Quantifying the spatial extent of the corollary discharge benefit to transsaccadic visual perception.

Extraretinal information, such as corollary discharge (CD), is hypothesized to help compensate for saccade-induced visual input disruptions. However, support for this hypothesis is largely for one-dimensional transsaccadic visual changes, with little comprehensive information on the spatial characteristics. Here we systematically mapped the two-dimensional extent of this compensation by quantifying the insensitivity to different displacement metrics. Human subjects made saccades to targets positioned at different amplitudes (4° or 8°) and directions (rightward, oblique, or upward). After the saccade the initial target disappeared and, after a blank period, reappeared at a shifted location-a collinear, diagonal, or orthogonal displacement. Subjects reported the perceived shift direction, and we determined the displacement detection based on the perceptual judgments. The two-dimensional insensitivity fields resulting from the perceptual thresholds had spatial features similar to the saccadic eye movement variability: 1) scaled with movement amplitude, 2) oriented (less sensitive to the change) along the saccade vector, and 3) approximately constant in shape when normalized by movement amplitude. In addition, comparing the postsaccadic perceptual estimate of the presaccadic target location to that based solely on the postsaccade visual error showed that overall the perceptual estimate was approximately 50% more accurate and 35% less variable than estimates based solely on this visual information. However, this relationship was not uniform: The benefit of extraretinal information was observed largely for displacements with a component parallel to the saccade vector. These results suggest a graded use of extraretinal information when forming the postsaccadic perceptual evaluation of transsaccadic environmental changes.

The effect of saccade metrics on the corollary discharge contribution to perceived eye location.

Corollary discharge (CD) is hypothesized to provide the movement information (direction and amplitude) required to compensate for the saccade-induced disruptions to visual input. Here, we investigated to what extent these conveyed metrics influence perceptual stability in human subjects with a target-displacement detection task. Subjects made saccades to targets located at different amplitudes (4°, 6°, or 8°) and directions (horizontal or vertical). During the saccade, the target disappeared and then reappeared at a shifted location either in the same direction or opposite to the movement vector. Subjects reported the target displacement direction, and from these reports we determined the perceptual threshold for shift detection and estimate of target location. Our results indicate that the thresholds for all amplitudes and directions generally scaled with saccade amplitude. Additionally, subjects on average produced hypometric saccades with an estimated CD gain <1. Finally, we examined the contribution of different error signals to perceptual performance, the saccade error (movement-to-movement variability in saccade amplitude) and visual error (distance between the fovea and the shifted target location). Perceptual judgment was not influenced by the fluctuations in movement amplitude, and performance was largely the same across movement directions for different magnitudes of visual error. Importantly, subjects reported the correct direction of target displacement above chance level for very small visual errors (<0.75°), even when these errors were opposite the target-shift direction. Collectively, these results suggest that the CD-based compensatory mechanisms for visual disruptions are highly accurate and comparable for saccades with different metrics.

A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling.

Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.

Reward-based learning for virtual neurorobotics through emotional speech processing.

Reward-based learning can easily be applied to real life with a prevalence in children teaching methods. It also allows machines and software agents to automatically determine the ideal behavior from a simple reward feedback (e.g., encouragement) to maximize their performance. Advancements in affective computing, especially emotional speech processing (ESP) have allowed for more natural interaction between humans and robots. Our research focuses on integrating a novel ESP system in a relevant virtual neurorobotic (VNR) application. We created an emotional speech classifier that successfully distinguished happy and utterances. The accuracy of the system was 95.3 and 98.7% during the offline mode (using an emotional speech database) and the live mode (using live recordings), respectively. It was then integrated in a neurorobotic scenario, where a virtual neurorobot had to learn a simple exercise through reward-based learning. If the correct decision was made the robot received a spoken reward, which in turn stimulated synapses (in our simulated model) undergoing spike-timing dependent plasticity (STDP) and reinforced the corresponding neural pathways. Both our ESP and neurorobotic systems allowed our neurorobot to successfully and consistently learn the exercise. The integration of ESP in real-time computational neuroscience architecture is a first step toward the combination of human emotions and virtual neurorobotics.

A circuit-level model of hippocampal place field dynamics modulated by entorhinal grid and suppression-generating cells.

Hippocampal "place cells" and the precession of their extracellularly recorded spiking during traversal of a "place field" are well-established phenomena. More recent experiments describe associated entorhinal "grid cell" firing, but to date only conceptual models have been offered to explain the potential interactions among entorhinal cortex (EC) and hippocampus. To better understand not only spatial navigation, but mechanisms of episodic and semantic memory consolidation and reconsolidation, more detailed physiological models are needed to guide confirmatory experiments. Here, we report the results of a putative entorhinal-hippocampal circuit level model that incorporates recurrent asynchronous-irregular non-linear (RAIN) dynamics, in the context of recent in vivo findings showing specific intracellular-extracellular precession disparities and place field destabilization by entorhinal lesioning. In particular, during computer-simulated rodent maze navigation, our model demonstrate asymmetric ramp-like depolarization, increased theta power, and frequency (that can explain the phase precession disparity), and a role for STDP and K(AHP) channels. Additionally, we propose distinct roles for two entorhinal cell populations projecting to hippocampus. Grid cell populations transiently trigger place field activity, while tonic "suppression-generating cell" populations minimize aberrant place cell activation, and limit the number of active place cells during traversal of a given field. Applied to place-cell RAIN networks, this tonic suppression explains an otherwise seemingly discordant association with overall increased firing. The findings of this circuit level model suggest in vivo and in vitro experiments that could refute or support the proposed mechanisms of place cell dynamics and modulating influences of EC.