Maps of complex motion selectivity in the superior temporal cortex of the alert macaque monkey: a double-label 2-deoxyglucose study

The superior temporal sulcus (STS) of the macaque monkey contains multiple visual areas. Many neurons within these regions respond selectively to motion direction and to more complex motion patterns, such as expansion, contraction and rotation. Single-unit recording and optical recording studies in MT/MST suggest that cells with similar tuning properties are clustered into columns extending through multiple cortical layers. In this study, we used a double-label 2-deoxyglucose technique in awake, behaving macaque monkeys to clarify this functional organization. This technique allowed us to label, in a single animal, two populations of neurons responding to two different visual stimuli. In one monkey we compared expansion with contraction; in a second monkey we compared expansion with clockwise rotation. Within the STS we found a patchy arrangement of cortical columns with alternating stimulus selectivity: columns of neurons preferring expansion versus contraction were more widely separated than those selective for expansion versus rotation. This mosaic of interdigitating columns on the floor and posterior bank of the STS included area MT and some neighboring regions of cortex, perhaps including area MST

Organizational Form and Insurance Company Performance: Stocks versus Mutuals

One unusual feature of the U.S. property-casualty insurance industry is the coexistence of stock and mutual companies. This paper explores the performance of these forms in the industry through a dynamic assessment of how mutual and stock insurance companies respond to differences in their underwriting environment. Agency theories suggest that the stock company may be more 'opportunistic' and less obligated to their insureds than mutuals. This article assesses the responses by stock and mutual firms to changes in the underwriting environment from 1984 to 1991, using measures of individual firms' performance, by state and by line, in eight different lines of insurance. Stock companies are more likely than mutuals to reduce their business in unprofitable situations, and have higher losses than mutuals for a given amount of premiums.

Corticocortical feedback increases the spatial extent of normalization

Normalization has been proposed as a canonical computation operating across different brain regions, sensory modalities, and species. It provides a good phenomenological description of non-linear response properties in primary visual cortex (V1), including the contrast response function and surround suppression. Despite its widespread application throughout the visual system, the underlying neural mechanisms remain largely unknown. We recently observed that corticocortical feedback contributes to surround suppression in V1, raising the possibility that feedback acts through normalization. To test this idea, we characterized area summation and contrast response properties in V1 with and without feedback from V2 and V3 in alert macaques and applied a standard normalization model to the data. Area summation properties were well explained by a form of divisive normalization, which computes the ratio between a neuron's driving input and the spatially integrated activity of a “normalization pool.” Feedback inactivation reduced surround suppression by shrinking the spatial extent of the normalization pool. This effect was independent of the gain modulation thought to mediate the influence of contrast on area summation, which remained intact during feedback inactivation. Contrast sensitivity within the receptive field center was also unaffected by feedback inactivation, providing further evidence that feedback participates in normalization independent of the circuit mechanisms involved in modulating contrast gain and saturation. These results suggest that corticocortical feedback contributes to surround suppression by increasing the visuotopic extent of normalization and, via this mechanism, feedback can play a critical role in contextual information processing

A Modality-Specific Feedforward Component of Choice-Related Activity in MT

The activity of individual sensory neurons can be predictive of an animal\u27s choices. These decision signals arise from network properties dependent on feedforward and feedback inputs; however, the relative contributions of these inputs are poorly understood. We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks. During each session, we reversibly inactivated V2 and V3, which provide feedforward input to MT that conveys more information about depth than motion. We thus monitored the choice-related activity of the same neuron both before and during V2/V3 inactivation. During inactivation, MT neurons became less predictive of decisions for the depth task but not the motion task, indicating that a feedforward pathway that gives rise to tuning preferences also contributes to decision signals. We show that our data are consistent with V2/V3 input conferring structured noise correlations onto the MT population

Roundtable on Community Engagement and Collective Impact.

To advance the conversation about how to engage the community in collective impact, the Aspen Institute Forum for Community Solutions gathered scholars and practitioners for an honest discussion. In this roundtable, the participants discuss why it is important to involve the community actively, how it can be done within a collective impact initiative, and the challenges and pitfalls of engaging the community

State-dependencies of learning across brain scales

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly