Past events

Any physical device, including computers, when comparing A to B, must send the information to point C. Explanations of brain processing take such a convergence for granted thus generating models relying on increasingly converging hierarchical streams. Such models, however, consistently fail to explain many perceptual phenomena. To see whether the brain, at times, can compare (integrate, process) events that take place at different loci without sending the information to a common target, I performed experiments in three modalities, somato-sensory, auditory, and visual, where 2 different loci at the primary cortex were stimulated. Subjects were able to integrate inputs in time and space affecting small separate cortical loci. The ability to correlate activity between loci was independent of cortical distance up to 2-4 cm. Given the organization of sensory cortex where localized responses in primary cortex do not interact while convergence in downstream areas results in loss of individual stimulus identity and in decreasing selectivity to elementary stimuli, those results cannot be explained by conventional convergence models. We must thus assume a non-converging mechanism whereby two (or more) activated cortical loci can be integrated without sending information via axons into another downstream integrating site. Once we allow for such a non-converging mechanism, many perceptual phenomena can be viewed differently. Object recognition and representation is such a phenomenon that, I suggest, does not result from hierarchical convergence of cells with ever-increasing feature selectivity but rather from parallel interactions between various visual and non-visual areas. If my hypothesis of the brain ability to relate activity taking place at separate loci without using convergence-by-wires is correct, it implies that the brain can use heretofore unconsidered (unknown?) parallel processing and that conventional models, including computer programs, would not be able to capture many brain processes.

The neuronal mechanisms underlying perceptual grouping of discrete, similarly oriented elements are not well understood. To investigate this, we imaged population responses in V1 of monkeys trained on a contour detection task. Mapping neuronal populations processing contour/background elements in V1 enabled studying the role of two encoding mechanisms: strength of population response and synchronization. Response maps early in time showed activation patches corresponding to the contour/background individual elements. An early increased synchronization between the contour elements, accompanied by decreased synchronization between the background elements, suggested that contour integration is initiated with synchronization changes. However only response modulation at later times, defined by increased activity in the contour elements, along with suppressed activity in the background elements, enabled to visualize in single trials, a salient continuous contour segregated from a noisy background. Finally, the late modulation was correlated with psychophysical performance of contour saliency, further supporting its role in contour perception. In the second part of this talk we will demonstrate the effects of microsaccades on perceptual mechanisms in V1.

A major objective of neuroscience is to understand the neural code, namely how the patterns of neuronal signals (e.g. action potentials, membrane potential, calcium concentrations) “represent” physical objects, commands for actions, or psychological phenomena. An successful neural coding scheme has to be robust to noise (i.e. random neuronal activity). We have recently shown that using a small perturbation, an introduction of one “extra”-spike to the activity of a single neuron in the cortex, and studying the consequence of that perturbation we can obtain bounds on the level of noise in the cortex. Theoretical analysis of the data indicates that intrinsic, stimulus-independent variations in membrane potential of cortical neurons are on the order of 2.2–4.5 mV—variations that are pure noise, and so carry no information at all. Such level of noise places severe limitations on the plausibility of neural code based on precise spike timing. Using recent advances in optogentics we can extend the approach of introducing a precisely controlled perturbation. We explore how these perturbations affect the dynamics of activity in the cortex as well as theirs effect on animal performance on a task, to gain further bounds and insights on the neural code.

The ability of human subjects to choose between disparate kinds of rewards suggests that the neural circuits for valuing different reward types must converge. Economic theory suggests that these convergence points represent the subjective values (SVs) of different reward types on a common scale for comparison. I will describe two studies related to this theory. First, to directly examine this theory and to map the neural circuits for reward valuation, we had food and water deprived subjects make risky choices for money, food and water both in and out of a brain scanner. In the second study we sought to determine whether the risk preferences of these same rewards change as a function of internal state. We found that risk preferences across reward types were highly correlated. We also found that partially distinct neural networks represent the SV of monetary and food rewards and that these distinct networks showed specific convergence points. In addition, we show that subjects tend to converge to a similar, weakly risk-averse attitude when deprived. These results may suggest that partially distinct valuation networks for different reward types converge on a unified valuation network, which enables a direct comparison between different reward types and hence guides valuation and choice. When healthy humans are sated they show heterogeneity of risk preferences, but when deprived a convergence point appears to emerge. It is as if evolution pressure, when resources are scarce, drives humans to a similar level of risk aversion but allows heterogeneity when resources are plentiful.

A major goal in systems neuroscience is to characterize normal cortical dynamics. Numerous in vitro and in vivo studies demonstrated that ongoing cortical dynamics are characterized by cascades of activity across many spatial scales, termed neuronal avalanches. Avalanche dynamics are identified by¬ two measures (1) a power law in the size distribution of activity cascades, with an exponent of -3/2 and (2) a branching parameter of 1, which reflects a balance in the propagation of cortical activity at the border of premature termination and potential exponential blow up. Here we analyzed resting state brain activity recorded using MEG from more than 100 healthy human subjects. We identified discrete events in the MEG signal and segmented them into cascades, using multiple timescales. Cascade-size distributions were found to obey power laws. At the timescale where the branching parameter was close to the critical value of 1, the power law exponent was -3/2, in line with expectations for neuronal avalanches. This behavior was robust to scaling of the number of sensors and to coarse-graining the sensor resolution. As controls, phase-shuffled data with the same power spectrum or empty-scanner data did not exhibit neuronal avalanches. These results indicate that normal resting cortical dynamics are well described by a critical branching process. Both theory and experiments suggest that cortical networks with such critical, scale-free dynamics optimize various types of information processing. Neuronal avalanches could thus provide a biomarker for disorders in information processing, paving the way for novel quantification of normal and pathological cortical states.

This second talk will focus on olfactory circuits in the context of learning. I will present first a couple of non-associative phenomena, both linking plasticity and synchrony. I will then describe more recent work connecting STDP and reward signals, indicating that STDP rules are labile and influenced by the context in which they are being used. If time allows, I will present the outlines of the new work that we started at MPI Brain Research, on computation in an ancient cortex.

Histamine-dependent and histamine-independent itch are detected and transduced in primary sensory neurons through distinct molecular signaling mechanisms. It remains unclear, however, whether pruritogens activate these mechanisms within the same or different afferents and if these afferents are dispensable for pain. To address this, we have selectively blocked histamine-dependent and -independent primary afferent fibers in vivo using targeted delivery of the membrane-impermeant sodium-channel blocker, QX-314. Silencing histamine-sensitive pruriceptors abolished subsequent histamine-evoked scratching but not that produced by the histamine-independent pruritogens chloroquine and SLIGRL-NH2, and vice versa. We conclude that distinct fibers mediate the two itches. Moreover, we also demonstrate that targeted blockade of itch does not reduce pain-associated behavior, implying that pruriceptors are a labeled line only for itch.