Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex

RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short time scales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex

Adoption as a Social Marker: Innovation Diffusion with Outgroup Aversion

Social identities are among the key factors driving behavior in complex societies. Signals of social identity are known to influence individual behaviors in the adoption of innovations. Yet the population-level consequences of identity signaling on the diffusion of innovations are largely unknown. Here we use both analytical and agent-based modeling to consider the spread of a beneficial innovation in a structured population in which there exist two groups who are averse to being mistaken for each other. We investigate the dynamics of adoption and consider the role of structural factors such as demographic skew and communication scale on population-level outcomes. We find that outgroup aversion can lead to adoption being delayed or suppressed in one group, and that population-wide underadoption is common. Comparing the two models, we find that differential adoption can arise due to structural constraints on information flow even in the absence of intrinsic between-group differences in adoption rates. Further, we find that patterns of polarization in adoption at both local and global scales depend on the details of demographic organization and the scale of communication. This research has particular relevance to widely beneficial but identity-relevant products and behaviors, such as green technologies, where overall levels of adoption determine the positive benefits that accrue to society at large.Comment: 26 pages, 10 figure

Social-Emotional Learning for the Alternative High School

The purpose of this capstone is to provide social-emotional learning (SEL) strategies for educational professionals to use with high-school students in the alternative high school. Included in this capstone is a detailed description of SEL as well as an analysis of the need for SEL offering in alternative high schools. The project provides three succinct strategies that educators and professionals can implement within an alternative education setting to best support the high needs of their students. Adapted from Nancy Hellander Pung’s UpCoaching framework, the three strategies focus on deepening questions that allow students to realize their values, their intentions, and aid them in goal setting and conflict resolution. The strategies are framed around these three questions: 1) What is your goal?; 2) What will that give you?; 3) What can you do to achieve that goal? Within conversations with a student, the educator would continue to ask deepening questions to unveil the student’s values and ultimate intentions. This process can incorporate all five of the SEL competencies as described by the Collaborative for Academic, Social, and Emotional Learning (CASEL, 2017): social-awareness, self-awareness, self-management, relationship skills, and responsible decision-making. There is yet to be sufficient evidence of their effectiveness due to the lack of extensive data. Given that the implementation of these strategies rely highly on both buy-in from education professionals as well as students, results will vary

Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components

Many neural regions are arranged into two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered by the lack of appropriate tools. It has been particularly difficult to bridge across levels of detail, because simulators are typically geared to a specific level, while interfacing between simulators has been a major technical challenge. In this paper, we show that the Python-based Topographica simulator makes it straightforward to build systems that cross levels of analysis, as well as providing a common framework for evaluating and comparing models implemented in other simulators. These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators. In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica. Additional examples show how to interface easily with models in other types of simulators. Researchers simulating topographic maps externally should consider using Topographica's analysis tools (such as preference map, receptive field, or tuning curve measurement) to compare results consistently, and for connecting models at different levels. This seamless interoperability will help neuroscientists and computational scientists to work together to understand how neurons in topographic maps organize and operate

Edge co-occurrences can account for rapid categorization of natural versus animal images

International audienceMaking a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve high-level associative brain areas. Previous explanations require progressively analyzing the scene hierarchically at increasing levels of abstraction, from edge extraction to mid-level object recognition and then object categorization. Here we show that the statistics of edge co-occurrences alone are sufficient to perform a rough yet robust (translation, scale, and rotation invariant) scene categorization. We first extracted the edges from images using a scale-space analysis coupled with a sparse coding algorithm. We then computed the "association field" for different categories (natural, man-made, or containing an animal) by computing the statistics of edge co-occurrences. These differed strongly, with animal images having more curved configurations. We show that this geometry alone is sufficient for categorization, and that the pattern of errors made by humans is consistent with this procedure. Because these statistics could be measured as early as the primary visual cortex, the results challenge widely held assumptions about the flow of computations in the visual system. The results also suggest new algorithms for image classification and signal processing that exploit correlations between low-level structure and the underlying semantic category

How does the chromatin fiber deal with topological constraints?

In the nuclei of eukaryotic cells, DNA is packaged through several levels of compaction in an orderly retrievable way that enables the correct regulation of gene expression. The functional dynamics of this assembly involves the unwinding of the so-called 30 nm chromatin fiber and accordingly imposes strong topological constraints. We present a general method for computing both the twist and the writhe of any winding pattern. An explicit derivation is implemented for the chromatin fiber which provides the linking number of DNA in eukaryotic chromosomes. We show that there exists one and only one unwinding path which satisfies both topological and mechanical constraints that DNA has to deal with during condensation/decondensation processes.Comment: Presented in Nature "News and views in brief" Vol. 429 (13 May 2004). Movies available at http://www.lptl.jussieu.fr/recherche/operationE_fichiers/Page_figurePRL.htm

Probabilistic Spatial and Temporal Resilience Landscapes for the Congo Basin

Recent research by Hirota et al. (2011) introduced the concept of resilience landscapes for tropical forests and savannahs. Basically, the approach statistically relates the probability of current forest/savannah occurrence with the concept of tipping points, at which the ecosystem has no other choice except to switch from on stable state (e.g., forest) to its alternative stable state (e.g., savannah) or vice versa. This work will use a biogeochemical modelling approach to establish such probabilistic resilience landscapes for the Congo Basin rainforest biome. In a first step, the occurrence of tipping points will be related to climate features like annual precipitation, dry season length, occurrence of startiform non-precipitating cloud cover and the inter-annual variation in precipitation. In the second, spatial resilience landscapes for the Congo Basin will be provided using present climate conditions. Their relation to current forest/savannah distribution will be assessed and evident congruencies and discrepancies will be discussed. In a third step, the concept of temporal resilience landscapes will be developed along the patch-level life cycle dynamics of the Congo Basin rainforest biome. In a final step, the implications of results for ecosystem management decision will be assessed and possible implications on policy and land-use decisions will be presented

Computational Modeling of Contrast Sensitivity and Orientation Tuning in Schizophrenia

Computational modeling is being increasingly used to understand schizophrenia, but, to date, it has not been used to account for the common perceptual disturbances in the disorder. We manipulated schizophrenia-relevant parameters in the GCAL (gain control, adaptation, laterally connected) model (Stevens et al., 2013), run using the Topographica simulator (Bednar, 2012), to model low-level visual processing changes in the disorder. Our models incorporated: separate sheets for retinal, LGN, and V1 activity; gain control in the LGN; homeostatic adaptation in V1 based on a weighted sum of all inputs and limited by a logistic (sigmoid) nonlinearity; lateral excitation and inhibition in V1; and self-organization of synaptic weights based on Hebbian learning. Data indicated that: 1) findings of increased contrast sensitivity for low spatial frequency stimuli in first episode schizophrenia (FES) can be successfully modeled as a function of reduced retinal and LGN efferent activity within the context of normal LGN gain control and cortical mechanisms (see Figure 1); and 2) findings of reduced contrast sensitivity and broadened orientation tuning in chronic schizophrenia can be successfully modeled by a combination of reduced V1 lateral inhibition and an increase in the Hebbian learning rate at V1 synapses for LGN input (see Figures 1-3). These models are consistent with many current findings (Silverstein, 2016) and they predict relationships that have not yet been explored. They also have implications for understanding links between perceptual changes and psychotic symptom formation, and for understanding changes during the long-term course of the disorder