More Like this

1. Modeling and characterization of pure and odorant mixture processing in the Drosophila mushroom body calyx

   https://doi.org/10.3389/fphys.2024.1410946  

   Lazar, Aurel A; Liu, Tingkai; Yeh, Chung-Heng; Zhou, Yiyin (October 2024, Frontiers in Physiology)

   Fiala, André; Meltzer, Hagar; Schleyer, Michael; Turrel, Oriane; Widmann, Annekathrin (Ed.)

   Associative memory in the Mushroom Body of the fruit fly brain depends on the encoding and processing of odorants in the first three stages of the Early Olfactory System: the Antenna, the Antennal Lobe and the Mushroom Body Calyx. The Kenyon Cells (KCs) of the Calyx provide the Mushroom Body compartments the identity of pure and odorant mixtures encoded as a train of spikes. Characterizing the code underlying the KC spike trains is a major challenge in neuroscience. To address this challenge we start by explicitly modeling the space of odorants using constructs of both semantic and syntactic information. Odorant semantics concerns the identity of odorants while odorant syntactics pertains to their concentration amplitude. These odorant attributes are multiplicatively coupled in the process of olfactory transduction. A key question that early olfactory systems must address is how to disentangle the odorant semantic information from the odorant syntactic information. To address the untanglement we devised an Odorant Encoding Machine (OEM) modeling the first three stages of early olfactory processing in the fruit fly brain. Each processing stage is modeled by Divisive Normalization Processors (DNPs). DNPs are spatio-temporal models of canonical computation of brain circuits. The end-to-end OEM is constructed as cascaded DNPs. By extensively modeling and characterizing the processing of pure and odorant mixtures in the Calyx, we seek to answer the question of its functional significance. We demonstrate that the DNP circuits in the OEM combinedly reduce the variability of the Calyx response to odorant concentration, thereby separating odorant semantic information from syntactic information. We then advance a code, called first spike sequence code, that the KCs make available at the output of the Calyx. We show that the semantics of odorants can be represented by this code in the spike domain and is ready for easy memory access in the Mushroom Body compartments. 

   more » « less
2. Integrating Flexible Normalization into Midlevel Representations of Deep Convolutional Neural Networks

   https://doi.org/10.1162/neco_a_01226  

   Giraldo, Luis Gonzalo; Schwartz, Odelia (November 2019, Neural Computation)

   Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex. However, contextual effects, which are prevalent in neural processing and in perception, are not explicitly handled by current CNNs, including those used for neural prediction. In primary visual cortex, neural responses are modulated by stimuli spatially surrounding the classical receptive field in rich ways. These effects have been modeled with divisive normalization approaches, including flexible models, where spatial normalization is recruited only to the degree that responses from center and surround locations are deemed statistically dependent. We propose a flexible normalization model applied to midlevel representations of deep CNNs as a tractable way to study contextual normalization mechanisms in midlevel cortical areas. This approach captures nontrivial spatial dependencies among midlevel features in CNNs, such as those present in textures and other visual stimuli, that arise from tiling high-order features geometrically. We expect that the proposed approach can make predictions about when spatial normalization might be recruited in midlevel cortical areas. We also expect this approach to be useful as part of the CNN tool kit, therefore going beyond more restrictive fixed forms of normalization. 

   more » « less
3. A visual sense of number emerges from divisive normalization in a simple center-surround convolutional network

   https://doi.org/10.7554/eLife.80990  

   Park, Joonkoo; Huber, David E (October 2022, eLife)

   Many species of animals exhibit an intuitive sense of number, suggesting a fundamental neural mechanism for representing numerosity in a visual scene. Recent empirical studies demonstrate that early feedforward visual responses are sensitive to numerosity of a dot array but substantially less so to continuous dimensions orthogonal to numerosity, such as size and spacing of the dots. However, the mechanisms that extract numerosity are unknown. Here, we identified the core neurocomputational principles underlying these effects: (1) center-surround contrast filters; (2) at different spatial scales; with (3) divisive normalization across network units. In an untrained computational model, these principles eliminated sensitivity to size and spacing, making numerosity the main determinant of the neuronal response magnitude. Moreover, a model implementation of these principles explained both well-known and relatively novel illusions of numerosity perception across space and time. This supports the conclusion that the neural structures and feedforward processes that encode numerosity naturally produce visual illusions of numerosity. Taken together, these results identify a set of neurocomputational properties that gives rise to the ubiquity of the number sense in the animal kingdom. 

   more » « less
4. Interaction of supported phospholipid bilayers with diamond nanoparticles non-covalently functionalized with a cationic polyelectrolyte

   https://doi.org/10.1039/D3EN00349C  

   Kuech, Thomas R.; Ganji, Nasim; Anastasia, Caroline; Torelli, Marco D.; Melby, Eric S.; Mensch, Arielle C.; Caudill, Emily R.; Zimmermann, Ralf; Hamers, Robert J.; Pedersen, Joel A. (September 2023, Environmental Science: Nano)

   We use diamond nanoparticles (DNPs) wrapped in the cationic polyelectrolyte poly(allylamine) hydrochloride (PAH) and bilayers composed of either pure DOPC or a mixture of DOPC/DOPG to investigate the influence of membrane phospholipid composition and net surface charge on nanoparticle-membrane interactions and the extent of nanoparticle adhesion to supported phospholipid bilayers. Our results show that in all cases electrostatic attractions between the negatively charged bilayer and cationic PAH-DNP were responsible for the initial attachment of particles, and the lateral electrostatic repulsion between adsorbed particles on the bilayer surface determined the final extent of PAH-DNP attachment. Upon attachment, NPs attract lipids by the contact ion pairing between the ammonium groups on PAH and phosphate and glycerol groups on the lipids and acquire a lipid corona. Lipid corona formation on the PAH-DNP reduces the effective charge density of the particle and is in fact a key factor determining the final extent of NP attachment to the bilayer. Incorporation of DOPG to the bilayer leads to a decrease in efficiency and final extent of attachment compared to DOPC alone. The reduction in PAH-DNP attachment in the presence of DOPG is attributed to the adsorption of free PAH in equilibrium with bound PAH in the nanoparticle solution, thus reducing electrostatic attraction between PAH-DNPs and SLBs. This leads to an increase in hydrogen bonding interactions between lipid headgroups that limits extraction of phospholipids from the bilayer by PAH-DNP, lessening the reduction in interparticle repulsion achieved by acquisition of a lipid corona. Our results indicate that the inclusion of charged phospholipids in SLBs changes bilayer rigidity and stability and hinders the attachment of PAH-DNPs by preventing lipid extraction from the bilayer. 

   more » « less
5. Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors

   https://doi.org/10.1038/s41467-025-60006-x  

   Xiong, Zheshun; Liang, Wen; Zhang, Meiyue; Mao, Dacheng; Xia, Qiangfei; Xu, Guangyu (May 2025, Nature Communications)

   Abstract In-sensor processing of dynamic and static information of visual objects avoids exchanging redundant data between physically separated sensing and computing units, holding promise for computer vision hardware. To this end, gate-tunable photodetectors, if built in a highly scalable array form, would lend themselves to large-scale in-sensor visual processing because of their potential in volume production and hence, parallel operation. Here we present two scalable in-sensor visual processing arrays based on dual-gate silicon photodiodes, enabling parallelized event sensing and edge detection, respectively. Both arrays are built in CMOS compatible processes and operated with zero static power. Furthermore, their bipolar analog output captures the amplitude of event-driven light changes and the spatial convolution of optical power densities at the device level, a feature that helps boost their performance in classifying dynamic motions and static images. Capable of processing both temporal and spatial visual information, these retinomorphic arrays suggest a path towards large-scale in-sensor visual processing systems for high-throughput computer vision. 

   more » « less