Retinal drug delivery: rethinking outcomes for the efficient replication of retinal behavior

The retina is a highly organized structure that is considered to be "an approachable part of the brain." It is attracting the interest of development scientists, as it provides a model neurovascular system. Over the last few years, we have been witnessing significant development in the knowledge of the mechanisms that induce the shape of the retinal vascular system, as well as knowledge of disease processes that lead to retina degeneration. Knowledge and understanding of how our vision works are crucial to creating a hardware-adaptive computational model that can replicate retinal behavior. The neuronal system is nonlinear and very intricate. It is thus instrumental to have a clear view of the neurophysiological and neuroanatomic processes and to take into account the underlying principles that govern the process of hardware transformation to produce an appropriate model that can be mapped to a physical device. The mechanistic and integrated computational models have enormous potential toward helping to understand disease mechanisms and to explain the associations identified in large model-free data sets. The approach used is modulated and based on different models of drug administration, including the geometry of the eye. This work aimed to review the recently used mathematical models to map a directed retinal network.The authors acknowledge the financial support received from the Portuguese Science and Technology Foundation (FCT/MCT) and the European Funds (PRODER/COMPETE) for the project UIDB/04469/2020 (strategic fund), co-financed by FEDER, under the Partnership Agreement PT2020. The authors also acknowledge FAPESP – São Paulo Research Foundation, for the financial support for the publication of the article.info:eu-repo/semantics/publishedVersio

Analog Weights in ReRAM DNN Accelerators

Artificial neural networks have become ubiquitous in modern life, which has triggered the emergence of a new class of application specific integrated circuits for their acceleration. ReRAM-based accelerators have gained significant traction due to their ability to leverage in-memory computations. In a crossbar structure, they can perform multiply-and-accumulate operations more efficiently than standard CMOS logic. By virtue of being resistive switches, ReRAM switches can only reliably store one of two states. This is a severe limitation on the range of values in a computational kernel. This paper presents a novel scheme in alleviating the single-bit-per-device restriction by exploiting frequency dependence of v-i plane hysteresis, and assigning kernel information not only to the device conductance but also partially distributing it to the frequency of a time-varying input. We show this approach reduces average power consumption for a single crossbar convolution by up to a factor of x16 for an unsigned 8-bit input image, where each convolutional process consumes a worst-case of 1.1mW, and reduces area by a factor of x8, without reducing accuracy to the level of binarized neural networks. This presents a massive saving in computing cost when there are many simultaneous in-situ multiply-and-accumulate processes occurring across different crossbars.Comment: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, 5 pages, 4 figure

Modelling and performance analysis of a neurostimulation system

2009 - 2010The activity of this thesis is devoted to modelling the electromagnetic behaviour of complex biological structures and in particular of nerve cells and the study of nanotechnology applications to these structures for therapeutic, diagnostic and investigative purposes. The 'design' of artificial nanoscale devices is the chance to accurately understand and manipulate the phenomena that occur inside biological structures. Until a few years ago, the most widely accepted technology was that of MEA (Micro Electrode Arrays), which indeed is characterized by several limitations, such as microelectrodes whose sizes are very larger than individual cells, lack of local control of electrical activity, etc. In recent years, new possibilities have arisen due to the increasing development of nanotechnology. In particular, the Carbon Nanotubes (CNTs) are very compatible as systems capable of interfacing with the Central Nervous System (CNS). This paves the way for neuro-implantable devices for vision, hearing, taste, movement, elimination of seizures, repair and improvement of brain functions. Increasingly, attempts are being made to try to integrate CNTs with other technologies to develop biochips that can help repairing damaged tissues of the CNS. MEA with electrodes coated with CNTs have recently been proposed. Further progress is being made in the direction of the nanoarray electrode (the so-called NEA - Nano Electrode Array), to have greater spatial and temporal resolution. It is within this very broad and extremely complex scenario that this thesis is placed, investigating Finite Element Method (FEM) modelling and design issues of a neurostimulation system at nanoscale. The apparatus is an array consisting of vertically-aligned CNTs acting as nanoelectrodes to provide a signal electrical stimulation. In particular, the application considered is the neurostimulation of the retina, where the spatial resolution is a crucial factor and a sensitivity analysis proves to be very useful in studying system performances dependences on different main setup parameters. The electromagnetic modelling of the system is performed in a FEM multiphysics environment used to couple in an efficient way the non linear differential equations related to biological system with Maxwell equations. One of the peculiar characteristics of this study is the approach proposed to obtain the dynamics of Action Potential (AP), the basic unit of the nervous message, inside a neuron membrane without the use of the "transmission line" equation, widely adopted in literature. Starting from the Hodgkin and Huxley (HH) model (the nonlinear partial differential equations describing the chemical transport through the nerve membrane) a translation in terms of suitable equivalent electric parameters is obtained for different pieces of the neuron and in a model describing soma, axon hillock and initial axon segment. The HH equations are coupled with those of Maxwell (in quasi-static formulation, due to the considered range of frequencies) and, exploiting the high nonlinearity of the membrane domain, the triggering and propagation of the AP is simulated. A comparison between two proposed modelling approaches is carried out investigating the trade-off between accuracy and numerical burden. Moreover, a systematic determination of the most significant parameters and design variables is also carried out (e.g. AP triggering and its promptness as well as AP duration), allowing to maximize the effectiveness of the artificial stimulation of the biological cell. The optimization of system parameters ( in terms of stimulus and electrode geometrical features) in order to maximize the selective initiation of APs on one neuron or on clusters of several neurons can be effectively accomplished to boost the spatial resolution of the device. Finally, conclusions are drawn and possible future developments are discussed both in terms of model implementation and further studies of the neuroelectrical stimulation and analysis.[edited by author]IX n.s

Modèle et simulateur à grande échelle d'une rétine biologique, avec contrôle de gain

The retina is a complex neural structure. The characteristics of retinal processing are reviewed extensively in Part I of this work: It is a very ordered structure, which proceeds to band-pass spatio-temporal enhancements of the incoming light, along different parallel output pathways with distinct spatio-temporal properties. The spike trains emitted by the retina have a complex statistical structure, such that precise spike timings may play a role in the code conveyed by the retina. Several mechanisms of gain control provide a constant adaptation of the retina to luminosity and contrast. The retina model that we have defined and implemented in Part II can account for a good part of this complexity. It can model spatio-temporal band-pass behavior with adjustable filtering scales, with the inclusion of plausible mechanisms of contrast gain control and spike generation. The gain control mechanism proposed in the model provides a good fit to experimental data, and it can induce interesting effects of local renormalization in the output retinal image. Furthermore, a mathematical analysis confirms that the gain control behaves well under simple sinusoidal stimulation. Finally, the simulator /Virtual Retina/ implements the model on a large-scale, so that it can emulate up to around 100,000 cells with a processing speed of about 1/100 real time. It is ready for use in various applications, while including a number of advanced retinal functionalities which are too often overlooked.La rétine est une structure neuronale complexe, qui non seulement capte la lumière incidente au fond de l'oeil, mais procède également à des transformations importantes du signal lumineux. Dans la Partie I de ce travail, nous résumons en détail les caractéristiques fonctionnelles de la rétine des vertébrés: Il s'agit d'une structure très ordonnée, qui réalise un filtrage passe-bande du stimulus visuel, selon différents canaux parallèles d'information aux propriétés spatio-temporelles distinctes. Les trains de potentiels d'action émis par la rétine ont également une structure statistique complexe, susceptible de véhiculer une information importante. De nombreux mécanismes de contrôle de gain permettent une adaptation constante à la luminosité et au contraste. Le modèle de rétine défini et implémenté dans la Partie II de ce travail prend en compte une part importante de cette complexité. Il reproduit le comportement passe-bande, à l'aide de filtres linéaires spatio-temporels appropriés. Des mécanismes non-linéaires d'adaptation au contraste et de génération de potentiels d'action sont également inclus. Le mécanisme de contrôle du gain au contraste proposé permet une bonne reproduction des données expérimentales, et peut également véhiculer d'importants effets d'égalisation spatiale des contrastes en sortie de rétine. De plus, une analyse mathématique confirme que notre mécanisme a le comportement escompté en réponse à une stimulation sinusoïdale. Enfin, le simulateur /Virtual Retina/ implémente le modèle à grande échelle, permettant la simulation d'environ 100 000 cellules en un temps raisonnable (100 fois le temps réel)

Simulating and optimizing electrical current flow and neuronal activation in retinal implants

© 2018 Dr Timothy Bede Esler[Abstract Withheld

A mechanistic model of motion processing in the early visual system

A prerequisite for the perception of motion in primates is the transformation of varying intensities of light on the retina into an estimation of position, direction and speed of coherent objects. The neuro-computational mechanisms relevant for object feature encoding have been thoroughly explored, with many neurally plausible models able to represent static visual scenes. However, motion estimation requires the comparison of successive scenes through time. Precisely how the necessary neural dynamics arise and how other related neural system components interoperate have yet to be shown in a large-scale, biologically realistic simulation. The proposed model simulates a spiking neural network computation for representing object velocities in cortical areas V1 and middle temporal area (MT). The essential neural dynamics, hypothesized to reside in networks of V1 simple cells, are implemented through recurrent population connections that generate oscillating spatiotemporal tunings. These oscillators produce a resonance response when stimuli move in an appropriate manner in their receptive fields. The simulation shows close agreement between the predicted and actual impulse responses from V1 simple cells using an ideal stimulus. By integrating the activities of like V1 simple cells over space, a local measure of visual pattern velocity can be produced. This measure is also the linear weight of an associated velocity in a retinotopic map of optical flow. As a demonstration, the classic motion stimuli of drifting sinusoidal gratings and variably coherent dots are used as test stimuli and optical flow maps are generated. Vector field representations of this structure may serve as inputs for perception and decision making processes in later brain areas

The vertebrate retina: a functional review

In this report, we summarize the major properties of retinal filtering and organization, as understood by numerous experiments and models over the last decades. For this review, we take a functional approach, trying to answer this apparently simple question: What are the main characteristics of the retinal output in terms of signal processing, which should be retained in a functional model

On the potential role of lateral connectivity in retinal anticipation

We analyse the potential effects of lateral connectivity (amacrine cells and gap junctions) on motion anticipation in the retina. Our main result is that lateral connectivity can-under conditions analysed in the paper-trigger a wave of activity enhancing the anticipation mechanism provided by local gain control [8, 17]. We illustrate these predictions by two examples studied in the experimental literature: differential motion sensitive cells [1] and direction sensitive cells where direction sensitivity is inherited from asymmetry in gap junctions connectivity [73]. We finally present reconstructions of retinal responses to 2D visual inputs to assess the ability of our model to anticipate motion in the case of three different 2D stimuli