Synthesis of neuromorphic circuits with neuromodulatory properties

The field of neuromorphic engineering shows great promise in delivering novel devices inspired by biological principles that would undertake sensory and processing tasks with an unprecedented level of efficiency. In order to achieve that, engineers are required to understand and implement the many complex biological regulatory mechanisms that allow the nervous system to robustly operate and adapt over scales covering many orders of magnitude, while at the same time using unreliable and noisy components. As a step towards that, this thesis aims at discussing and implementing the principles of neuromodulation in neuromorphic hardware, mechanisms which allow neurons to change and regulate their behaviour through the continuous control of their internal currents. We discuss how neural dynamics and its modulation can be broken down into four essential feedback loops, and we introduce a simplified model of the neural membrane respecting this fundamental structure. We present a novel methodology for controlling the neuron's behaviour through the shaping of its I-V curves in distinct timescales, thus characterising the behaviour of the neural circuit through its input-output properties. We show how modulation of the feedback loops affects the behaviour, and importantly, captures the transition between spiking and bursting oscillatory regimes, two major signalling modes of neurons. We then show how the architecture can be easily implemented using well-known neuromorphic building blocks based on subthreshold MOSFET circuits. Finally, we discuss how the excitability switch captured by the model can be exploited in simple network settings, thus opening up the possibility for future research into novel architectures where the control of cellular properties is utilised to shape the global behaviour of the network

Modulation of Synaptic Amplification in Sympathetic Ganglia

The purpose of this dissertation was to assess synaptic integration in neurons from the rat superior cervical ganglion (SCG) using complex temporal patterns of virtual synaptic activity that mimic in vivo conditions. The SCG is a paravertebral ganglion that innervates different targets in the head. One of its important roles is to regulate vascular tone. Previous reports have concluded that SCG neurons behave as simple relays between preganglionic synaptic activity from the spinal cord and postganglionic control of end organs. We have tested the hypotheses that (1) postganglionic convergence of strong and weak nicotinic synapses produces variable synaptic amplification in SCG neurons; (2) entrainment of preganglionic activity to the cardiac cycle through arterial baroreceptors increases synaptic gain; (3) the contribution of weak nicotinic synapses to postganglionic integration has been underestimated in vivo due to membrane damage caused by sharp microelectrodes; and (4) angiotensin II (AngII) acts postsynaptically to increase ganglionic synaptic amplification. The approach to creating virtual synapses relied on dynamic clamp. Using whole-cell recordings of SCG neurons in short-term cultures, we found evidence for activity dependent synaptic gain and for the enhancement of gain by cardiac entrainment. Based on this approach, a computational model was developed to simulate human data – this showed that the statistics of human firing patterns could be accounted for by a model that includes secondary synapses and synaptic amplification. Cellular damage was simulated with dynamic clamp by implementing a non-depolarizing shunt conductance. This revealed that damage introduced by microelectrode recordings transformed the intrinsic firing properties of sympathetic neurons and obscured the contribution of weak nicotinic synapses to synaptic gain. Finally, G-protein coupled receptors for AngII increased postganglionic excitability, which facilitated the integration of weak synaptic activity and enhanced synaptic gain. These results have implications for understanding human blood pressure regulation during exercise and hypertension. Until now, the SCG had been discounted as a regulator of blood pressure. Data in this thesis supports an integrative role for synaptic convergence in sympathetic ganglia and the modulation of gain by AngII. These results suggest that future efforts to control blood pressure and treat hypertension could target ganglionic mechanisms

Etude expérimentale de neurones de Morris-Lecar : réalisation, couplage et interprétation

We present in this manuscript an experimental electronic neuron based on complete Morris-Lecar model without simplifications, able to become an experimental unit tool to study collective association of robust coupled neurons. The circuit design is given in details according to the ionic currents of this model. The experimental results are compared with the theoretical prediction, leading to a good agreement between them, which therefore validates the circuit. We present the different areas according to the bifurcation control parameters, the membrane capacitance and the excitation current. We have highlighted the behavior of the neuron for each parameters zone. A coupling of such neurons is introduced by using Pspice simulations (Multisim) where neurons have been designed to be the same as the experimental one. First, we simulate a chain of up to 26 neurons weakly coupled along which anti-phase wave patterns propagate with phases in opposition 2 to 2. Second, about ten neurons are coupled, and we succeed to generate clusters based on the boundary conditions of theneurons and their initial conditions. For this study, we work in the region close to the fold bifurcation of limit cycles, where two limit cycles exist, one being stable and another one unstable. Our studies show that the system evolves to a state where only 1, 2 or 3 neurons remain in the oscillatory state, while others returned to a state of rest, which highlights a phenomenon of clustering. The use of some parts of the circuit is also possible for other neuron models, namely for those based on ionic currents.Nous présentons dans ce manuscrit un neurone électronique expérimental basé sur le modèle complet de Morris-Lecar sans simplifications, afin d’obtenir une cellule de base pour étudier l’association collective de neurones couplés. La conception du circuit est donnée en détail selon les différents courants ioniques du modèle. Les résultats expérimentaux sont comparés aux prédictions théoriques, conduisant à un bon accord, ce qui valide donc notre circuit. Nous présentons les différents domaines de bifurcation selon les paramètres de contrôle, la capacité membranaire et le courant d’excitation. Nous avons mis en évidence le comportement du neurone pour chaque zone de paramétrage. Un couplage de ces neurones est introduit en utilisant des simulations Pspice (Multisim) où les neurones ont été conçus pour être les mêmes qu’expérimentalement. Premièrement, nous avons simulé une chaîne fermée de 26 neurones faiblement couplés le long de laquelle les ondes se propagent avec des phases en opposition 2 à 2. Pour cette première étude, on travaille dans une zone présentant uniquement un cycle limite stable. Deuxièmement, une dizaine de neurones sont couplés, avec un choix de paramètres correspondant à une deuxième zone où il y a deux attracteurs, un cycle limite stable et un point fixe stable, tandis qu’entre eux se trouve un cycle instable. Selon le nombre de neurones qui oscillent initialement et les conditions aux bords, nos études montrent que le système évolue vers un état où seuls 1, 2 ou 3 neurones restent à l’état oscillatoire, tandis que les autres sont retournés à un état de repos, ce qui met en évidence un phénomène de clusterisation. Il est à noter que certaines parties de notre circuit de base peuvent ainsi être utilisées dans d’autres modèles de neurones, car ces parties correspondent à la production des divers courants ioniques qu’on retrouve dans d’autres modèles

Exploring the potential of brain-inspired computing

The gap between brains and computers regarding both their cognitive capability and power efficiency is remarkably huge. Brains process information massively in parallel and its constituents are intrinsically self-organizing, while in digital computers the execution of instructions is deterministic and rather serial. The recent progress in the development of dedicated hardware systems implementing physical models of neurons and synapses enables to efficiently emulate spiking neural networks. In this work, we verify the design and explore the potential for brain-inspired computing of such an analog neuromorphic system, called Spikey. We demonstrate the versatility of this highly configurable substrate by the implementation of a rich repertoire of network models, including models for signal propagation and enhancement, general purpose classifiers, cortical models and decorrelating feedback systems. Network emulations on Spikey are highly accelerated and consume less than 1 nJ per synaptic transmission. The Spikey system, hence, outperforms modern desktop computers in terms of fast and efficient network simulations closing the gap to brains. During this thesis the stability, performance and user-friendliness of the Spikey system was improved integrating it into the neuroscientific tool chain and making it available for the community. The implementation of networks suitable to solve everyday tasks, like object or speech recognition, qualifies this technology to be an alternative to conventional computers. Considering the compactness, computational capability and power efficiency, neuromorphic systems may qualify as a valuable complement to classical computation