Calibration-free and hardware-efficient neural spike detection for brain machine interfaces

Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints – the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this paper, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18MU m CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm2 silicon area and consumes 4.86MU W from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training

The Affine Uncertainty Principle, Associated Frames and Applications in Signal Processing

Uncertainty relations play a prominent role in signal processing, stating that a signal can not be simultaneously concentrated in the two related domains of the corresponding phase space. In particular, a new uncertainty principle for the affine group, which is directly related to the wavelet transform has lead to a new minimizing waveform. In this thesis, a frame construction is proposed which leads to approximately tight frames based on this minimizing waveform. Frame properties such as the diagonality of the frame operator as well as lower and upper frame bounds are analyzed. Additionally, three applications of such frame constructions are introduced: inpainting of missing audio data, detection of neuronal spikes in extracellular recorded data and peak detection in MALDI imaging data

Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces

Intracortical Brain-Machine Interfaces (iBMIs) have advanced significantly over the past two decades, demonstrating their utility in various aspects, including neuroprosthetic control and communication. To increase the information transfer rate and improve the devices’ robustness and longevity, iBMI technology aims to increase channel counts to access more neural data while reducing invasiveness through miniaturisation and avoiding percutaneous connectors (wired implants). However, as the number of channels increases, the raw data bandwidth required for wireless transmission also increases becoming prohibitive, requiring efficient on-implant processing to reduce the amount of data through data compression or feature extraction. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30% with minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30\% with only minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget.Open Acces

Impact of the pulvinar on the ventral pathway of the cat visual cortex

Signals from the retina are relayed to the lateral geniculate nucleus from which they are sent to the primary visual cortex. At the cortical level, the information is transferred across several visual areas in which the complexity of the processing increases progressively. Anatomical and functional evidence demonstrate the existence of two main pathways in visual cortex processing distinct features of the visual information: the dorsal and ventral streams. Cortical areas composing the dorsal stream are implicated mostly in motion processing while those comprising the ventral stream are involved in the processing of form and colour. This classic view of the cortical functional organization is challenged by the existence of reciprocal connections of visual cortical areas with the thalamic nucleus named pulvinar. These connections allow the creation of a trans-thalamic pathway that parallels the cortico-cortical communications across the visual hierarchy. The main goal of the present thesis is twofold: first, to obtain a better comprehension of the processing of light increments and decrements in an area of the cat ventral stream (area 21a); second, to characterize the nature of the thalamo-cortical inputs from the cat lateral posterior nucleus (LP) to area 21a. In study #1, we investigated the spatiotemporal response profile of neurons from area 21a to light increments (brights) and decrements (darks) using a reverse correlation analysis of a sparse noise stimulus. Our findings showed that 21a neurons exhibited stronger responses to darks with receptive fields exhibiting larger dark subfields. However, no differences were found between the temporal dynamics of brights and darks. In comparison with the primary visual cortex, the dark preference in area 21a was found to be strongly enhanced, supporting the notion that the asymmetries between brights and darks are transmitted and amplified along the ventral stream. In study #2, we investigated the impact of the reversible pharmacological inactivation of the LP nucleus on the contrast response function (CRF) of neurons from area 21a and the primary visual cortex (area 17). The thalamic inactivation yielded distinct effects on both cortical areas. While in area 17 the LP inactivation caused a slight decrease in the response gain, in area 21a a strong increase was observed. Thus, our findings suggest that the LP exerts a modulatory influence on the cortical processing along the ventral stream with stronger impact on higher order extrastriate areas. Taken together, our findings allowed a better comprehension of the functional properties of the cat ventral stream and contributed to the current knowledge on the role of the pulvinar on the cortico-thalamo-cortical processing of visual information.Les signaux provenant de la rétine sont relayés dans le corps géniculé latéral où ils sont envoyés au cortex visuel primaire. L’information passe ensuite à travers plusieurs aires visuelles où la complexité du traitement augmente progressivement. Des données tant anatomiques que fonctionnelles ont démontré l’existence de deux voies principales qui traitent différentes propriétés de l’information visuelle : les voies dorsale et ventrale. Les aires corticales composant la voie dorsale sont impliquées principalement dans le traitement du mouvement tandis que les aires de la voie ventrale sont impliquées dans le traitement de la forme et de la couleur. Cette vision classique de l’organisation fonctionnelle du cortex est toutefois remise en question par l’existence de connections réciproques entre les aires corticales visuelles et le pulvinar, un noyau thalamique. En effet, ces connections permettent la création d’une voie trans-thalamique parallèle aux connections cortico-corticales à travers la hiérarchie visuelle. Le but principal de la présente thèse consiste en deux volets : le premier est d’obtenir une meilleure compréhension du traitement des incréments et décréments de la lumière dans une aire de la voie ventrale du chat (aire 21a); le second est de caractériser la nature des inputs thalamo-corticaux du noyau latéral postérieur (LP) à l’aire 21a chez le chat. Dans l’étude #1, nous avons investigué le profil spatiotemporel des réponses des neurones de l’aire 21a aux incréments (blancs) et décréments (noirs) de lumière en utilisant l’analyse de corrélation inverse d’un stimulus de bruit épars. Les neurones de l’aire 21a ont répondu plus fortement aux stimuli noirs, en montrant des champs récepteurs avec des sous-champs noirs plus larges. Cependant, aucune différence n’a été trouvée en ce qui concerne les dynamiques temporelles des réponses aux blancs et aux noirs. En comparaison avec le cortex visuel primaire, la préférence aux stimuli noirs dans l’aire 21a s’est avérée fortement augmentée. Ces données indiquent que les asymétries entre les réponses aux blancs et aux noirs sont transmises et amplifiées à travers la voie ventrale. Dans l’étude #2, nous avons investigué l’impact de l’inactivation pharmacologique réversible du noyau LP sur la fonction de réponse au contraste (CRF) des neurones de l’aire 21a et du cortex visuel primaire (aire 17). L’inactivation a eu différents effets dans les deux aires corticales. Alors que, dans l’aire 17, l’inactivation du LP a causé une légère réduction du gain de la réponse, une forte augmentation a été observée dans l’aire 21a. Ainsi, nos résultats suggèrent que le LP exerce une influence modulatrice dans le traitement cortical à travers la voie ventrale avec un impact plus important dans des aires extrastriées de plus haut niveau. Nos résultats ont permis d’avoir une meilleure compréhension des propriétés fonctionnelles de la voie ventrale du chat et de contribuer à enrichir les connaissances actuelles sur le rôle du pulvinar dans le traitement cortico-thalamo-cortical de l’information visuelle

Brain-Inspired Computing

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures