Scalable Digital Architecture of a Liquid State Machine

Liquid State Machine (LSM) is an adaptive neural computational model with rich dynamics to process spatio-temporal inputs. These machines are extremely fast in learning because the goal-oriented training is moved to the output layer, unlike conventional recurrent neural networks. The capability to multiplex at the output layer for multiple tasks makes LSM a powerful intelligent engine. These properties are desirable in several machine learning applications such as speech recognition, anomaly detection, user identification etc. Scalable hardware architectures for spatio-temporal signal processing algorithms like LSMs are energy efficient compared to the software implementations. These designs can also naturally adapt to dierent temporal streams of inputs. Early literature shows few behavioral models of LSM. However, they cannot process real time data either due to their hardware complexity or xed design approach. In this thesis, a scalable digital architecture of an LSM is proposed. A key feature of the architecture is a digital liquid that exploits spatial locality and is capable of processing real time data. The quality of the proposed LSM is analyzed using kernel quality, separation property of the liquid and Lyapunov exponent. When realized using TSMC 65nm technology node, the total power dissipation of the liquid layer, with 60 neurons, is 55.7 mW with an area requirement of 2 mm^2. The proposed model is validated for two benchmark. In the case of an epileptic seizure detection an average accuracy of 84% is observed. For user identification/authentication using gait an average accuracy of 98.65% is achieved

Computational physics of the mind

In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures

Kontinuierliche EEG Überwachung für die Therapie von Hunden und Katzen im Status epilepticus

Goal of this case series was to describe the management of Status epilepticus in dogs and cats with the aid of continuous electroencephalographic (EEG) monitoring. Ten patients (7 dogs, 3 cats) with SE of differing etiology (idiopathic epilepsy, n=3; toxicity, n=4; meningoencephalitis, n=2; undefined, n=1) were included in the study. The EEG was recorded continuously from five stainless steel needle-electrodes inserted subcutaneously. Animals were treated with diazepam and phenobarbital followed by either propofol (n=3) or pentobarbital (n=7) at a continuous rate of infusion. Clinical seizures stopped after induction of anesthesia in each animal. The EEG, however, still showed distinct epileptiform patterns (spikes, polyspikes) in all animals. Paroxysms were suppressed by increasing the infusion rate of either pentobarbital or propofol. A burst-suppression pattern was achieved in 5 animals. Electroencephalographic epileptiform activity reappeared in 4 animals when attempting to taper the dose after > 6 hours of anesthesia. This was interpreted as ongoing EEG seizure activity and an increased risk for clinical seizures, and the anesthetic dosage was adjusted accordingly. We conclude that continuous EEG monitoring appears to be a useful tool for therapeutic monitoring of SE in dogs and cats. It allows the detection of EEG seizures without the appearance of clinical seizures. Further investigations with blinded investigators and homogeneous animal groups to define therapeutic endpoints are warranted.Ziel der vorliegenden Arbeit ist die Beschreibung des Managements und der Therapie eines Status epilepticus mit Hilfe kontinuierlicher EEG-Ableitungen. Es wurden zehn Patienten (sieben Hunde, drei Katzen) im Status epilepticus untersucht. Ursache des Status waren unterschiedlich (idiopathische Epilepsie, n=3; Vergiftung, n=4; Meningoencephalitis, n=2; unbekannter Genese, n=1). Das EEG wurde kontinuierlich mittels fünf rostfreier subkutan angebrachter Stahlelektroden abgeleitet. Alle Tiere wurden initial mit Diazepam und Phenobarbital behandelt und wegen nicht unterdrückbarer Anfallsaktivität entweder mit Propofol (n=3) oder Pentobarbital (n=7) als kontinuierliche intravenöse Infusion in Narkose gehalten. Die klinischen Anfälle stoppten nach der Induktion der Narkose bei jedem Tier. Das EEG zeigte jedoch immer noch vereinzelt epilepsietypische Aktivität (spikes, polyspikes) bei jedem Tier. Die Paroxysmen verschwanden, als die Infusionsrate von Pentobarbital oder Propofol erhöht wurde. Bei 5 Tieren wurde ein Burst Suppression Pattern (BSP) erreicht. Als versucht wurde, die Infusionsrate nach mehr als 6 Stunden zu reduzieren, trat erneut epileptische Aktivität im EEG auf. Dies wurde als anhaltende EEG-Anfälle mit erhöhtem Risiko für die Entwicklung zu klinischen Anfällen interpretiert. Die Überwachung von Patienten im Status epilepticus mittels EEG erscheint sinnvoll, um den Effekt der Therapie zu kontrollieren. Das EEG gibt die Möglichkeit, epileptische Aktivität des Gehirns frühzeitig zu erkennen, ohne dass klinische Anfälle auftreten. Weitere Studien mit geblindeten Untersuchern, homogener Patientengruppe und mit Fokus auf Einflussparameter auf das EEG sind notwendig, um die Untersuchung zu einer Routineuntersuchung zu machen

Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems