Brain stimulation: feeling the buzz.

A recent study demonstrates that artificially generated patterns of brain activity are surprisingly easy to sense. Brain areas that differ substantially in their functional specialization are remarkably similar in their ability to support this awareness.

Saccadic eye movements modulate visual responses in the lateral geniculate nucleus.

We studied the effects of saccadic eye movements on visual signaling in the primate lateral geniculate nucleus (LGN), the earliest stage of central visual processing. Visual responses were probed with spatially uniform flickering stimuli, so that retinal processing was uninfluenced by eye movements. Nonetheless, saccades had diverse effects, altering not only response strength but also the temporal and chromatic properties of the receptive field. Of these changes, the most prominent was a biphasic modulation of response strength, weak suppression followed by strong enhancement. Saccadic modulation was widespread, and affected both of the major processing streams in the LGN. Our results demonstrate that during natural viewing, thalamic response properties can vary dramatically, even over the course of a single fixation.

Natural grouping of neural responses reveals spatially segregated clusters in prearcuate cortex.

A fundamental challenge in studying the frontal lobe is to parcellate this cortex into "natural" functional modules despite the absence of topographic maps, which are so helpful in primary sensory areas. Here we show that unsupervised clustering algorithms, applied to 96-channel array recordings from prearcuate gyrus, reveal spatially segregated subnetworks that remain stable across behavioral contexts. Looking for natural groupings of neurons based on response similarities, we discovered that the recorded area includes at least two spatially segregated subnetworks that differentially represent behavioral choice and reaction time. Importantly, these subnetworks are detectable during different behavioral states and, surprisingly, are defined better by "common noise" than task-evoked responses. Our parcellation process works well on "spontaneous" neural activity, and thus bears strong resemblance to the identification of "resting-state" networks in fMRI data sets. Our results demonstrate a powerful new tool for identifying cortical subnetworks by objective classification of simultaneously recorded electrophysiological activity.

Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials.

Decision making is a complex process in which different sources of information are combined into a decision variable (DV) that guides action [1, 2]. Neurophysiological studies have typically sought insight into the dynamics of the decision-making process and its neural mechanisms through statistical analysis of large numbers of trials from sequentially recorded single neurons or small groups of neurons [3-6]. However, detecting and analyzing the DV on individual trials has been challenging [7]. Here we show that by recording simultaneously from hundreds of units in prearcuate gyrus of macaque monkeys performing a direction discrimination task, we can predict the monkey's choices with high accuracy and decode DV dynamically as the decision unfolds on individual trials. This advance enabled us to study changes of mind (CoMs) that occasionally happen before the final commitment to a decision [8-10]. On individual trials, the decoded DV varied significantly over time and occasionally changed its sign, identifying a potential CoM. Interrogating the system by random stopping of the decision-making process during the delay period after stimulus presentation confirmed the validity of identified CoMs. Importantly, the properties of the candidate CoMs also conformed to expectations based on prior theoretical and behavioral studies [8]: they were more likely to go from an incorrect to a correct choice, they were more likely for weak and intermediate stimuli than for strong stimuli, and they were more likely earlier in the trial. We suggest that simultaneous recording of large neural populations provides a good estimate of DV and explains idiosyncratic aspects of the decision-making process that were inaccessible before.