A video-driven model of response statistics in the primate middle temporal area

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.neunet.2018.09.004 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Neurons in the primate middle temporal area (MT) encode information about visual motion and binocular disparity. MT has been studied intensively for decades, so there is a great deal of information in the literature about MT neuron tuning. In this study, our goal is to consolidate some of this information into a statistical model of the MT population response. The model accepts arbitrary stereo video as input. It uses computer-vision methods to calculate known correlates of the responses (such as motion velocity), and then predicts activity using a combination of tuning functions that have previously been used to describe data in various experiments. To construct the population response, we also estimate the distributions of many model parameters from data in the electrophysiology literature. We show that the model accounts well for a separate dataset of MT speed tuning that was not used in developing the model. The model may be useful for studying relationships between MT activity and behavior in ethologically relevant tasks. As an example, we show that the model can provide regression targets for internal activity in a deep convolutional network that performs a visual odometry task, so that its representations become more physiologically realistic.MitacsCrossWing In

Extensive spontaneous plasticity of corticospinal projections after primate spinal cord injury.

Although axonal regeneration after CNS injury is limited, partial injury is frequently accompanied by extensive functional recovery. To investigate mechanisms underlying spontaneous recovery after incomplete spinal cord injury, we administered C7 spinal cord hemisections to adult rhesus monkeys and analyzed behavioral, electrophysiological and anatomical adaptations. We found marked spontaneous plasticity of corticospinal projections, with reconstitution of fully 60% of pre-lesion axon density arising from sprouting of spinal cord midline-crossing axons. This extensive anatomical recovery was associated with improvement in coordinated muscle recruitment, hand function and locomotion. These findings identify what may be the most extensive natural recovery of mammalian axonal projections after nervous system injury observed to date, highlighting an important role for primate models in translational disease research

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades

Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labelling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.Comment: 10 pages, 6 figures in Frontiers in Neuromorphic Engineering, special topic on Benchmarks and Challenges for Neuromorphic Engineering, 2015 (under review

Spatial summation of individual cones in human color vision.

The human retina contains three classes of cone photoreceptors each sensitive to different portions of the visual spectrum: long (L), medium (M) and short (S) wavelengths. Color information is computed by downstream neurons that compare relative activity across the three cone types. How cone signals are combined at a cellular scale has been more difficult to resolve. This is especially true near the fovea, where spectrally-opponent neurons in the parvocellular pathway draw excitatory input from a single cone and thus even the smallest stimulus projected through natural optics will engage multiple color-signaling neurons. We used an adaptive optics microstimulator to target individual and pairs of cones with light. Consistent with prior work, we found that color percepts elicited from individual cones were predicted by their spectral sensitivity, although there was considerable variability even between cones within the same spectral class. The appearance of spots targeted at two cones were predicted by an average of their individual activations. However, two cones of the same subclass elicited percepts that were systematically more saturated than predicted by an average. Together, these observations suggest both spectral opponency and prior experience influence the appearance of small spots