Biology subjects

Abedi Khoozani, P.

Publications and source records attributed to Abedi Khoozani, P..

4 recordsLinked to original sources

bioRxiv2024

Multisensory integration across reference frames with additive feed-forward networks

The integration of multiple sensory inputs is essential for human perception and action in uncertain environments. This process includes reference frame transformations as different sensory signals are encoded in different coordinate systems. Studies have shown multisensory integration in humans is consistent with Bayesian optimal inference. However, neural mechanisms underlying this process are still debated. Different population coding models have been proposed to implement probabilistic inference. This includes a recent suggestion that explicit divisive normalization accounts for empirical principles of multisensory integration. However, whether and how divisive operations are implemented in the brain is not well understood. Indeed, all existing models suffer from the curse of dimensionality and thus fail to scale to real-world problems. Here, we propose an alternative model for multisensory integration that approximates Bayesian inference: a multilayer-feedforward neural network of multisensory integration (MSI) across different reference frames trained on the analytical Bayesian solution. This model displays all empirical principles of multisensory integration and produces similar behavior to that reported in Ventral intraparietal (VIP) neurons in the brain. The model achieved this without a neatly organized and regular connectivity structure between contributing neurons, such as required by explicit divisive normalization. Overall, we show that simple feedforward networks of purely additive units can approximate optimal inference across different reference frames through parallel computing principles. This suggests that it is not necessary for the brain to use explicit divisive normalization to achieve multisensory integration. Significance StatementThis research presents an alternative model to divisive normalization models of multisensory integration in the brain. Our study demonstrates that a feed-forward neural network can achieve optimal multisensory integration across different reference frames without explicitly implementing divisive operations, challenging the long-held assumption that such operations are necessary for multisensory integration. The model displays all the empirical principles of multisensory integration, producing similar behavior to that reported in Ventral intraparietal (VIP) neurons in the brain. This work offers profound insights into the putative neural computations underlying multisensory processing.

neuroscience↗

bioRxiv2021

Integration of allocentric and egocentric visual information in a convolutional / multilayer perceptron network model of goal-directed gaze shifts

Allocentric (landmark-centered) and egocentric (eye-centered) visual codes are fundamental for spatial cognition, navigation, and goal-directed movement. Neuroimaging and neurophysiology suggest these codes are segregated initially, but then reintegrated in frontal cortex for movement control. We created and validated a theoretical framework for this process using physiologically constrained inputs and outputs. To implement a general framework, we integrated a Convolutional Neural Network (CNN) of the visual system with a Multilayer Perceptron (MLP) model of the sensorimotor transformation. The network was trained on a task where a landmark shifted relative to the saccade target. These visual parameters were input to the CNN, the CNN output and initial gaze position to the MLP, and a decoder transformed MLP output into saccade vectors. Decoded saccade output replicated idealized training sets with various allocentric weightings, and actual monkey data where the landmark shift had a partial influence (R2 = 0.8). Furthermore, MLP output units accurately simulated prefrontal response field shifts recorded from monkeys during the same paradigm. In summary, our model replicated both the general properties of the visuomotor transformations for gaze and specific experimental results obtained during allocentric-egocentric integration, suggesting it can provide a general framework for understanding these and other complex visuomotor behaviors.

neuroscience↗

bioRxiv2019

Reaching around obstacles accounts for uncertainty in coordinate transformations

When reaching to a visual target, humans need to transform the spatial target representation into the coordinate system of their moving arm. It has been shown that increased uncertainty in such coordinate transformations, for instance when the head is rolled toward one shoulder, leads to higher movement variability and influence movement decisions. However, it is unknown whether the brain incorporates such added variability in planning and executing movements. We designed an obstacle avoidance task in which participants had to reach with or without visual feedback of the hand to a visual target while avoiding collisions with an obstacle. We varied coordinate transformation uncertainty by varying head roll (straight, 30{degrees} clockwise and 30{degrees} counterclockwise). In agreement with previous studies, we observed that the reaching variability increased when the head was tilted. Indeed, head roll did not influence the number of collisions during reaching compared to the head straight condition, but it did systematically change the obstacle avoidance behavior. Participants changed the preferred direction of passing the obstacle and increased the safety margins indicated by stronger movement curvature. These results suggest that the brain takes the added movement variability during head roll into account and compensates for it by adjusting the reaching trajectories.

neuroscience↗

bioRxiv2017

Neck muscle spindle noise biases reaches in a multi-sensory integration task

Reference frame Transformations (RFTs) are crucial components of sensorimotor transformations in the brain. Stochasticity in RFTs has been suggested to add noise to the transformed signal due to variability in transformation parameter estimates (e.g. angle) as well as the stochastic nature of computations in spiking networks of neurons. Here, we varied the RFT angle together with the associated variability and evaluated the behavioral impact in a reaching task that required variability-dependent visual-proprioceptive multi-sensory integration. Crucially, reaches were performed with the head either straight or rolled 30deg to either shoulder and we also applied neck loads of 0 or 1.8kg (left or right) in a 3x3 design, resulting in different combinations of estimated head roll angle magnitude and variance required in RFTs. A novel 3D stochastic model of multi-sensory integration across reference frames was fitted to the data and captured our main behavioral findings: (1) neck load biased head angle estimation across all head roll orientations resulting in systematic shifts in reach errors; (2) Increased neck muscle tone led to increased reach variability, due to signal-dependent noise; (3) both head roll and neck load created larger angular errors in reaches to visual targets away from the body compared to reaches toward the body. These results show that noise in muscle spindles and stochasticity in general have a tangible effect on RFTs underlying reach planning. Since RFTs are omnipresent in the brain, our results could have implication for processes as diverse as motor control, decision making, posture / balance control, and perception.\n\nNew & NoteworthyWe show that increasing neck muscle tone systematically biases reach movements. A novel 3D multisensory integration across reference frames model captures the data well and provides evidence that the brain must have online knowledge of full body geometry together with the associated variability to accurately plan reach movements.

neuroscience↗