Engineered biological neural networks on high density CMOS micro electrode arrays

In bottom-up neuroscience, questions on neural information processing are addressed by engineering small but reproducible biological neural networks of defined network topology \textit{in vitro}. The network topology can be controlled by culturing neurons within polydimethylsiloxane (PDMS) microstructures that are combined with microelectrode arrays (MEAs) for electric access to the network. However, currently used glass MEAs are limited to 256 electrodes and pose a limitation to the spatial resolution as well as the design of more complex microstructures. The use of high density complementary metal-oxide-semiconductor (CMOS) MEAs greatly increases the spatiotemporal resolution, enabling sub-cellular readout and stimulation of neurons in defined neural networks. Unfortunately, the non-planar surface of CMOS MEAs complicates the attachment of PDMS microstructures. To overcome the problem of axons escaping the microstructures through the ridges of the CMOS MEA, we stamp-transferred a thin film of hexane-diluted PDMS onto the array such that the PDMS filled the ridges at the contact surface of the microstructures without clogging the axon guidance channels. Moreover, we provide an impedance-based method to visualize the exact location of the microstructures on the MEA and show that our method can confine axonal growth within the PDMS microstructures. Finally, the high spatiotemporal resolution of the CMOS MEA enabled us to show that we can guide action potentials using the unidirectional topology of our circular multi-node microstructure.

Cognitive neural prosthetics – the way from experiment to clinical application

Accepted: September 3, 2021. Objective of this review is to highlight some aspects of the development and use of cognitive neuroprostheses, such as the technological background for their developing and key modern projects in this field. The literature sources were analyzed and the place of neuroprostheses among other artificial organs and tissues, which are under development or already used in clinical practice, was defined. The main principles of their implementation, structural elements and operating conditions were described. Also, this review presents some examples of diseases which can be corrected by cognitive neuroprostheses. The mechanisms of compensation for the functions of the damaged brain structures when using neuroprostheses are described on the basis of the principles of their interaction with biological neural networks. Descriptions of advanced developments that are currently relevant are given. Moreover, information is provided on the protocols and results of tests on animals and humans of the artificial hippocampus, as well as the results of testing a prosthesis that allows restoring the functions of the prefrontal cortex in animals. The examples considered in the review allow us to conclude that cognitive neuroprostheses are not just a hypothetic concept. They are implemented as specialized experimental solutions for practical clinical issues. Currently, the greatest success has been achieved in restoring the hippocampus functions.

A Correspondence between Normalization Strategies in Artificial and Biological Neural Networks

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalization, weight normalization, and their many variants help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this article, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neuron's activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent—that is, both push activation patterns of hidden units toward a homeostatic state, where all neurons are equally used—and we argue that such representations can improve coding capacity, discrimination, and regularization. As a proof of concept, we develop an algorithm, inspired by a neural normalization technique called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several data sets. Overall, we hope this bidirectional connection will inspire neuroscientists and machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

Neural Networks Application on Human Skin Biophysical Impedance Characterizations

Artificial neural networks (ANNs) are basically the structures that perform input–output mapping. This mapping mimics the signal processing in biological neural networks. The basic element of biological neural network is a neuron. Neurons receive input signals from other neurons or the environment, process them, and generate their output which represents the input to another neuron of the network. Neurons can change their sensitivity to input signals. Each neuron has a simple rule to process an input signal. Biological neural networks have the property that signals are processed through many parallel connections (massively parallel processing). The activity of all neurons in these parallel connections is summed and represents the output of the whole network. The main feature of biological neural networks is that changes in the sensitivity of the neurons lead to changes in the operation of the entire network. This is called adaptation and is correlated with the learning process of living organisms. In this paper, a set of artificial neural networks are used for classifying the human skin biophysical impedance data.

Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks

One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.

Dynamic Detection and Reversal of Myocardial Ischemia Using an Artificially Intelligent Bioelectronic Medicine

Myocardial ischemia is spontaneous, usually asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, biological neural networks cannot reliably detect and correct myocardial ischemia on their own. Supplementing biological neural networks may enable reliable detection, and potentially even facilitate correction, of myocardial ischemia. In this study, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements biological neural networks enabling reliable detection and correction of myocardial ischemia (preclinical experiments in rats). This responsive bioelectronic medicine uses an ANN with a long short-term memory layer to decode spontaneous myocardial ischemia and autonomously trigger vagus nerve stimulation (VNS) for reducing chronotropy, afterload, and myocardial oxygen demand. We first used injections of cardiovascular stress inducing agents (dobutamine and norepinephrine) that produce a model of spontaneous myocardial ischemia. Next, ANNs were trained to decode spontaneous cardiovascular stress and myocardial ischemia, with an overall accuracy of ~92%. ANN-controlled VNS reversed the major biomarkers of myocardial ischemia with no side effects. In contrast, open-loop VNS or ANN controlled VNS following a caudal vagotomy essentially failed to reverse correlates of myocardial ischemia. Lastly, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations of stress pathophysiology and unsupervised detection of new emerging stress states. Together, this adaptive architecture provides clinically relevant insights as pathophysiology evolves. Overall, these results provide a first-time demonstration that ANNs can supplement deficient biological neural networks to dynamically detect and help bioelectronically reverse cardiovascular pathophysiology.