Neuromorphic analogue VLSI

Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Simulations of information processing, control, and plasticity effects in the olfactory bulb

The olfactory system processes complex and varied information in its detection, recognition, and memory of odors. The exact functions that the olfactory bulb plays in this processing is still largely unknown. Studies were performed to help reveal bulb functionality in the olfactory system while contributing to the set of computer methods available for the study of neural systems.One interesting property of bulbar neurons is an increase in primary cell firing thresholds with depth. Since increased odor concentrations generally result in higher frequency inputs to the bulb and thus higher summation levels of primary cell membrane potentials, this threshold gradation transforms the frequency-encoded concentration data into a spatial representation in the number of primary cells responding in a single olfactory bulb glomerular region.Since this transformation relies on temporal summation of post-synaptic potentials (PSPs) to reflect concentration levels, direct physiological modeling of the transformation was possible while providing the added efficiency to permit the simulation of large numbers of cells and synaptic interactions. A novel physiological modeling methodology was developed for these tests that extends the extant physiological models to include time-constant and driving-force interactive effects between post-synaptic inputs. This novel method is derived using linear superposition of inputs to a lumped-circuit cell representation, resulting in a difference-of-exponentials PSP function that is more realistic and flexible than the common empirically-chosen alpha function.Also, the effects that interneuronal dendritic spines have on bulbar inhibitions were tested using biophysical computer simulations of primary-to-granule dendrodendritic reciprocal interactions. The graded strength properties of these synapses showed that reciprocal inhibitions to primary mitral cells are facilitated by the spine structures without the need of a high gain gradation while reducing lateral inhibition to other mitral cells. Furthermore, increases in the neck axial resistance of the synapsed spine further strengthen the reciprocal response and reduce the lateral inhibition; such resistance changes could therefore result in dendrodendritic synaptic plasticities and olfactory memory operations

Memory and forgetting processes with the firing neuron model

The aim of this paper is to present a novel algorithm for learning and forgetting within a very simplified, biologically derived model of the neuron, called firing cell (FC). FC includes the properties: (a) delay and decay of postsynaptic potentials, (b) modification of internal weights due to propagation of postsynaptic potentials through the dendrite, (c) modification of properties of the analog weight memory for each input due to a pattern of long-term synaptic potentiation. The FC model could be used in one of the three forms: excitatory, inhibitory, or receptory (gan­glion cell). The computer simulations showed that FC precisely performs the time integration and coincidence detection for incoming spike trains on all inputs. Any modification of the initial values (internal parameters) or inputs patterns caused the following changes of the interspike intervals time series on the output, even for the 10 s or 20 s real time course simulations. It is the basic evidence that the FC model has chaotic dynamical properties. The second goal is the presentation of various nonlinear methods for analysis of a biological time series. (Folia Morphol 2018; 77, 2: 221–233

Infomorphic networks: Locally learning neural networks derived from partial information decomposition

Understanding the intricate cooperation among individual neurons in performing complex tasks remains a challenge to this date. In this paper, we propose a novel type of model neuron that emulates the functional characteristics of biological neurons by optimizing an abstract local information processing goal. We have previously formulated such a goal function based on principles from partial information decomposition (PID). Here, we present a corresponding parametric local learning rule which serves as the foundation of "infomorphic networks" as a novel concrete model of neural networks. We demonstrate the versatility of these networks to perform tasks from supervised, unsupervised and memory learning. By leveraging the explanatory power and interpretable nature of the PID framework, these infomorphic networks represent a valuable tool to advance our understanding of cortical function.Comment: 31 pages, 5 figure

A Study on Physics Informed Neural Networks, with Applications for Compartment Models

The main goal of this thesis was to investigate the methodology of Physics Informed Neural Networks (PiNN), as a computational tool leveraging differential equations as a regularization for a learning task. PiNN is a new field of research and therefore particular concern was given to the task of obtaining an understanding of the method, gauging benefits, performance, and appropriateness in relation to established methods. In order to develop this knowledge, the methodology was implemented and applied through four case studies, three of which demonstrates achievements already supported by the literature. In addition case three incorporates a thorough testing scheme, scoping out PiNNs’ capabilities of parameter discovery and regularization. From this a larger framework is developed. In case four, the framework is utilized applying of the method of PiNN in a real world biomedical context, realized as a model of the circulatory system. The implementations were realized in a bottom up approach utilizing the neural network capabilities of PyTorch. Overall, the findings of the thesis support the established findings of previous literature in regards to performance and capabilities. Additionally, important details in regards to implementation and solution validity is highlighted, addressing the conditions necessary for the optimal use of PiNN as a methodology

Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications

In recent years, increasingly complex computational models are being built to describe physical systems which has led to increased use of surrogate models to reduce computational cost. In problems related to Structural Health Monitoring (SHM), models capable of both handling high-dimensional data and quantifying uncertainty are required. In this work, our goal is to propose a conditional deep generative model as a surrogate aimed at such applications and high-dimensional stochastic structural simulations in general. To that end, a conditional variational autoencoder (CVAE) utilizing convolutional neural networks (CNNs) is employed to obtain reconstructions of spatially ordered structural response quantities for structural elements that are subjected to stochastic loading. Two numerical examples, inspired by potential SHM applications, are utilized to demonstrate the performance of the surrogate. The model is able to achieve high reconstruction accuracy compared to the reference Finite Element (FE) solutions, while at the same time successfully encoding the load uncertainty.Comment: 28 pages, 14 figures. Submitted to Elsevier Journal for publicatio