Behind on the literature in decision-making and sensory guided action? Here’s our summary.
July 1, 2015
My lab members and I did a “Literature Blitz” today: each person in the group gave a short presentation, including only a single figure, on a recent finding in the area of decision-making and sensory guided action. Short presentations like this don’t allow for the in-depth discussions we have when we read a single paper, but they give us a snapshot of a whole field that we can absorb in just a few hours. This inevitably broadens all of our perspectives.
1. From Kawai et al in Neuron: Motor cortex isn’t needed to support complex, sequenced movements that harvest rewards. However, motor cortex is required to learn these movements. The movies associated with this paper and incredible, definitely check them out
2. From R. Kiani et al in Neuron: Natural groupings of neurons, based on time varying response similarities, can define spatially segregated subnetworks. Surprisingly, these subnetworks have correlated noise, especially during quiet wakefulness when no stimuli are present. This approach suggests we might want to consider a new way to define cortical regions and subregions, especially in areas like the frontal lobe which has historically been difficult to parcellate.
3. From Strandbug-Peshkin at al in Science: This group fitted wild Kenyan baboons with GPS collars and worked out which factors determine their collective movements. The first part we might have guessed: the number of animals, and their commitment to a particular direction of movement play a large role in determining whether the other baboons will join the move. One aspect was surprising: although baboons have a strong social hierarchy, it doesn’t play much of a role in determine where animals go next. In other words, just because the king-of-the-pack goes north, it doesn’t mean the other baboons follow suit.
4. From Juavinett & Callaway in Current Biology: Here, the authors used intrinsic signal mapping to pinpoint multiple visual areas and then measured howthey differed in terms of their ability to represent complex motion. Specifically, they tracked whether individual neurons were sensitive to the pattern motion defined by a plaid created by two overlapping gratings. Similar to classic observations in monkey, there was a transition from primary areas which mainly reflected the pattern motion, to secondary areas (especially RL) which were more likely to respond to component motino.
5. From Chen et al in Nature Neuroscience: This paper showed that during learning of a level-press task, the spines of pyramidal neurons in primary motor cortex change dramatically. Further, they determined that this change was largely mediated by a specific class of interneurons, SOM+ neurons which preferentially target the apical dendrites of pyramidal neurons.
6. From Rohe & Noppeney in PLOS Biology: These authors used fMRI to evaluate how causal inference is performed in humans who must judge whether auditory and visual information bears on the same source. Their main observation is that this occurs hierarchically: in early sensory areas (A1 & V1), activity reflects the assumption that there are two sourcesof information, whereas in the anterior intraparietal sulcus, activity reflects the assumption that the two signals are from a common source and should be integrated.
7. From Murayama et al in Neuron: Projections from secondary motor cortex feedback to secondary somatosensory cortex to help shape information about texture in mice. This suggests that feedback projections play a key role in shaping sensory experience.
8. From Cooke et al in Nature Neuroscience: This paper coined a new term, “vidget”, which refers to a visually induced fidget. Apparently head-fixed mice are especially prone to these when they experience a novel visual stimulus, even a grating in an orientation they haven’t seen in a while. Using NMDA blockers and PV-ChR2 mice, the authors argue that memory for visual images, as evidenced by vidgets, requires area V1.
9. From M. Siegel et al in Science: Functions, such as knowledge of task context and visual responses, are shared, not compartmentalized, across cortical regions. Here. the authors recorded neurons in 6 cortical areas on a complex decision task and evaluated how representations changed from sensory to parietal to frontal regions. I liked the approach and hope the dataset will be further analyzed. By experimenting with different ways to combine neurons, the authors might learn more about the kinds of computations feasible in each area.