Showing papers by "Rodrigo Quian Quiroga published in 2016"

Improving data quality in neuronal population recordings

Abstract: Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.

Long-term coding of personal and universal associations underlying the memory web in the human brain.

Abstract: Neurons in the medial temporal lobe (MTL), a critical area for declarative memory, have been shown to change their tuning in associative learning tasks. Yet, it is unclear how durable these neuronal representations are and if they outlast the execution of the task. To address this issue, we studied the responses of MTL neurons in neurosurgical patients to known concepts (people and places). Using association scores provided by the patients and a web-based metric, here we show that whenever MTL neurons respond to more than one concept, these concepts are typically related. Furthermore, the degree of association between concepts could be successfully predicted based on the neurons' response patterns. These results provide evidence for a long-term involvement of MTL neurons in the representation of durable associations, a hallmark of human declarative memory.

Where is the ball? Behavioral and neural responses elicited by a magic trick.

Abstract: We present results from two experiments, in which subjects watched continuous videos of a professional magician repeatedly performing a maneuver in which a ball could "magically" appear under a cup. In all cases, subjects were asked to predict whether the ball would appear under the cup or not, while scalp EEG recordings were performed. Both experiments elicited strong and consistent behavioral and neural responses. In the first experiment, we used two blocks of videos with different probabilities of the ball appearing in the cup and found that, first, based on the behavioral responses, the subjects could track this probability change; and second, the different probabilities modulated the neural responses. In the second experiment, we introduced a control condition in which the magician performed the maneuver under the table, out of subjects' view. Comparing the two conditions (i.e., performing the maneuver within or out of the subjects' view), we found that, first, the magic trick dramatically biased the subjects' behavioral responses; and second, the two conditions led to differential neural responses, in spite of the fact that the stimulus triggering the evoked responses (seeing the ball in the cup) was exactly the same. Altogether, our results show how new insights into sensory and cognitive processing can be obtained using adapted magic tricks. Moreover, the approach of analyzing responses to continuous video presentations offers a more ecological setting compared to classic evoked potential paradigms, which are typically based on presenting static images flashed at the center of the screen.

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes

Abstract: Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant's gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views.