Abstract Brain function relies on circuits of spiking neurons with synapses playing the key role merging transmission memory storage and processing. Electronics has made important advances to emulate brain-computer interfacing concepts that interlink brain brain-inspired devices are beginning materialise. We report memristive links between silicon plasticity properties real synapses. A memristor paired a metal-thin film titanium oxide microelectrode connects neuron rat hippocampus....

With the advent of high-density micro-electrodes arrays, developing neural probes satisfying real-time and stringent power-efficiency requirements becomes more challenging. A smart probe is an essential device in future neuroscientific research medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip data processing training for signal analysis. It consists digitally-assisted 16-channel analog front-end 1.52 μW/Ch, dedicated bio-processing accelerators...

In the field of brain-machine interfaces, biohybrids offer an interesting new perspective, as in them, technological side acts like a closed-loop extension or real counterpart biological tissue, instead usual open loop approaches tranditional BMI. To achieve credible to usually employ one several neuromorphic components hardware half biohybrid. However, advanced circuit such memristor crossbars operate best dedicated lab with corresponding support equipment. The same is true for which makes...

Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-time monitoring patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that emotion would facilitate patients' social integration and lead to improved healthcare outcomes for them their loved ones. Yet spite direct relevance neuro-medicine, hardware platforms classification...

Despite an abundance of computational models for learning synaptic weights, there has been relatively little research on structural plasticity, i.e. the creation and elimination synapses. Especially, it is not clear how plasticity works in concert with spike-timing-dependent (STDP) what advantages their combination offers. Here we present a fairly large-scale functional model that uses leaky integrate-and-fire neurons, STDP, homeostasis, recurrent connections, to learn input encoding,...

The goal in neuromorphic engineering is to design circuits and systems which emulate the computational principles of biological nervous systems. As these follow same fundamental as their counterparts, they represent an elegant solution for bio-hybrid computing architectures. We present a system electronic are coupled directly neuronal cell cultures providing low-level access signal processing. To form this bio-hybrid, we introduce backbone allows implements different network topologies by...

Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics.

In neural implants and biohybrid research systems, the integration of electrode recording stimulation front-ends with pre-processing circuitry promises a drastic increase in real-time capabilities [1,6]. our proposed system, constant sampling bandwidth 9.8kHz yields $6.73 \mu \text{V}$ input-referred noise (IRN) at power-per-channel $0.34 \text{W}$ for time-continuous $\Delta \Sigma -$modulator, $0.52 digital filters spike detectors. We introduce dynamic current/bandwidth selection \Sigma$...

Research in neuroscience suggests that networks of biological neurons undergo a constant reconfiguration their topology via activity-dependent plasticity mechanisms. The observed growing and retracting dendritic spines can be hypothesized to resource-optimizing strategy limits the amount energy spent on maintaining large number synapses are not contributing performance. Neuromorphic analog VLSI emulates biophysical processes neural tissue using CMOS transistors operated sub-threshold regime,...

Structural Plasticity describes a form of long-term plasticity, in which the pruning and creation synapses lead to formation memories topology network neurons. In contrast, classical learning rules such as spike-timing dependent plasticity (STDP) focus on changing efficacy synapses, for example by looking at correlation pre-and post-synaptic activity spiking neural networks. Typically, prolonged correlated leads potentiation synaptic weight, while anti-correlated depresses weight. We...

Stochastic computing has shown promising results for low-power area-efficient hardware implementations of neural networks. In particular, probabilistic methods are being actively explored in models spiking processing systems enabling noisy and low-precision neuromorphic architectures to implement state-of-the-art recognition inference systems. It is therefore important develop suitable sources stochastic behavior these that will allow them maintain their compact benefits. Here we present a...

This brief presents a scalable electrophysiology front-end designed in Globalfoundries 22 nm FDSOI. A very low footprint of 0.018 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mm^{2}$ </tex-math></inline-formula> per channel is achieved. Robust and yet power- area-efficient digital circuits replace Process-, Supply Voltage- Temperature-(PVT) sensitive components like high- low-pass filters. Low-noise...

Throughout evolution the brain has mastered art of processing real-world inputs through networks interlinked spiking neurons. Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with memory storage and computation. Electronics made important steps in emulating neurons neuromorphic circuits synapses nanoscale memristors, yet novel applications that interlink them heterogeneous bio-inspired bio-hybrid architectures just beginning...

Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics. We present a novel solution that leverages the high integration density 22nm FDSOI CMOS technology to address these challenges. The proposed highly integrated programmable System-on-Chip comprises 68-channel 0.41 \textmu...

Abstract Monitoring electrical activity across multiple planes in 3D cell cultures and organoids is imperative to comprehensively understand their functional connections behavior. However, traditional planar microelectrode arrays (MEAs) are intended for surface recordings inadequate addressing this aspect. The limitations, such as longer production times limited adaptability imposed by standard clean‐room techniques, constrain the design possibilities electrode potentially hinder effective...

This letter presents a scalable technique to reduce the power of analog input stage in neural recording front-ends Globalfoundries 22 -nm FDSOI. The back-gate voltages are adapted threshold voltage and thus allow supply reduction. adaption increases PVT stability subthreshold circuits. A comparison conventional delta–sigma ADC is drawn minimum point measured, resulting 0.7 - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math...

The real-time multi-channel intracranial recording of neural signals is required in both neuroscientific research and clinical practice. Due to the limited power budget increasing number channels, a compression engine highly recommended. This paper proposes 16-channel adaptive (ACE) exploiting signal properties. It able switch between lossless near-lossless modes. In mode, it compresses spike region discards rest. achieved space-saving ratio (SSR) on average about 62.5% 91% for modes,...

The increase of recording channels in modern electrode-based neural systems is limited by considerations power. Emerging CMOS technologies like 22 nm FDSOI promise to open new perspectives overcoming power constraints due their particular energy efficiency digital circuit integration, they however also pose a challenge for the analogue front-end stages such systems, especially with respect signal noise. This paper addresses design low noise amplifiers (LNA), most critical component and...

Neurostimulation and extensive signal processing are requirements for closed-loop neural interfaces. At the same time, issues of thermal biocompatibility limit power budget in such applications, driving trend towards small technologies like 22 nm FDSOI. While overcoming aforementioned constraints, this complicates design current stimulators with up to 3. 3V supply voltage technology. Stacking 1.8 V compliant IO-transistors, is part solution. In work we address challenge, showcasing a...

This paper addresses efficient DC-DC converters for neural recording and processing implants in 22 nm FDSOI from a 3.7 V battery supply. Default solution with several supplies is impracticable w.r.t. complex cabling, PCB design larger form factor. Thus we present which robust regarding input voltage range flexible output range. A bandgap-derived 0.5 to 0.8 selectable. The supply monitored as an additional safety feature bio-medical applications. In combination system FDSOI, feasibility of...