Free choice activates a decision circuit between frontal and parietal cortex

We often face alternatives that we are free to choose between. Planning movements to select an alternative involves several areas in frontal and parietal cortex that are anatomically connected into long-range circuits. These areas must coordinate their activity to select a common movement goal, but how neural circuits make decisions remains poorly understood. Here we simultaneously record from the dorsal premotor area (PMd) in frontal cortex and the parietal reach region (PRR) in parietal cortex to investigate neural circuit mechanisms for decision making. We find that correlations in spike and local field potential (LFP) activity between these areas are greater when monkeys are freely making choices than when they are following instructions. We propose that a decision circuit featuring a sub-population of cells in frontal and parietal cortex may exchange information to coordinate activity between these areas. Cells participating in this decision circuit may influence movement choices by providing a common bias to the selection of movement goals

An integrated 1D–2D hydraulic modelling approach to assess the sensitivity of a coastal region to compound flooding hazard under climate change

Coastal regions are dynamic areas that often lie at the junction of different natural hazards. Extreme events such as storm surges and high precipitation are significant sources of concern for flood management. As climatic changes and sea-level rise put further pressure on these vulnerable systems, there is a need for a better understanding of the implications of compounding hazards. Recent computational advances in hydraulic modelling offer new opportunities to support decision-making and adaptation. Our research makes use of recently released features in the HEC-RAS version 5.0 software to develop an integrated 1D–2D hydrodynamic model. Using extreme value analysis with the Peaks-Over-Threshold method to define extreme scenarios, the model was applied to the eastern coast of the UK. The sensitivity of the protected wetland known as the Broads to a combination of fluvial, tidal and coastal sources of flooding was assessed, accounting for different rates of twenty-first century sea-level rise up to the year 2100. The 1D–2D approach led to a more detailed representation of inundation in coastal urban areas, while allowing for interactions with more fluvially dominated inland areas to be captured. While flooding was primarily driven by increased sea levels, combined events exacerbated flooded area by 5–40% and average depth by 10–32%, affecting different locations depending on the scenario. The results emphasise the importance of catchment-scale strategies that account for potentially interacting sources of flooding

Mechanisms of Maximum Information Preservation in the Drosophila Antennal Lobe

We examined the presence of maximum information preservation, which may be a fundamental principle of information transmission in all sensory modalities, in the Drosophila antennal lobe using an experimentally grounded network model and physiological data. Recent studies have shown a nonlinear firing rate transformation between olfactory receptor neurons (ORNs) and second-order projection neurons (PNs). As a result, PNs can use their dynamic range more uniformly than ORNs in response to a diverse set of odors. Although this firing rate transformation is thought to assist the decoder in discriminating between odors, there are no comprehensive, quantitatively supported studies examining this notion. Therefore, we quantitatively investigated the efficiency of this firing rate transformation from the viewpoint of information preservation by computing the mutual information between odor stimuli and PN responses in our network model. In the Drosophila olfactory system, all ORNs and PNs are divided into unique functional processing units called glomeruli. The nonlinear transformation between ORNs and PNs is formed by intraglomerular transformation and interglomerular interaction through local neurons (LNs). By exploring possible nonlinear transformations produced by these two factors in our network model, we found that mutual information is maximized when a weak ORN input is preferentially amplified within a glomerulus and the net LN input to each glomerulus is inhibitory. It is noteworthy that this is the very combination observed experimentally. Furthermore, the shape of the resultant nonlinear transformation is similar to that observed experimentally. These results imply that information related to odor stimuli is almost maximally preserved in the Drosophila olfactory circuit. We also discuss how intraglomerular transformation and interglomerular inhibition combine to maximize mutual information

Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex

Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity [... however,] even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. [...] Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation.Comment: 49 pages, 11 figures, 7 table