Perspective: Organic electronic materials and devices for neuromorphic engineering

Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies (ICT). In fact, new computing solutions and new hardware platforms are expected to emerge to answer to the new needs and challenges of our societies. In this revolution, lots of candidates technologies are explored and will require leveraging of the pro and cons. In this perspective paper belonging to the special issue on neuromorphic engineering of Journal of Applied Physics, we focus on the current achievements in the field of organic electronics and the potentialities and specificities of this research field. We highlight how unique material features available through organic materials can be used to engineer useful and promising bioinspired devices and circuits. We also discuss about the opportunities that organic electronic are offering for future research directions in the neuromorphic engineering field

Deciphering Neuron-Glia Compartmentalization in Cortical Energy Metabolism

Energy demand is an important constraint on neural signaling. Several methods have been proposed to assess the energy budget of the brain based on a bottom-up approach in which the energy demand of individual biophysical processes are first estimated independently and then summed up to compute the brain's total energy budget. Here, we address this question using a novel approach that makes use of published datasets that reported average cerebral glucose and oxygen utilization in humans and rodents during different activation states. Our approach allows us (1) to decipher neuron-glia compartmentalization in energy metabolism and (2) to compute a precise state-dependent energy budget for the brain. Under the assumption that the fraction of energy used for signaling is proportional to the cycling of neurotransmitters, we find that in the activated state, most of the energy (∼80%) is oxidatively produced and consumed by neurons to support neuron-to-neuron signaling. Glial cells, while only contributing for a small fraction to energy production (∼6%), actually take up a significant fraction of glucose (50% or more) from the blood and provide neurons with glucose-derived energy substrates. Our results suggest that glycolysis occurs for a significant part in astrocytes whereas most of the oxygen is utilized in neurons. As a consequence, a transfer of glucose-derived metabolites from glial cells to neurons has to take place. Furthermore, we find that the amplitude of this transfer is correlated to (1) the activity level of the brain; the larger the activity, the more metabolites are shuttled from glia to neurons and (2) the oxidative activity in astrocytes; with higher glial pyruvate metabolism, less metabolites are shuttled from glia to neurons. While some of the details of a bottom-up biophysical approach have to be simplified, our method allows for a straightforward assessment of the brain's energy budget from macroscopic measurements with minimal underlying assumptions

MR diffusion changes in the perimeter of the lateral ventricles demonstrate periventricular injury in post-hemorrhagic hydrocephalus of prematurity

OBJECTIVES: Injury to the preterm lateral ventricular perimeter (LVP), which contains the neural stem cells responsible for brain development, may contribute to the neurological sequelae of intraventricular hemorrhage (IVH) and post-hemorrhagic hydrocephalus of prematurity (PHH). This study utilizes diffusion MRI (dMRI) to characterize the microstructural effects of IVH/PHH on the LVP and segmented frontal-occipital horn perimeters (FOHP). STUDY DESIGN: Prospective study of 56 full-term infants, 72 very preterm infants without brain injury (VPT), 17 VPT infants with high-grade IVH without hydrocephalus (HG-IVH), and 13 VPT infants with PHH who underwent dMRI at term equivalent. LVP and FOHP dMRI measures and ventricular size-dMRI correlations were assessed. RESULTS: In the LVP, PHH had consistently lower FA and higher MD and RD than FT and VPT (p\u3c.050). However, while PHH FA was lower, and PHH RD was higher than their respective HG-IVH measures (p\u3c.050), the MD and AD values did not differ. In the FOHP, PHH infants had lower FA and higher RD than FT and VPT (p\u3c.010), and a lower FA than the HG-IVH group (p\u3c.001). While the magnitude of AD in both the LVP and FOHP were consistently less in the PHH group on pairwise comparisons to the other groups, the differences were not significant (p\u3e.050). Ventricular size correlated negatively with FA, and positively with MD and RD (p\u3c.001) in both the LVP and FOHP. In the PHH group, FA was lower in the FOHP than in the LVP, which was contrary to the observed findings in the healthy infants (p\u3c.001). Nevertheless, there were no regional differences in AD, MD, and RD in the PHH group. CONCLUSION: HG-IVH and PHH results in aberrant LVP/FOHP microstructure, with prominent abnormalities among the PHH group, most notably in the FOHP. Larger ventricular size was associated with greater magnitude of abnormality. LVP/FOHP dMRI measures may provide valuable biomarkers for future studies directed at improving the management and neurological outcomes of IVH/PHH

Neuronal transmission mechanisms

Several techniques were used to study the morphology of the salivary apparatus of the cockroach, Nauphoeta cinevea. A general survey of the ultrastructure was made. The acinar cells were of two distinct types: peripheral and central cells. The ducts that these cells give rise to could be classified into three morphologically distinct areas. The fine structure of the reservoir ducts was also studied.Intracellular injections of Procion yellow dye and the use of lanthanum, as an electron dense marker, showed that there were many intercalated gap-junctions between the septate desmosomes of the acinar cells.The innervation of the apparatus was studied in detail and it was observed, using techniques of fluorescence histochemistry and electron microscopy, that the salivary nerves, which arise from the suboesophageal ganglion, branch over the surface of the acini. The axons associated with the acini were found to be of two morphologically distinct types, designated type A and type B. Several histochemical tests indicated that type A axons contained a catecholamine.It was attempted to stimulate the salivary nerves. This resulted in structural changes within peripheral cells and type A axons. When the salivary nerves were cut several degenerating axon profiles could be identified in association with the gland cells. This was not observed when the stomatogastric nerve was cut.Finally, enzyme-inhibiting drugs were used to interrupt the synthetic pathways of catecholamines. These experiments led to a number of unexpected results including the observation that some of the actions of these drugs appeared to be postsynaptic

Artificial Neurogenesis: An Introduction and Selective Review

International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature

Glioblastoma Models Reveal the Connection between Adult Glial Progenitors and the Proneural Phenotype

Tumor heterogeneity is a major obstacle for finding effective treatment of Glioblastoma (GBM). Based on global expression analysis, GBM can be classified into distinct subtypes: Proneural, Neural, Classical and Mesenchymal. The signatures of these different tumor subtypes may reflect the phenotypes of cells giving rise to them. However, the experimental evidence connecting any specific subtype of GBM to particular cells of origin is lacking. In addition, it is unclear how different genetic alterations interact with cells of origin in determining tumor heterogeneity. This issue cannot be addressed by studying end-stage human tumors.To address this issue, we used retroviruses to deliver transforming genetic lesions to glial progenitors in adult mouse brain. We compared the resulting tumors to human GBM. We found that different initiating genetic lesions gave rise to tumors with different growth rates. However all mouse tumors closely resembled the human Proneural GBM. Comparative analysis of these mouse tumors allowed us to identify a set of genes whose expression in humans with Proneural GBM correlates with survival.This study offers insights into the relationship between adult glial progenitors and Proneural GBM, and allows us to identify molecular alterations that lead to more aggressive tumor growth. In addition, we present a new preclinical model that can be used to test treatments directed at a specific type of GBM in future studies

Photonic neural networks based on integrated silicon microresonators

The recent progress of artificial intelligence (AI) has boosted the computational possibilities in fields where standard computers are not able to perform. The AI paradigm is to emulate human intelligence and therefore breaks the familiar architecture on which digital computers are based. In particular, neuromorphic computing, artificial neural networks (ANN) and deep learning models mimic how the brain computes. Large networks of interconnected neurons whose synapsis are individually strengthened or weakened during the learning phase find many applications. With this respect, photonics is a suitable platform to implement ANN hardware thanks to its speed, low power dissipation and multi-wavelength opportunities. One photonic device candidate to perform as an optical neuron is the optical microring resonator. Indeed microring resonators show both a nonlinear response and a capability of optical energy storage, which can be interpreted as a fading memory. Moreover, by using silicon photonics, the photonic integrated circuits can be fabricated in volume and with integrated electronics on board. For these reasons, here, we describe the physics of silicon microring resonators and of arrays of microring resonators for application in neuromorphic computing. We describe different types of ANNs from feed-forward networks to photonics extreme learning machines and reservoir computing. In addition, we discuss also hybrid systems where silicon microresonators are coupled to other active materials. this review aims to introduce the basics and to discuss the most recent developments in the field.Comment: 35 pages, 23 figure