Investigation of Pulse electric field effect on HeLa cells alignment properties on extracellular matrix protein patterned surface

YesCell behavior in terms of adhesion, orientation and guidance, on extracellular matrix (ECM) molecules including collagen, fibronectin and laminin can be examined using micro contact printing (MCP). These cell adhesion proteins can direct cellular adhesion, migration, differentiation and network formation in-vitro. This study investigates the effect of microcontact printed ECM protein, namely fibronectin, on alignment and morphology of HeLa cells cultured in-vitro. Fibronectin was stamped on plain glass cover slips to create patterns of 25μm, 50μm and 100μm width. However, HeLa cells seeded on 50μm induced the best alignment on fibronectin pattern (7.66° ±1.55SD). As a consequence of this, 50μm wide fibronectin pattern was used to see how fibronectin induced cell guidance of HeLa cells was influenced by 100μs and single pulse electric fields (PEF) of 1kV/cm. The results indicates that cells aligned more under pulse electric field exposure (2.33° ±1.52SD) on fibronectin pattern substrate. Thus, PEF usage on biological cells would appear to enhance cell surface attachment and cell guidance. Understanding this further may have applications in enhancing tissue graft generation and potentially wound repair.Ministry of Higher Education Malaysia and UTHM Tier 1 Research Grant (U865

Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN

International audienceDecoding and reconstructing images from brain imaging data is a research area of high interest. Recent progress in deep generative neural networks has introduced new opportunities to tackle this problem. Here, we employ a recently proposed large-scale bi-directional generative adversarial network, called BigBiGAN, to decode and reconstruct natural scenes from fMRI patterns. BigBiGAN converts images into a 120-dimensional latent space which encodes class and attribute information together, and can also reconstruct images based on their latent vectors. We computed a linear mapping between fMRI data, acquired over images from 150 different categories of ImageNet, and their corresponding BigBiGAN latent vectors. Then, we applied this mapping to the fMRI activity patterns obtained from 50 new test images from 50 unseen categories in order to retrieve their latent vectors, and reconstruct the corresponding images. Pairwise image decoding from the predicted latent vectors was highly accurate (84%). Moreover, qualitative and quantitative assessments revealed that the resulting image reconstructions were visually plausible, successfully captured many attributes of the original images, and had high perceptual similarity with the original content. This method establishes a new state-of-the-art for fMRI-based natural image reconstruction, and can be flexibly updated to take into account any future improvements in generative models of natural scene images

Multimodal neural networks better explain multivoxel patterns in the hippocampus

International audienceThe human hippocampus possesses "concept cells", neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality. Recently, similar concept cells were discovered in a multimodal network called CLIP [1]. Here, we ask whether CLIP can explain the fMRI activity of the human hippocampus better than a purely visual (or linguistic) model. We extend our analysis to a range of publicly available uni-and multi-modal models. We demonstrate that "multimodality" stands out as a key component when assessing the ability of a network to explain the multivoxel activity in the hippocampus

On the role of feedback in visual processing: a predictive coding perspective

'Andrea Alamia' and 'Milad Mozafari' contributed equally to this workBrain-inspired machine learning is gaining increasing consideration, particularly in computer vision. Several studies investigated the inclusion of top-down feedback connections in convolutional networks; however, it remains unclear how and when these connections are functionally helpful. Here we address this question in the context of object recognition under noisy conditions. We consider deep convolutional networks (CNNs) as models of feed-forward visual processing and implement Predictive Coding (PC) dynamics through feedback connections (predictive feedback) trained for reconstruction or classification of clean images. To directly assess the computational role of predictive feedback in various experimental situations, we optimize and interpret the hyper-parameters controlling the network's recurrent dynamics. That is, we let the optimization process determine whether top-down connections and predictive coding dynamics are functionally beneficial. Across different model depths and architectures (3-layer CNN, ResNet18, and EfficientNetB0) and against various types of noise (CIFAR100-C), we find that the network increasingly relies on top-down predictions as the noise level increases; in deeper networks, this effect is most prominent at lower layers. In addition, the accuracy of the network implementing PC dynamics significantly increases over time-steps, compared to its equivalent forward network. All in all, our results provide novel insights relevant to Neuroscience by confirming the computational role of feedback connections in sensory systems, and to Machine Learning by revealing how these can improve the robustness of current vision models

Multimodal neural networks better explain multivoxel patterns in the hippocampus

Code to reproduce our results is available at: https://github.com/bhavinc/mutlimodal-conceptsInternational audienceThe human hippocampus possesses “concept cells”, neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality. Recently, similar concept cells were discovered in a multimodal network called CLIP (Radford et al., 2021). Here, we ask whether CLIP can explain the fMRI activity of the human hippocampus better than a purely visual (or linguistic) model. We extend our analysis to a range of publicly available uni- and multi-modal models. We demonstrate that “multimodality” stands out as a key component when assessing the ability of a network to explain the multivoxel activity in the hippocampus

Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs

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Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs

International audienceReconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research. Prior studies had success in reconstructing either the low-level image features or the semantic/high-level aspects, but rarely both. In this study, we utilized an Instance-Conditioned GAN (IC-GAN) model to reconstruct images from fMRI patterns with both accurate semantic attributes and preserved low-level details. The IC-GAN model takes as input a 119-dim noise vector and a 2048-dim instance feature vector extracted from a target image via a self-supervised learning model (SwAV ResNet-50); these instance features act as a conditioning for IC-GAN image generation, while the noise vector introduces variability between samples. We trained ridge regression models to predict instance features, noise vectors, and dense vectors (the output of the first dense layer of the IC-GAN generator) of stimuli from corresponding fMRI patterns. Then, we used the IC-GAN generator to reconstruct novel test images based on these fMRI-predicted variables. The generated images presented state-of-the-art results in terms of capturing the semantic attributes of the original test images while remaining relatively faithful to low-level image details. Finally, we use the learned regression model and the IC-GAN generator to systematically explore and visualize the semantic features that maximally drive each of several regions-of-interest in the human brain

First-Spike-Based Visual Categorization Using Reward-Modulated STDP

Reinforcement learning (RL) has recently regained popularity, with major achievements such as beating the European game of Go champion. Here, for the first time, we show that RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier. We used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire. If this assumption was correct, the neuron was rewarded, i.e. spike-timing-dependent plasticity (STDP) was applied, which reinforced the neuron's selectivity. Otherwise, anti-STDP was applied, which encouraged the neuron to learn something else. As demonstrated on various image datasets (Caltech, ETH-80, and NORB), this reward modulated STDP (R-STDP) approach extracted particularly discriminative visual features, whereas classic unsupervised STDP extracts any feature that consistently repeats. As a result, R-STDP outperformed STDP on these datasets. Furthermore, R-STDP is suitable for online learning, and can adapt to drastic changes such as label permutations. Finally, it is worth mentioning that both feature extraction and classification were done with spikes, using at most one spike per neuron. Thus the network is hardware friendly and energy efficient.Comment: supplementary materials are added, Caltech face/motorbike demonstration figure is updated, some parts of the main manuscript are moved to the supplementary materials, additional network analysis and performance comparison with deep nets are adde

Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics

International audienceDeep neural networks excel at image classification, but their performance is far less robust to input perturbations than human perception. In this work we explore whether this shortcoming may be partly addressed by incorporating brain-inspired recurrent dynamics in deep convolutional networks. We take inspiration from a popular framework in neuroscience: 'predictive coding'. At each layer of the hierarchical model, generative feedback 'predicts' (i.e., reconstructs) the pattern of activity in the previous layer. The reconstruction errors are used to iteratively update the network's representations across timesteps, and to optimize the network's feedback weights over the natural image dataset-a form of unsupervised training. We show that implementing this strategy into two popular networks, VGG16 and EfficientNetB0, improves their robustness against various corruptions. We hypothesize that other feedforward networks could similarly benefit from the proposed framework. To promote research in this direction, we provide an open-sourced PyTorch-based package called Predify, which can be used to implement and investigate the impacts of the predictive coding dynamics in any convolutional neural network

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

International audienceApplication of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of the existing SNN simulation frameworks are not practically efficient enough for large-scale AI tasks. In this paper, we introduce SpykeTorch, an open-source high-speed simulation framework based on PyTorch. This framework simulates convolutional SNNs with at most one spike per neuron and the rank-order encoding scheme. In terms of learning rules, both spike-timing-dependent plasticity (STDP) and reward-modulated STDP (R-STDP) are implemented, but other rules could be implemented easily. Apart from the aforementioned properties, SpykeTorch is highly generic and capable of reproducing the results of various studies. Computations in the proposed framework are tensor-based and totally done by PyTorch functions, which in turn brings the ability of just-in-time optimization for running on CPUs, GPUs, or Multi-GPU platforms