Neural structure of a sensory decoder for motor control

Metrics of two-dimensional smooth pursuit are diverse across participants and stable across days

Smooth pursuit eye movements are used to volitionally track moving objects, keeping their image near the fovea. Pursuit gain, the ratio of eye to stimulus speed, is used to quantify tracking accuracy and is usually close to 1 for healthy observers. Although previous studies have shown directional asymmetries such as horizontal gain exceeding vertical gain, the temporal stability of these biases and the correlation between oculomotor metrics for tracking in different directions and speeds have not been investigated. Here, in testing sessions 4 to 10 days apart, 45 human observers tracked targets moving along two-dimensional trajectories. Horizontal, vertical, and radial pursuit gain had high test-retest reliability (mean intraclass correlation 0.84). The frequency of all saccades and anticipatory saccades during pursuit also had high test-retest reliability (intraclass correlation coefficients = 0.66 and 0.73, respectively). In addition, gain metrics showed strong intermetric correlation, and saccade metrics separately showed strong intercorrelation; however, gain and saccade metrics showed only weak intercorrelation. These correlations are likely to originate from a mixture of sensory, motor, and integrative mechanisms. The test-retest reliability of multiple distinct pursuit metrics represents a "pursuit identity" for individuals, but we argue against this ultimately contributing to an oculomotor biomarker.

A sensory-motor decoder that transforms neural responses in extrastriate area MT into smooth pursuit eye movements

Visual motion drives smooth pursuit eye movements through a sensory-motor decoder that uses multiple parallel neural pathways to transform the population response in extrastriate area MT into movement. We evaluated the decoder by challenging pursuit in monkeys with reduced motion reliability created by reducing coherence of motion in patches of dots. Our strategy was to determine how reduced dot coherence changes the population response in MT. We then predicted the properties of a decoder that transforms the MT population response into dot coherence-induced deficits in the initiation of pursuit and steady-state tracking. During pursuit initiation, decreased dot coherence reduces MT population response amplitude without changing the preferred speed at its peak. The successful decoder reproduces the measured eye movements by multiplication of 1) the estimate of target speed from the peak of the population response with 2) visual-motor gain based on the amplitude of the population response. During steady-state tracking, the decoder that worked for pursuit initiation failed to reproduce the paradox that steady-state eye speeds do not accelerate to the target speed despite persistent image motion. It predicted eye acceleration to target speed even when monkeys' eye speeds were steady at well below the target speed. To account for the effect of dot coherence on steady-state eye speed, we postulate that the decoder uses sensory-motor gain to modulate the eye velocity positive feedback that normally sustains perfect steady-state tracking. Then, poor steady-state tracking persists because of balance between eye deceleration caused by low positive feedback gain and acceleration driven by MT.NEW & NOTEWORTHY By challenging a sensory-motor system with degraded sensory stimuli, we reveal how the sensory-motor decoder transforms the population response in extrastriate area MT into commands for the initiation and steady-state behavior of smooth pursuit eye movements. Conclusions are based on measuring population responses in MT for multiple target speeds and different levels of motion reliability and evaluating a decoder with a biologically motivated architecture to determine the decoder properties that create the measured eye movements.

Amplified cortical neural responses as animals learn to use novel activity patterns

Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting that the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found that their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task; therefore, these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.

A sensory-motor decoder that transforms neural responses in extrastriate area MT into smooth pursuit eye movements

Visual motion drives smooth pursuit eye movements through a sensory-motor decoder that uses multiple parallel components and neural pathways to transform the population response in extrastriate area MT into movement. We evaluated the decoder by challenging pursuit in monkeys with reduced motion reliability created by reducing coherence of motion in patches of dots. Reduced dot coherence caused deficits in both the initiation of pursuit and steady-state tracking, revealing the paradox of steady-state eye speeds that fail to accelerate to target speed in spite of persistent image motion. We recorded neural responses to reduced dot coherence in MT and found a decoder that transforms MT population responses into eye movements. During pursuit initiation, decreased dot coherence reduces MT population response amplitude without changing the preferred speed at the peak of the population response. The successful decoder reproduces the measured eye movements by multiplication of (i) the estimate of target speed from the peak of the population response with (ii) visual-motor gain based on the amplitude of the population response. During steady-state tracking, the decoder that worked for pursuit initiation failed. It predicted eye acceleration to target speed even when monkeys' eye speeds were steady at a level well below target speed. We can account for the effect of dot coherence on steady-state eye speed if sensorymotor gain also modulates the eye velocity positive feedback that normally sustains perfect steadystate tracking. Then, poor steady-state tracking persists because of balance between deceleration caused by low positive feedback gain and acceleration driven by MT.

Toward a Biomimetic Neural Circuit Model of Sensory-Motor Processing

Computational models have been a mainstay of research on smooth pursuit eye movements in monkeys. Pursuit is a sensory-motor system that is driven by the visual motion of small targets. It creates a smooth eye movement that accelerates up to target speed and tracks the moving target essentially perfectly. In this review of my laboratory's research, I trace the development of computational models of pursuit eye movements from the early control-theory models to the most recent neural circuit models. I outline a combined experimental and computational plan to move the models to the next level. Finally, I explain why research on nonhuman primates is so critical to the development of the neural circuit models I think we need.