13 papers · ranked by Valyu relevance
Xu Pan, Luis Gonzalo Sánchez Giraldo, Elif Kartal, Odelia Schwartz
We studied a local normalization paradigm, namely weighted normalization, that better reflects the current understanding of the brain. Specifically, the normalization weight is trainable, and has a more realistic surround pool selection. Weighted normalization outperformed other normalizations in image classification…
Zaina A. Zayyad, John H. R. Maunsell, Jason N. MacLean
When multiple stimuli appear together in the receptive field of a visual cortical neuron, the response is typically close to the average of that neuron’s response to each individual stimulus. The departure from a linear sum of each individual response is referred to as normalization. In mammals, normalization has been…
David J. Heeger, Klavdia O. Zemlianova
The normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model’s defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. In spite of the success of the…
Flaviano Morone, Shivang Rawat, David J. Heeger, Stefano Martiniani
Stability is a fundamental requirement for both biological and engineered neural circuits, yet it is surprisingly difficult to guarantee in the presence of recurrent interactions. Standard linear dynamical models of recurrent networks are sensitive to the precise values of the synaptic weights, since stability requires…
Flaviano Morone, Shivang Rawat, David J. Heeger, Stefano Martiniani
Stability is a fundamental requirement for both biological and engineered neural circuits, yet it is surprisingly difficult to guarantee in the presence of recurrent interactions. Standard linear dynamical models of recurrent networks are un-reasonably sensitive to the precise values of the synaptic weights, since…
Stephanie C. Hicks, Kwame Okrah, Joseph N. Paulson, John Quackenbush + 2 more
Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the…
A. Sina Booeshaghi, Ingileif B. Hallgrímsdóttir, Ángel Gálvez-Merchán, Lior Pachter
Single-cell genomics analysis requires normalization of feature counts that stabilizes variance, accounts for variable cell sequencing depth, and preserves monotonicity of within-cell feature abundances. We show that normalization via an (additive in the raw counts) proportional fitting step followed by the logarithm…
Angus F. Chapman, Rachel N. Denison
Perception and neural activity are profoundly shaped by the spatial and temporal context of sensory input, which has been modeled by divisive normalization over space or time. However, theoretical work has largely treated normalization separately within these dimensions and has not explained how future stimuli can…
Ruben Coen-Cagli, Selina S. Solomon
Cortical responses to repeated presentations of a stimulus are variable. This variability is sensitive to experimental manipulations that are also known to engage divisive normalization: a widespread description of neural activity as the ratio of a numerator (the excitatory stimulus drive) and denominator (the…
Zeming Fang, Ilona Bloem, Catherine Olsson, Wei Ji Ma + 1 more
An influential account of neuronal responses in primary visual cortex is the normalized energy model. This model is often implemented as a two-stage computation. The first stage is the extraction of contrast energy, whereby a complex cell computes the squared and summed outputs of a pair of linear filters in quadrature…
Olivier Potvin, Louis Dieumegarde, Simon Duchesne
We present NOMIS (https://github.com/medicslab/NOMIS), a comprehensive open MRI tool to assess morphometric deviation from normality in the adult human brain. Based on MR anatomical images from 6,909 cognitively healthy individuals aged 18-100 years, we modeled 1,344 measures computed using the open access FreeSurfer…
Saige Rutherford, Pieter Barkema, Ivy F. Tso, Chandra Sripada + 3 more
In this work, we expand the normative model repository introduced in Rutherford et al. (2022a) to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online…
Philip D. Gingerich
The zero-force evolutionary law (ZFEL) of McShea et al. states that independently evolving entities, with no forces or constraints acting on them, will tend to accumulate differences and therefore diverge from each other. McShea et al. quantified the law by assuming normality on an additive arithmetic scale and…