Efficient hardware implementations of bio-inspired networks

The human brain, with its massive computational capability and power efficiency in small form factor, continues to inspire the ultimate goal of building machines that can perform tasks without being explicitly programmed. In an effort to mimic the natural information processing paradigms observed in the brain, several neural network generations have been proposed over the years. Among the neural networks inspired by biology, second-generation Artificial or Deep Neural Networks (ANNs/DNNs) use memoryless neuron models and have shown unprecedented success surpassing humans in a wide variety of tasks. Unlike ANNs, third-generation Spiking Neural Networks (SNNs) closely mimic biological neurons by operating on discrete and sparse events in time called spikes, which are obtained by the time integration of previous inputs. Implementation of data-intensive neural network models on computers based on the von Neumann architecture is mainly limited by the continuous data transfer between the physically separated memory and processing units. Hence, non-von Neumann architectural solutions are essential for processing these memory-intensive bio-inspired neural networks in an energy-efficient manner. Among the non-von Neumann architectures, implementations employing non-volatile memory (NVM) devices are most promising due to their compact size and low operating power. However, it is non-trivial to integrate these nanoscale devices on conventional computational substrates due to their non-idealities, such as limited dynamic range, finite bit resolution, programming variability, etc. This dissertation demonstrates the architectural and algorithmic optimizations of implementing bio-inspired neural networks using emerging nanoscale devices. The first half of the dissertation focuses on the hardware acceleration of DNN implementations. A 4-layer stochastic DNN in a crossbar architecture with memristive devices at the cross point is analyzed for accelerating DNN training. This network is then used as a baseline to explore the impact of experimental memristive device behavior on network performance. Programming variability is found to have a critical role in determining network performance compared to other non-ideal characteristics of the devices. In addition, noise-resilient inference engines are demonstrated using stochastic memristive DNNs with 100 bits for stochastic encoding during inference and 10 bits for the expensive training. The second half of the dissertation focuses on a novel probabilistic framework for SNNs using the Generalized Linear Model (GLM) neurons for capturing neuronal behavior. This work demonstrates that probabilistic SNNs have comparable perform-ance against equivalent ANNs on two popular benchmarks - handwritten-digit classification and human activity recognition. Considering the potential of SNNs in energy-efficient implementations, a hardware accelerator for inference is proposed, termed as Spintronic Accelerator for Probabilistic SNNs (SpinAPS). The learning algorithm is optimized for a hardware friendly implementation and uses first-to-spike decoding scheme for low latency inference. With binary spintronic synapses and digital CMOS logic neurons for computations, SpinAPS achieves a performance improvement of 4x in terms of GSOPS/W/mm$^2$ when compared to a conventional SRAM-based design. Collectively, this work demonstrates the potential of emerging memory technologies in building energy-efficient hardware architectures for deep and spiking neural networks. The design strategies adopted in this work can be extended to other spike and non-spike based systems for building embedded solutions having power/energy constraints

Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern Recognizers

This PhD thesis is focused on the central idea that single neurons in the brain should be regarded as temporally precise and highly complex spatio-temporal pattern recognizers. This is opposed to the prevalent view of biological neurons as simple and mainly spatial pattern recognizers by most neuroscientists today. In this thesis, I will attempt to demonstrate that this is an important distinction, predominantly because the above-mentioned computational properties of single neurons have far-reaching implications with respect to the various brain circuits that neurons compose, and on how information is encoded by neuronal activity in the brain. Namely, that these particular "low-level" details at the single neuron level have substantial system-wide ramifications. In the introduction we will highlight the main components that comprise a neural microcircuit that can perform useful computations and illustrate the inter-dependence of these components from a system perspective. In chapter 1 we discuss the great complexity of the spatio-temporal input-output relationship of cortical neurons that are the result of morphological structure and biophysical properties of the neuron. In chapter 2 we demonstrate that single neurons can generate temporally precise output patterns in response to specific spatio-temporal input patterns with a very simple biologically plausible learning rule. In chapter 3, we use the differentiable deep network analog of a realistic cortical neuron as a tool to approximate the gradient of the output of the neuron with respect to its input and use this capability in an attempt to teach the neuron to perform nonlinear XOR operation. In chapter 4 we expand chapter 3 to describe extension of our ideas to neuronal networks composed of many realistic biological spiking neurons that represent either small microcircuits or entire brain regions

Influence of the dentritic morphology on electrophysiological responses of thalamocortical neurons

Les neurones thalamiques de relai ont un rôle exclusif dans la transformation et de transfert de presque toute l'information sensorielle dans le cortex. L'intégration synaptique et la réponse électrophysiologique des neurones thalamiques de relai sont déterminées non seulement par l’état du réseau impliqué, mais ils sont également contrôlés par leurs propriétés intrinsèques tels les divers canaux ioniques voltage-dépendants ainsi que l’arborisation dendritique élaboré. Par conséquent, investiguer sur le profil complexe de morphologie dendritique et sur les propriétés dendritiques actives révèle des renseignements importants sur la fonction d'entrée-sortie de neurones thalamiques de relai. Dans cette étude, nous avons reconstruit huit neurones thalamocorticaux (TC) du noyau VPL de chat adulte. En se basant sur ces données morphologiques complètes, nous avons développé plusieurs modèles multicompartimentaux afin de trouver un rôle potentiellement important des arbres dendritiques des neurones de TC dans l'intégration synaptique et l’intégration neuronale. L'analyse des caractéristiques morphologiques des neurones TC accordent des valeurs précises à des paramètres géométriques semblables ou différents de ceux publiés antérieurement. En outre, cette analyse fait ressortir de tous nouveaux renseignements concernant le patron de connectivité entre les sections dendritiques telles que l'index de l'asymétrie et la longueur de parcours moyen (c'est-à-dire, les paramètres topologiques). Nous avons confirmé l’étendue des valeurs rapportée antérieurement pour plusieurs paramètres géométriques tels que la zone somatique (2956.24±918.89 m2), la longueur dendritique totale (168017.49±4364.64 m) et le nombre de sous-arbres (8.3±1.5) pour huit neurones TC. Cependant, contrairement aux données rapportées antérieurement, le patron de ramification dendritique (avec des cas de bifurcation 98 %) ne suit pas la règle de puissance de Rall 3/2 pour le ratio géométrique (GR), et la valeur moyenne de GR pour un signal de propagation est 2,5 fois plus grande que pour un signal rétropropagé. Nous avons également démontré une variabilité significative dans l'index de symétrie entre les sous-arbres de neurones TC, mais la longueur du parcours moyen n'a pas montré une grande variation à travers les ramifications dendritiques des différents neurones. Nous avons examiné la conséquence d’une distribution non-uniforme des canaux T le long de l'arbre dendritique sur la réponse électrophysiologique émergeante, soit le potentiel Ca 2+ à seuil bas (low-threshold calcium spike, LTS) des neurones TC. En appliquant l'hypothèse du «coût minimal métabolique», nous avons constaté que le neurone modélisé nécessite un nombre minimal de canaux-T pour générer un LTS, lorsque les canaux-T sont situés dans les dendrites proximales. Dans la prochaine étude, notre modèle informatique a illustré l'étendue d'une rétropropagation du potentiel d'action et de l'efficacité de la propagation vers des PPSEs générés aux branches dendritiques distales. Nous avons démontré que la propagation dendritique des signaux électriques est fortement contrôlée par les paramètres morphologiques comme illustré par les différents paliers de polarisation obtenus par un neurone à équidistance de soma pendant la propagation et la rétropropagation des signaux électriques. Nos résultats ont révélé que les propriétés géométriques (c.-à-d. diamètre, GR) ont un impact plus fort sur la propagation du signal électrique que les propriétés topologiques. Nous concluons que (1) la diversité dans les propriétés morphologiques entre les sous-arbres d'un seul neurone TC donne une capacité spécifique pour l'intégration synaptique et l’intégration neuronale des différents dendrites, (2) le paramètre géométrique d'un arbre dendritique fournissent une influence plus élevée sur le contrôle de l'efficacité synaptique et l'étendue du potentiel d'action rétropropagé que les propriétés topologiques, (3) neurones TC suivent le principe d’optimisation pour la distribution de la conductance voltage-dépendant sur les arbres dendritiques.Thalamic relay neurons have an exclusive role in processing and transferring nearly all sensory information into the cortex. The synaptic integration and the electrophysiological response of thalamic relay neurons are determined not only by a state of the involved network, but they are also controlled by their intrinsic properties; such as diverse voltage-dependent ionic channels as well as by elaborated dendritic arborization. Therefore, investigating the complex pattern of dendritic morphology and dendritic active properties reveals important information on the input-output function of thalamic relay neurons. In this study, we reconstructed eight thalamocortical (TC) neurons from the VPL nucleus of adult cats. Based on these complete morphological data, we developed several multi-compartment models in order to find a potentially important role for dendritic trees of TC neurons in the synaptic integration and neuronal computation. The analysis of morphological features of TC neurons yield precise values of geometrical parameters either similar or different from those previously reported. In addition, this analysis extracted new information regarding the pattern of connectivity between dendritic sections such as asymmetry index and mean path length (i.e., topological parameters). We confirmed the same range of previously reported value for several geometric parameters such as the somatic area (2956.24±918.89 m2), the total dendritic length (168017.49±4364.64 m) and the number of subtrees (8.3±1.5) for eight TC neurons. However, contrary to previously reported data, the dendritic branching pattern (with 98% bifurcation cases) does not follow Rall’s 3/2 power rule for the geometrical ratio (GR), and the average GR value for a forward propagation signal was 2.5 times bigger than for a backward propagating signal. We also demonstrated a significant variability in the symmetry index between subtrees of TC neurons, but the mean path length did not show a large variation through the dendritic arborizations of different neurons. We examined the consequence of non-uniform distribution of T-channels along the dendritic tree on the prominent electrophysiological response, the low-threshold Ca2+ spike (LTS) of TC neurons. By applying the hypothesis of “minimizing metabolic cost”, we found that the modeled neuron needed a minimum number of T-channels to generate low-threshold Ca2+ spike (LTS), when T-channels were located in proximal dendrites. In the next study, our computational model illustrated the extent of an action potential back propagation and the efficacy of forward propagation of EPSPs arriving at the distal dendritic branches. We demonstrated that dendritic propagation of electrical signals is strongly controlled by morphological parameters as shown by different levels of polarization achieved by a neuron at equidistance from the soma during back and forward propagation of electrical signals. Our results revealed that geometrical properties (i.e. diameter, GR) have a stronger impact on the electrical signal propagation than topological properties. We conclude that (1) diversity in the morphological properties between subtrees of a single TC neuron lead to a specific ability for synaptic integration and neuronal computation of different dendrites, (2) geometrical parameter of a dendritic tree provide higher influence on the control of synaptic efficacy and the extent of the back propagating action potential than topological properties, (3) TC neurons follow the optimized principle for distribution of voltage-dependent conductance on dendritic trees

On the path integration system of insects: there and back again

Navigation is an essential capability of animate organisms and robots. Among animate organisms of particular interest are insects because they are capable of a variety of navigation competencies solving challenging problems with limited resources, thereby providing inspiration for robot navigation. Ants, bees and other insects are able to return to their nest using a navigation strategy known as path integration. During path integration, the animal maintains a running estimate of the distance and direction to its nest as it travels. This estimate, known as the `home vector', enables the animal to return to its nest. Path integration was the technique used by sea navigators to cross the open seas in the past. To perform path integration, both sailors and insects need access to two pieces of information, their direction and their speed of motion over time. Neurons encoding the heading and speed have been found to converge on a highly conserved region of the insect brain, the central complex. It is, therefore, believed that the central complex is key to the computations pertaining to path integration. However, several questions remain about the exact structure of the neuronal circuit that tracks the animal's heading, how it differs between insect species, and how the speed and direction are integrated into a home vector and maintained in memory. In this thesis, I have combined behavioural, anatomical, and physiological data with computational modelling and agent simulations to tackle these questions. Analysis of the internal compass circuit of two insect species with highly divergent ecologies, the fruit fly Drosophila melanogaster and the desert locust Schistocerca gregaria, revealed that despite 400 million years of evolutionary divergence, both species share a fundamentally common internal compass circuit that keeps track of the animal's heading. However, subtle differences in the neuronal morphologies result in distinct circuit dynamics adapted to the ecology of each species, thereby providing insights into how neural circuits evolved to accommodate species-specific behaviours. The fast-moving insects need to update their home vector memory continuously as they move, yet they can remember it for several hours. This conjunction of fast updating and long persistence of the home vector does not directly map to current short, mid, and long-term memory accounts. An extensive literature review revealed a lack of available memory models that could support the home vector memory requirements. A comparison of existing behavioural data with the homing behaviour of simulated robot agents illustrated that the prevalent hypothesis, which posits that the neural substrate of the path integration memory is a bump attractor network, is contradicted by behavioural evidence. An investigation of the type of memory utilised during path integration revealed that cold-induced anaesthesia disrupts the ability of ants to return to their nest, but it does not eliminate their ability to move in the correct homing direction. Using computational modelling and simulated agents, I argue that the best explanation for this phenomenon is not two separate memories differently affected by temperature but a shared memory that encodes both the direction and distance. The results presented in this thesis shed some more light on the labyrinth that researchers of animal navigation have been exploring in their attempts to unravel a few more rounds of Ariadne's thread back to its origin. The findings provide valuable insights into the path integration system of insects and inspiration for future memory research, advancing path integration techniques in robotics, and developing novel neuromorphic solutions to computational problems

Cerebellar Codings for Control of Compensatory Eye Movements

This thesis focuses on the control of the cerebellum on motor behaviour, and more specifically on the role of the cerebellar Purkinje cells in exerting this control. As the cerebellum is an online control system, we look at both motor performance and learning, trying to identify components involved at the molecular, cellular and network level. To study the cerebellum we used the vestibulocerebellum, with visual and vestibular stimulation as input and eye movements as recorded output. The advantage of the vestibulocerebellum over other parts is that the input given is highly controllable, while the output can be reliably measured, and performance and learning can be easily studied. In addition, we conducted electrophysiological recordings from the vestibulocerebellum, in particular of Purkinje cells in the flocculus. Combining the spiking behaviour of Purkinje cells with visual input and eye movement output allowed us to study how the cerebellum functions and using genetically modified animals we could determine the role of different elements in this system. To provide some insights in the techniques used and the theory behind them, we will discuss the following topics in this introduction: compensatory eye movements, the anatomy of pathways to, within and out of the flocculus, the cellular physiology of Purkinje cells in relation to performance and the plasticity mechanisms related to motor learning

Structural and functional integrity of neural circuits

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, September 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections."September, 2011." Cataloged from student submitted PDF version of thesis.Includes bibliographical references.This dissertation documents how healthy aging and Parkinson's disease (PD) affect brain anatomy and physiology and how these neural changes relate to measures of cognition and perception. While healthy aging and PD are both accompanied by a wide-range of cognitive impairments, the neural underpinnings of cognitive decline in each is likely mediated by deterioration of different systems. The four chapters of this dissertation address specific aspects of how healthy aging and PD affect the neural circuits that support sensory processes and high-level cognition. The experiments in Chapters 2 and 3 examine the effects of healthy aging on the integrity of neural circuits that modulate cognitive control processes. In Chapter 2, we test the hypothesis that the patterns of age-related change differ between white matter and gray matter regions, and that changes in the integrity of anterior regions correlate most strongly with performance on cognitive control tasks. In Chapter 3, we build upon the structural findings by examining the hypothesis that age-related changes in white matter integrity are associated with disrupted oscillatory dynamics observed during a visual search task. Chapter 4 investigates healthy age-related changes in somatosensory mu rhythms and evoked responses and uses a computational model of primary somatosensory cortex to predict the underlying cellular and neurophysiolgical bases of these alterations. In contrast to the widespread cortical changes seen in healthy OA, the cardinal motor symptoms of PD are largely explained by degeneration of the dopaminergic substantia nigra, pars compacta (SNc). Cognitive sequelae of PD, however, likely result from disruptions in multiple neurotransmitter systems, including nondopaminergic nuclei, but research on these aspects of the disease has been hindered by a lack of sensitive MRI biomarkers for the affected structures. Chapter 5 presents new multispectral MRI tools that visualize the SNc and the cholinergic basal forebrain (BF). We applied these methods to test the hypothesis that degenerative processes in PD affect the SNc before the BF. This experiment lays important groundwork for future studies that will examine the relative contribution of the SNc and BF to cognitive impairments in PD.by David A. Ziegler.Ph.D

Computational studies of glutamate transporters and receptors

In this thesis we use molecular dynamics simulations to study glutamate transporters and receptors, as well as toxin binding to human potassium channels. We apply a variety of techniques in computational biology such as ligand docking, parameterization of molecules, homology modeling, free energy perturbation and potential of mean force. We first study the archaeal aspartate transporter GltPh, which is a homolog of the human glutamate transporters (EAATs). We locate the third sodium ion binding site and show that this site is also conserved in the EAATs. We then perform molecular dynamics simulations in the outward and inward facing states of GltPh, calculating the ligand affinities in both conformations, the order of ligand binding/unbinding, and the differences in the gating mechanism between the inward and outward states. Using the GltPh crystal structures, we build models for the human glutamate transporters. We use our models to locate the potassium and proton binding sites in EAATs, investigate the gating mechanism, and elucidate the mechanism of proton transport. We also calculate the standard binding free energy of five small molecules to the ligand binding domain of the GluA2 receptor, using a free energy perturbation approach that produces results with very good agreement to experimental values. We show that this method is effective for polar and charged ligands, and can be applied to many biological systems, providing an useful tool in computer aided drug design. Finally, we develop a method based on free energy perturbation that can be used to calculate the binding free energy differences of peptide toxin mutants to Kv potassium channels, producing very good agreement to potential of mean force and experimental results. We use it to obtain a ShK mutant that is selective for the Kv1.1 channel over Kv1.3, and therefore may be used in the treatment of auto-immune diseases

Computational studies of glutamate transporters and receptors

In this thesis we use molecular dynamics simulations to study glutamate transporters and receptors, as well as toxin binding to human potassium channels. We apply a variety of techniques in computational biology such as ligand docking, parameterization of molecules, homology modeling, free energy perturbation and potential of mean force. We first study the archaeal aspartate transporter GltPh, which is a homolog of the human glutamate transporters (EAATs). We locate the third sodium ion binding site and show that this site is also conserved in the EAATs. We then perform molecular dynamics simulations in the outward and inward facing states of GltPh, calculating the ligand affinities in both conformations, the order of ligand binding/unbinding, and the differences in the gating mechanism between the inward and outward states. Using the GltPh crystal structures, we build models for the human glutamate transporters. We use our models to locate the potassium and proton binding sites in EAATs, investigate the gating mechanism, and elucidate the mechanism of proton transport. We also calculate the standard binding free energy of five small molecules to the ligand binding domain of the GluA2 receptor, using a free energy perturbation approach that produces results with very good agreement to experimental values. We show that this method is effective for polar and charged ligands, and can be applied to many biological systems, providing an useful tool in computer aided drug design. Finally, we develop a method based on free energy perturbation that can be used to calculate the binding free energy differences of peptide toxin mutants to Kv potassium channels, producing very good agreement to potential of mean force and experimental results. We use it to obtain a ShK mutant that is selective for the Kv1.1 channel over Kv1.3, and therefore may be used in the treatment of auto-immune diseases

DESIGN, SYNTHESIS, AND PHARMACOLOGICAL EVALUATION OF A SERIES OF NOVEL, GUANIDINE AND AMIDINE-CONTAINING NEONICOTINOID-LIKE ANALOGS OF NICOTINE: SUBTYPE-SELECTIVE INTERACTIONS AT NEURONAL NICOTINIC-ACETYLCHOLINE RECEPTOR.

The current project examined the ability of a novel series of guandine and amidine-containing nicotine analogs to interact with several native and recombinantlyexpressed mammalian neuronal nicotinic-acetylcholine receptor (nAChR) subtypes. Rational drug design methods and parallel organic synthesis was used to generate a library of guanidine-containing nicotine (NIC) analogs (AH compounds). A smaller series of amidine-containing nicotine analogs (JC compounds) were also synthesized. In total, \u3e150 compounds were examined. Compounds were first assayed for affinity in a high-throughput [3H]epibatidine radioligand-binding screen. Lead compounds were evaluated in subtype-selective binding experiments to probe for affinity at the α4β2* and α7* neuronal nAChRs. Several compounds were identified which possess affinity and selectivity for the α4β2* subtype [AH-132 (Ki=27nm) and JC-3-9 (Ki=11nM)]. Schild analysis of binding suggests a complex one-site binding interaction at the desensitized high-affinity nAChR. Whole-cell functional fluorescence (FLIPR) assays revealed mixed subtype pharmacology. AH-compounds were identified which act as activators and inhibitors at nAChR subtypes, while lead JC-compounds were found which possess full agonist activity at α4β2* and α3β4* subtypes. Compounds were identified as partial agonists, full agonists and inhibitors of multiple nAChR subtypes. Several SAR-based, ligand-receptor pharmacophore models were developed to guide future ligand design. Second-generation lead compounds were identified