurons to communicate different messages to different targets depending on whom they are synchronized with (and how, e.g., phase, frequency). That way, the same neurons can participate in different functions yet still convey unambiguous messages. For a brief discussion of this latter point and why we need multiplexing in the cortex see: Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
This paper reports FMRI in humans performing a task requiring first-order rules (S-R associations with a specific motor output) and second order rules that govern the use of the first-order rules. Cerebellum lobules that project to the prefrontal cortex show activation for both types of rules. This suggests that the cerebellum contributes to rule-based behaviors even when the rules are higher-order and don’t directly involve a motor command.
http://cercor.oxfordjournals.org/content/23/6/1433.abstract
For further reading on the role of rules in cognition and their neural implementation see:
Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202. 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
Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Single neurons in the prefrontal cortex encode abstract rules. Nature, 411:953-956. View PDF »
Miller Lab Research Scientist Vicky Puig quoted in a article in El Pais. The Picower Institute at MIT is called “one of the best neuroscience centers in the world”. One of the?
In Spanish, but that’s why we have Google Translate.
http://elpais.com/elpais/2013/05/01/eps/1367419412_106866.html
When an operation left Henry Molaison unable to form new memories, he became the most important patient in the history of brain science. Neurologist Suzanne Corkin reveals what it was like to work with ‘HM’ for 46 years
http://www.guardian.co.uk/science/2013/may/05/henry-molaison-amnesiac-corkin-book-feature?CMP=twt_gu
A nice article in the Wall Street Journal describing Jack Gallant’s recent FMRI work. They didn’t just subtract conditions and come up with a typical imaging map with one or a few isolated bits of activation. Jack L. Gallant, Tolga Çukur and colleagues used sophisticated analyses to find the relationship between the patterns of whole brain activity and the content of videos watched by the subjects. This revealed wide networks, not isolated patches, of neurons engaged by attention to different things in the video (humans vs vehicles, etc).
It also showed how dynamic and flexible the brain is. When subjects looked for humans, large portions of the cortex were sensitive to humans and less sensitive to vehicles. When subjects looked for vehicles, large portions of the cortex became vehicle detectors. Many of the same brain areas were involved in multiple networks, changing when people changed the focus of their attention. Thus, rather than the cortex being composed of modules with strict specializations, high-level information is spread across wide-ranging cortical networks of neurons that participate in many different functions, adapting their properties to current cognitive demands.
We have long argued that mixed selectivity, adaptive coding neurons are crucial for hallmarks of cognition like flexibility. And in forthcoming paper (Rigotti et al), we show computationally that you can’t build a complex brain w/o them.
For a brief discussion of this issue, read this Preview of a paper by Stokes et al:
Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
And stay tuned for this paper:
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (in press) The importance of mixed selectivity in complex cognitive tasks. Nature.
Two people describe the same shape in the sky 2 hours and 2 time zones apart. Thy must have seen the same real thing, right? Maybe, but maybe not.
http://www.huffingtonpost.com/roger-marsh/witnesses-1200-miles-apart_b_3179709.html
This could just be a coincidence driven by large numbers. Millions of people view the sky every night over thousands of moments, night after night. That is a huge number of observations. People report all sorts of weird stuff in the sky. The chance that two people happen to think they see the same thing on the same night in a timely fashion has a very low probability. But with so many observations of the sky, night after night, that low probability can nonetheless result in real numbers. Note that there are only two isolated witnesses. It wasn’t like a crowd of 100 saw each manifestation.
I always say: It would be weird if coincidences never happened.
Granger causality is designed to measure effect, not mechanim.
http://www.frontiersin.org/Neuroinformatics/10.3389/fninf.2013.00006/full
Eichenbaum and crew show that neurons in different parts of the CA3 area of the rat hippocampus acquire different memories at different levels. More specific memories are rapidly acquired by dorsal CA3 whereas the ventral CA3 neurons more gradually accumulate information across training episodes to form broader, more generalized context representations.
http://www.jneurosci.org/content/33/18/8079.abstract
The interplay between brain systems that rapidly acquire specific information and systems that more slowly build generalizations may be a common theme of brain function (Seger and Miller, 2010). We have suggested a similar relationship between the basal ganglia and frontal cortex for goal directed learning. Think of it as the basal ganglia learning the pieces of a puzzle and the prefrontal cortex gradually putting the puzzle together (Miller and Buschman, 2007).
The idea is that rapid plasticity in the basal ganglia learns simple, specific things very quickly. The output of the basal ganglia repeatedly “trains” slower plasticity in the frontal cortex (Pasupathy and Miller, 2005). In this way, the frontal cortex gradually forms abstractions from the common structure in the information fed to it by the basal ganglia. We have seen direct evidence for this during category learning (Antzoulatos and Miller, 2011). Early in learning, when animals try to solve the task by learning about each specific exemplar, basal ganglia neurons lead the charge. Then, when the number of exemplars becomes overwhelming and the animals finally start abstracting the categories, the prefrontal cortex takes over.
For further reading see:
Antzoulatos,E.G. and Miller, E.K. (2011) Differences between neural activity in prefrontal cortex and striatum during learning of novel, abstract categories. Neuron. 71(2): 243-249. View PDF »
Seger, C.A. and Miller, E.K. (2010) Category learning in the brain. Annual Review of Neuroscience, Vol. 33: 203-219. View PDF »
Miller, E.K. and Buschman, T.J. (2007) Rules through recursion: How interactions between the frontal cortex and basal ganglia may build abstract, complex, rules from concrete, simple, ones. In: The Neuroscience of Rule-Guided Behavior, S. Bunge & J. Wallis (eds.), Oxford University Press. View PDF »
Pasupathy, A. and Miller, E.K. (2005) Different time courses for learning-related activity in the prefrontal cortex and striatum. Nature, 433:873-876. View PDF »