Daniel A. Butts

Regulation of CNS synapses by neuronal MHC class I

Until recently, neurons in the healthy brain were considered immune-privileged because they did not appear to express MHC class I (MHCI). However, MHCI mRNA was found to be regulated by neural activity in the developing visual system and has been detected in other regions of the uninjured brain. Here we show that MHCI regulates aspects of synaptic function in response to activity. MHCI protein is colocalized postsynaptically with PSD-95 in dendrites of hippocampal neurons. In vitro, whole-cell recordings of hippocampal neurons from β2m/TAP1 knockout (KO) mice, which have reduced MHCI surface levels, indicate a 40% increase in mini-EPSC (mEPSC) frequency. mEPSC frequency is also increased 100% in layer 4 cortical neurons. Similarly, in KO hippocampal cultures, there is a modest increase in the size of presynaptic boutons relative to WT, whereas postsynaptic parameters (PSD-95 puncta size and mEPSC amplitude) are normal. In EM of intact hippocampus, KO synapses show a corresponding increase in vesicles number. Finally, KO neurons in vitro fail to respond normally to TTX treatment by scaling up synaptic parameters. Together, these results suggest that postsynaptically localized MHCl acts in homeostatic regulation of synaptic function and morphology during development and in response to activity blockade. The results also imply that MHCI acts retrogradely across the synapse to translate activity into lasting change in structure.

Tuning Curves, Neuronal Variability, and Sensory Coding

Tuning curves are widely used to characterize the responses of sensory neurons to external stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is assumed that a neurons role is to encode the stimulus at the tuning curve peak, because high firing rates are the neurons most distinct responses. In contrast, many theoretical and empirical studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve. Here, we demonstrate that both intuitions are correct, but that their relative importance depends on the experimental context and the level of variability in the neuronal response. Using three different information-based measures of encoding applied to experimentally measured sensory neurons, we show how the best-encoded stimulus can transition from high-slope to high-firing-rate regions of the tuning curve with increasing noise level. We further show that our results are consistent with recent experimental findings that correlate neuronal sensitivities with perception and behavior. This study illustrates the importance of the noise level in determining the encoding properties of sensory neurons and provides a unified framework for interpreting how the tuning curve and neuronal variability relate to the overall role of the neuron in sensory encoding.

Dynamic Processes Shape Spatiotemporal Properties of Retinal Waves

In the developing mammalian retina, spontaneous waves of action potentials are present in the ganglion cell layer weeks before vision. These waves are known to be generated by a synaptically connected network of amacrine cells and retinal ganglion cells, and exhibit complex spatiotemporal patterns, characterized by shifting domains of coactivation. Here, we present a novel dynamical model consisting of two coupled populations of cells that quantitatively reproduces the experimentally observed domain sizes, interwave intervals, and wavefront velocity profiles. Model and experiment together show that the highly correlated activity generated by retinal waves can be explained by a combination of random spontaneous activation of cells and the past history of local retinal activity.

An instructive role for patterned spontaneous retinal activity in mouse visual map development.

Complex neural circuits in the mammalian brain develop through a combination of genetic instruction and activity-dependent refinement. The relative role of these factors and the form of neuronal activity responsible for circuit development is a matter of significant debate. In the mammalian visual system, retinal ganglion cell projections to the brain are mapped with respect to retinotopic location and eye of origin. We manipulated the pattern of spontaneous retinal waves present during development without changing overall activity levels through the transgenic expression of β2-nicotinic acetylcholine receptors in retinal ganglion cells of mice. We used this manipulation to demonstrate that spontaneous retinal activity is not just permissive, but instructive in the emergence of eye-specific segregation and retinotopic refinement in the mouse visual system. This suggests that specific patterns of spontaneous activity throughout the developing brain are essential in the emergence of specific and distinct patterns of neuronal connectivity.

Trapped Fermi gases

We study the properties of a spin-polarized Fermi gas in a harmonic trap, using the semiclassical (Thomas-Fermi) approximation. Universal forms for the spatial and momentum distributions are calculated, and the results compared with the corresponding properties of a dilute Bose gas.

Adaptation to stimulus contrast and correlations during natural visual stimulation

In this study, we characterize the adaptation of neurons in the cat lateral geniculate nucleus to changes in stimulus contrast and correlations. By comparing responses to high- and low-contrast natural scene movie and white noise stimuli, we show that an increase in contrast or correlations results in receptive fields with faster temporal dynamics and stronger antagonistic surrounds, as well as decreases in gain and selectivity. We also observe contrast- and correlation-induced changes in the reliability and sparseness of neural responses. We find that reliability is determined primarily by processing in the receptive field (the effective contrast of the stimulus), while sparseness is determined by the interactions between several functional properties. These results reveal a number of adaptive phenomena and suggest that adaptation to stimulus contrast and correlations may play an important role in visual coding in a dynamic natural environment.

How much information is associated with a particular stimulus

Although the Shannon mutual information can be used to reveal general features of the neural code, it cannot directly address which symbols of the code are significant. Further insight can be gained by using information measures that are specific to particular stimuli or responses. The specific information is a previously proposed measure of the amount of information associated with a particular response; however, as I show, it does not properly characterize the amount of information associated with particular stimuli. Instead, I propose a new measure: the stimulus-specific information (SSI), defined to be the average specific information of responses given the presence of a particular stimulus. Like other information theoretic measures, the SSI does not rely on assumptions about the neural code, and is robust to non-linearities of the system. To demonstrate its applicability, the SSI is applied to data from simulated visual neurons, and identifies stimuli consistent with the neurons linear kernel. While the SSI reveals the essential linearity of the visual neurons, it also successfully identifies the well-encoded stimuli in a modified example where linear analysis techniques fail. Thus, I demonstrate that the SSI is an appropriate measure of the information associated with particular stimuli, and provides a new unbiased method of analysing the significant stimuli of a neural code.

Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs

The computation represented by a sensory neurons response to stimuli is constructed from an array of physiological processes both belonging to that neuron and inherited from its inputs. Although many of these physiological processes are known to be nonlinear, linear approximations are commonly used to describe the stimulus selectivity of sensory neurons (i.e., linear receptive fields). Here we present an approach for modeling sensory processing, termed the Nonlinear Input Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise from rectification of a neurons inputs. Incorporating such ‘upstream nonlinearities’ within the standard linear-nonlinear (LN) cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neurons response, which become directly interpretable as either excitatory or inhibitory. Because its form is analogous to an integrate-and-fire neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a given neuron, and elements of the resulting model can often result in specific physiological predictions. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. Parameter estimation is robust and efficient even with large numbers of model components and in the context of high-dimensional stimuli with complex statistical structure (e.g. natural stimuli). We describe detailed methods for estimating the model parameters, and illustrate the advantages of the NIM using a range of example sensory neurons in the visual and auditory systems. We thus present a modeling framework that can capture a broad range of nonlinear response functions while providing physiologically interpretable descriptions of neural computation.

Role of Efficient Neurotransmitter Release in Barrel Map Development

Cortical maps are remarkably precise, with organized arrays of thalamocortical afferents (TCAs) that project into distinct neuronal modules. Here, we present evidence for the involvement of efficient neurotransmitter release in mouse cortical barrel map development using barrelless mice, a loss-of-function mutant of calcium/calmodulin-activated adenylyl cyclase I (AC1), and mice with a mutation in Rab3-interacting molecule 1α (RIM1α), an active zone protein that regulates neurotransmitter release. We demonstrate that release efficacy is substantially decreased in barrelless TCAs. We identify RIMs as important phosphorylation targets for AC1 in the presynaptic terminal. We further show that RIM1α mutant mice have reduced TCA neurotransmitter release efficacy and barrel map deficits, although not as severe as those found in barrelless mice. This supports the role of RIM proteins in mediating, in part, AC1 signaling in barrel map development. Finally, we present a model to show how inadequacies in presynaptic function can interfere with activity-dependent processes in neuronal circuit formation. These results demonstrate how efficient synaptic transmission mediated by AC1 function contributes to the development of cortical barrel maps.

Hierarchical processing of complex motion along the primate dorsal visual pathway

Neurons in the medial superior temporal (MST) area of the primate visual cortex respond selectively to complex motion patterns defined by expansion, rotation, and deformation. Consequently they are often hypothesized to be involved in important behavioral functions, such as encoding the velocities of moving objects and surfaces relative to the observer. However, the computations underlying such selectivity are unknown. In this work we have developed a unique, naturalistic motion stimulus and used it to probe the complex selectivity of MST neurons. The resulting data were then used to estimate the properties of the feed-forward inputs to each neuron. This analysis yielded models that successfully accounted for much of the observed stimulus selectivity, provided that the inputs were combined via a nonlinear integration mechanism that approximates a multiplicative interaction among MST inputs. In simulations we found that this type of integration has the functional role of improving estimates of the 3D velocity of moving objects. As this computation is of general utility for detecting complex stimulus features, we suggest that it may represent a fundamental aspect of hierarchical sensory processing.