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

Ruff and Cohen report evidence that attention can increase or decrease neural correlations depending on whether the neurons have the same or different functions.

Our lab and others (e.g., Buschman and Miller, 2007; Bastos et al 2012) has suggested that top-down (feedback) vs bottom-up (feedforward) cortical processing is mediated by synchrony between cortical areas at different frequencies: lower (e.g., beta band) for top-down vs higher (e.g., gamma band) for bottom-up.  These two different frequency bands allow top-down vs bottom-signals to multiplex through the same circuits, much as different FM radio stations multiplex through the airwaves.  They may also allow cortical microcircuits to engage in helpful things like predictive coding (Bastos et al., 2012).

Schmiedt et al (2014) provide new evidence for this.    They recorded neural activity in visual area V4 after damage to primary visual area V1.  V4 is higher in the cortical hierarchy, so V1 has a bottom-up influence on V4.  They found that damage to V1 decreased the gamma in V4 that follows appearance of a visual stimulus.  That is consistent with gamma carrying bottom-up or feedforward signals, lost after V1 damage.  By contrast, V4 beta activity was minimally affected, reflecting the unaffected top-down influence on V4   Normally there is beta suppression during visual stimulation, presumably because the bottom-up inputs overwhelm or suppress beta-mediated top-down processing.  After V1 damage, this suppression of top-down beta rhythms was diminished, presumably because it was no longer suppressed by bottom-up influences from V1.

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 »

Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ. Canonical microcircuits for predictive coding. Neuron. 2012 Nov 21;76(4):695-711. doi:
10.1016/j.neuron.2012.10.038. Review.

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.

Visual attention increases synchrony of neural activity in visual cortex.  Fries and colleagues showed that synchronization differs for putative excitatory (broad-spiking) and inhibitory (narrow-spiking) neurons.  The inhibitory neurons synchronize in the gamma band twice as strongly as excitatory neurons but the excitatory neurons synchronize to an earlier phase than inhibitory neurons.  Further, attention increases gamma synchrony for the most active neurons but decreases synchrony for the least active neurons.  These results show that attention-related neural synchrony is not uniform but instead an orchestration between different neuron types showing different types of synchrony.  This lends further support for the role of neural synchrony in attention.

Van der Linden et al used computer generated images to study categorization in the human brain.  They found that the frontal cortex showed sensitivity to the features diagnostic for the categories, which is consistent with results from animal studies at the neuron level.

Jack Gallant and crew used FMRI to examine scene processing in the human brain.  They found that scenes activated many regions of anterior visual cortex and that the scene categories capture the co-occurrence of the objects that compose the scenes.

Max Riesenhuber and colleagues used EEG to examine the time course of shape and category signals in the human brain.  Neural adaptation for category changes was seen in frontal cortex and then subsequently in temporal cortex.  This supports the hypothesis that shape categories are formed by shape signals from temporal cortex that converge and form explicit category representations in frontal cortex.  A late category signal in temporal cortex is consistent with category signals feeding back from frontal to temporal cortex.

Markov et al provide an excellent review and analysis of the anatomy of visual cortex and beyond.  The show that supragranular layers contain highly segregated feedforward and feedback pathways.  Their analysis of the detailed anatomy revealed that feedback connections are more numerous and have more levels than feedforward connections.  By contrast, infragranular layers are less hierarchical and may be more involved in point-to-point cross-talk than feedforward or feedback processing.  Markov et al map the feedforward and feedback pathways to recent observations that feedforward vs feedback communication is supported by gamma vs beta cortical oscillations.

For more on the role of oscillations in feedforward and feedback cortical communication, see our review:
Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention.  Current Opinion in Neurobiology.  23:216–222  View PDF »

Miller Lab alumnus Andreas Nieder shows that dopamine (DA) has different effects on two different classes of neurons in the prefrontal cortex.  For neurons with a short latency visual response, DA suppressed activity but preserved their signal to noise ratio.  For neurons with a longer visual latency (exclusively broad-spiking, putative pyramidal neurons), DA increased excitability and enhanced signal/noise ratio.  Thus, DA can shape how the prefrontal cortex processes bottom-up sensory inputs.
Jacob et al

A Neuron Preview for Miller Lab graduate student Simon Kornblith’s paper on a network for scene processing:
Scene Areas in Humans and Macaques by Epstein and Julian

Here’s the original post on Simon’s paper and a link to it:
A Network For Scene Processing

Jutras et al find a relationship between hippocampal theta and visual exploration via saccadic eye movements.  Saccades caused a theta reset that was predictive of subsequent recognition of visual images.  Enhanced theta power before stimulus onset was also predictive of recognition.

Miller Lab graduate student Simon Kornblith publishes a paper in Neuron from work in his old lab.  By combining FMRI with electrode recording and stimulation, they found an area in the occipitotemporal cortex that has many scene-selective neurons, the lateral place patch (LPP).  By stimulating it, they discover connections to several other cortical areas, including a medial place patch (MPP) in the parahippocampal gyrus.  Elegant and important work, Simon, congratulations!  Now, get back to work. 🙂

Cowell and Cottrell trained a computational model on images used in fMRI studies of object and face processing.  They used multivariate pattern analysis and were able to replicate evidence for a specialized face area even though the model had no specialized processing for faces.  The authors suggest that fMRI evidence for a specialized face area should be interpreted with caution.

Hohl et al use a task with richer behavioral output to better establish a link between neural activity and behavior.

Nicole Rust and crew show how the perirhinal cortex can take signals from the inferior temporal cortex and sort out visual targets from distractors.
Pagan et al (2013)

Pannunzi et al propose a model of visual category learning in which bottom-up sensory inputs to the inferior temporal cortex are sculpted by top-down inputs from the prefrontal cortex (PFC). The PFC improves signal to noise by enhancing the category-relevant features of the stimuli.

Miller Lab work cited:
Freedman, D.J., Riesenhuber, M., Poggio, T., and Miller, E.K. (2001) Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 291:312-316. View PDF »

Freedman, D.J., Riesenhuber, M., Poggio, T., and Miller, E.K (2003) A comparison of primate prefrontal and inferior temporal cortices during visual categorization. Journal of Neuroscience, 23(12):5235-5246. View PDF »

Meyers, E.M., Freedman, D.J., Kreiman, G., Miller, E.K., and Poggio, T. (2008) Dynamic population coding of category information in the inferior temporal cortex and prefrontal cortex. Journal of Neurophysiology. 100:1407-1419. View PDF »

Muhammad, R., Wallis, J.D., and Miller, E.K. (2006) A comparison of abstract rules in the prefrontal cortex, premotor cortex, the inferior temporal cortex and the striatum. Journal of Cognitive Neuroscience, 18: 974-989. 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 »

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

In this week’s NY Times, Susana Martinez-Conde reminds us that our visual system works by detecting change.