Biology subjects

Niebur, E.

Publications and source records attributed to Niebur, E..

5 recordsLinked to original sources

bioRxiv2023

The Role of Attention in Multi Attribute Decision Making

Real-life decisions typically involve multiple options, each with multiple attributes affecting value. In such complex cases, sequential shifts of attention to specific options and attributes are thought to guide the decision process. We designed a task that allowed us to monitor attention in monkeys engaged in such multi-attribute decisions. We recorded pre-supplementary motor area neurons encoding action value signals reflecting the decision process. Attention guides this process through two mechanisms. First, attention enhances the activity of neurons representing the currently sampled option, independent of the attended option value. Second, attention up-regulates the gain of information integration towards the evolving value estimate for the attended option. In contrast, we found no evidence for a third suggested mechanism, in which only the attended option is represented. Instead, attention influences the ongoing information accumulation and competition between the options by modulating the strength of the value information that drives this circuit.

neuroscience↗

bioRxiv2022

The influence of stereopsis on visual saliency in a proto-object based model of selective attention

Some animals including humans use stereoscopic vision which reconstructs spatial information about the environment from the disparity between images captured by eyes in two separate adjacent locations. Like other sensory information, such stereoscopic information is expected to influence attentional selection. We develop a biologically plausible model of binocular vision to study its effect on bottom-up visual attention, i.e., visual saliency. In our model, the scene is organized in terms of proto-objects on which attention acts, rather than on unbound sets of elementary features. We show that taking into account the stereoscopic information improves the performance of the model in the prediction of human eye movements with statistically significant differences.

neuroscience↗

bioRxiv2020

Overt visual attention and value computation in complex risky choice

Traditional models of decision making under uncertainty explain human behavior in simple situations with a minimal set of alternatives and attributes. Some of them, such as prospect theory, have been proven successful and robust in such simple situations. Yet, less is known about the preference formation during decision making in more complex cases. Furthermore, it is generally accepted that attention plays a role in the decision process but most theories make simplifying assumptions about where attention is deployed. In this study, we replace these assumptions by measuring where humans deploy overt attention, i.e. where they fixate. To assess the influence of task complexity, participants perform two tasks. The simpler of the two requires participants to choose between two alternatives with two attributes each (four items to consider). The more complex one requires a choice between four alternatives with four attributes each (16 items to consider). We then compare a large set of model classes, of different levels of complexity, by considering the dynamic interactions between uncertainty, attention and pairwise comparisons between attribute values. The task of all models is to predict what choices humans make, using the sequence of observed eye movements for each participant as input to the model. We find that two models outperform all others. The first is the two-layer leaky accumulator which predicts human choices on the simpler task better than any other model. We call the second model, which is introduced in this study, TNPRO. It is modified from a previous model from management science and designed to deal with highly complex decision problems. Our results show that this model performs well in the simpler of our two tasks (second best, after the accumulator model) and best for the complex task. Our results suggest that, when faced with complex choice problems, people prefer to accumulate preference based on attention-guided pairwise comparisons.

neuroscience↗

bioRxiv2020

Dendritic signal integration in Drosophila Mushroom Body Output Neuron (MBON) essential for learning and memory

The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroom body (MB), the flys center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.

neuroscience↗

bioRxiv2019

Integration of Odor-Induced Activity of Kenyon Cells in an Electrotonically Compact Drosophila Mushroom Body Output Neuron (MBON)

The formation of an ecologically useful lasting memory requires that the brain has an accurate internal representation of the surrounding environment. In addition, it must have the ability to integrate a variety of different sensory stimuli and associate them with rewarding and aversive behavioral outcomes. Over the previous years, a number of studies have dissected the anatomy and elucidated some of the working principles of the Drosophila mushroom body (MB), the flys center for learning and memory. As a consequence, we now have a functional understanding of where and how in the MB sensory stimuli converge and are associated. However, the molecular and cellular dynamics at the critical synaptic intersection for this process, the Kenyon cell-mushroom body output neuron (KC-MBON) synapse, are largely unknown. Here, we introduce a first approach to understand this integration process and the physiological changes occurring at the KC-MBON synapse during Kenyon cell (KC) activation. We use the published connectome of the Drosophila MB to construct a functional computational model of the MBON-3 dendritic structure. We simulate synaptic input by individual KC-MBON synapses by current injections into precisely (m) identified local dendritic sections, and the input from a model population of KCs representing an odor by a spatially distributed cluster of current injections. By recording the effect of the simulated current injections on the membrane potential of the neuron, we show that the MBON-3 is electrotonically compact. This suggests that odor-induced MBON activity is likely governed by input strength while the positions of KC input synapses are largely irrelevant.

neuroscience↗