Principal Investigator

Synaptic transmission involves the calcium dependent release of neurotransmitter from synaptic vesicles. Genetically encoded optical probes emitting different wavelengths of fluorescent light in response to neuronal activity offer a powerful approach to understand the spatial and temporal relationship of calcium dynamics to the release of neurotransmitter in defined neuronal populations. To simultaneously image synaptic vesicle recycling and changes in cytosolic calcium, we developed a red-shifted reporter of vesicle recycling based on a vesicular glutamate transporter, VGLUT1-mOrange2 (VGLUT1-mOr2), and a presynaptically localized green calcium indicator, synaptophysin-GCaMP3 (SyGCaMP3) with a large dynamic range. The fluorescence of VGLUT1-mOr2 is quenched by the low pH of synaptic vesicles. Exocytosis upon electrical stimulation exposes the luminal mOr2 to the neutral extracellular pH and relieves fluorescence quenching. Reacidification of the vesicle upon endocytosis again reduces fluorescence intensity. Changes in fluorescence intensity thus monitor synaptic vesicle exo- and endocytosis, as demonstrated previously for the green VGLUT1-pHluorin. To monitor changes in calcium, we fused the synaptic vesicle protein synaptophysin to the recently improved calcium indicator GCaMP3. SyGCaMP3 is targeted to presynaptic varicosities, and exhibits changes in fluorescence in response to electrical stimulation consistent with changes in calcium concentration. Using real time imaging of both reporters expressed in the same synapses, we determine the time course of changes in VGLUT1 recycling in relation to changes in presynaptic calcium concentration. Inhibition of P/Q- and N-type calcium channels reduces calcium levels, as well as the rate of synaptic vesicle exocytosis and the fraction of vesicles released.

What fascinates us about animal behavior is its richness and complexity, but understanding behavior and its neural basis requires a simpler description. Traditionally, simplification has been imposed by training animals to engage in a limited set of behaviors, by hand scoring behaviors into discrete classes, or by limiting the sensory experience of the organism. An alternative is to ask whether we can search through the dynamics of natural behaviors to find explicit evidence that these behaviors are simpler than they might have been. We review two mathematical approaches to simplification, dimensionality reduction and the maximum entropy method, and we draw on examples from different levels of biological organization, from the crawling behavior of Caenorhabditis elegans to the control of smooth pursuit eye movements in primates, and from the coding of natural scenes by networks of neurons in the retina to the rules of English spelling. In each case, we argue that the explicit search for simplicity uncovers new and unexpected features of the biological system and that the evidence for simplification gives us a language with which to phrase new questions for the next generation of experiments. The fact that similar mathematical structures succeed in taming the complexity of very different biological systems hints that there is something more general to be discovered.

To probe how the brain integrates visual motion signals to guide behavior, we analyzed the smooth pursuit eye movements evoked by target motion with a stochastic component. When each dot of a texture executed an independent random walk such that speed or direction varied across the spatial extent of the target, pursuit variance increased as a function of the variance of visual pattern motion. Noise in either target direction or speed increased the variance of both eye speed and direction, implying a common neural noise source for estimating target speed and direction. Spatial averaging was inefficient for targets with >20 dots. Together these data suggest that pursuit performance is limited by the properties of spatial averaging across a noisy population of sensory neurons rather than across the physical stimulus. When targets executed a spatially uniform random walk in time around a central direction of motion, an optimized linear filter that describes the transformation of target motion into eye motion accounted for approximately 50% of the variance in pursuit. Filters had widths of approximately 25 ms, much longer than the impulse response of the eye, and filter shape depended on both the range and correlation time of motion signals, suggesting that filters were products of sensory processing. By quantifying the effects of different levels of stimulus noise on pursuit, we have provided rigorous constraints for understanding sensory population decoding. We have shown how temporal and spatial integration of sensory signals converts noisy population responses into precise motor responses.

We have used a combination of theory and experiment to assess how information is represented in a realistic cortical population response, examining how motion direction and timing is encoded in groups of neurons in cortical area MT. Combining data from several single-unit experiments, we constructed model population responses in small time windows and represented the response in each window as a binary vector of 1s or 0s signifying spikes or no spikes from each cell. We found that patterns of spikes and silence across a population of nominally redundant neurons can carry up to twice as much information about visual motion than does population spike count, even when the neurons respond independently to their sensory inputs. This extra information arises by virtue of the broad diversity of firing rate dynamics found in even very similarly tuned groups of MT neurons. Additionally, specific patterns of spiking and silence can carry more information than the sum of their parts (synergy), opening up the possibility for combinatorial coding in cortex. These results also held for populations in which we imposed levels of nonindependence (correlation) comparable to those found in cortical recordings. Our findings suggest that combinatorial codes are advantageous for representing stimulus information on short time scales, even when neurons have no complicated, stimulus-dependent correlation structure.

Suppose that the variability in our movements is caused not by noise in the motor system itself, nor by fluctuations in our intentions or plans, but rather by errors in our sensory estimates of the external parameters that define the appropriate action. For tasks in which precision is at a premium, performance would be optimal if no noise were added in movement planning and execution: motor output would be as accurate as possible given the quality of sensory inputs. Here we use visually guided smooth-pursuit eye movements in primates as a testing ground for this notion of optimality. In response to repeated presentations of identical target motions, nearly 92% of the variance in eye trajectory can be accounted for as a consequence of errors in sensory estimates of the speed, direction and timing of target motion, plus a small background noise that is observed both during eye movements and during fixations. The magnitudes of the inferred sensory errors agree with the observed thresholds for sensory discrimination by perceptual systems, suggesting that the very different neural processes of perception and action are limited by the same sources of noise.