The power of connectivity: identity preserving transformations on visual streams in the spike domain

A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors

Bio-signal sensing is pivotal in medical bioelectronics. Traditional methods focus on high sampling rates, leading to large amounts of irrelevant data and high energy consumption. We introduce a self-clocked microelectrode array (MEA) that digitizes bio-signals at the pixel level by encoding changes as asynchronous digital address-events only when they exceed a threshold, significantly reducing off-chip data transmission. This novel MEA comprises a 64 × 64 electrode array, an asynchronous 2D-arbiter, and an Address-Event Representation (AER) communication block. Each pixel operates autonomously, monitoring voltage fluctuations from cellular activity and producing digital pulses for significant changes. Positive and negative signal changes are encoded as "up" and "down" events and are routed off-chip via the asynchronous arbiter. We present results from chip characterization and experimental measurements using electrogenic cells. Additionally, we interface the MEA to a mixed-signal neuromorphic processor, demonstrating a prototype for end-to-end event-based bio-signal sensing and processing.

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

A Motion Detection Algorithm Using Local Phase Information

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm.