A Reconfigurable Neural Recording FrontEnd IC for Multimodal Operation This paper presents a reconfigurable neural recording frontend integrated circuit (IC) for multimodal operation. The proposed IC performs the voltage sensing to acquire the electrical neural signal and detects the photodiode current to monitor the concentration of calcium ions in the neuronal cell population. The frontend IC is composed of the analog frontend, voltagetocurrent converters, path switches, and currenttodigital converters for recording voltage and current signals. When monitoring the electrical neural activity, the IC is configured to operate in the voltage sensing mode. When reading the photodiode current, the IC operates in the current sensing mode. The proposed frontend IC is designed using a 180nm CMOS process. The neural recording IC consumes the power of 5.4 xc2xb5Wchannel and 2 xc2xb5Wchannel in the voltage and current sensing modes, respectively. The active area occupied by the singlechannel circuit is 0.23 mm2.

MultiChannel Neural Recording Implants: A Review The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brainxe2x80x93machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multichannel neural recording implants are surveyed. After presenting various neuralsignal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.