Multimodal Neural Interface Circuits for Diverse Interaction With Neuronal Cell Population in Human Brain

Kim

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

et al. 2021

Self Cite

This paper presents a multimodal neural recording system incorporating reconfigurable neural recording front-ends to improve hardware availability. The proposed neural recording front-end utilizing the reconfigurable signal conveyor based on the folded-currentmirror structure is configured to process either voltage or current input signal, depending on the targeted experiment scenario, given experiment setup, and varying status of the recording environment. Thus, out of the total number of recording channels, how many channels are used for voltage and current recording can be set as desired, which maximizes the flexibility of the experiment and the usefulness of the recording system. The implemented system is successfully applied to the in vivo recording experiment conducted in the hippocampus area of a mouse brain. The neural recording front-end achieves input-referred noise performances of 2.4 μVrms and 5.19 pArms in the voltage and current recording modes, respectively. The power consumed by each recording channel is 5.718 μW, and only 54 nW is used to operate the reconfigurable signal conveyor.

Section: Reconfigurable Multimodal Neural Recording Ic a Application Scenariomentioning

confidence: 99%

“…Therefore, to conduct systematic neuroscience research, the current recording to measure Ca 2+ and neurotransmitters should be performed in conjunction with the voltage recording to monitor APs and LFPs, as shown in Fig. 1(top) [2].…”

Section: Introductionmentioning

confidence: 99%

A 5.7µW/Channel Folded-Current-Mirror-Based Reconfigurable Multimodal Neural Recording IC with Improved Hardware Availability

Kim

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

et al. 2021

Self Cite

Computational Intelligence and Neuroscience

“…The reconfigurable robot systems that have been developed are generally divided into two categories: static reconfigurable robots and self-reconfigurable robots [ 4 ]. The static reconfigurable robot completes the configuration change through manual assistance, while the self-reconfigurable robot automatically changes the connection relationship of each module according to the changes of the working environment and tasks, so as to realize the configuration reconfiguration [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Design of Multimodal Neural Network Control System for Mechanically Driven Reconfigurable Robot

Zhang

Gongyong

2022

According to the characteristics and division rules of the modules, this paper divides the rotary module, the swing module, and the mobile module. In order to realize the rapid identification of modules, according to the basic principles of module design, the above modules are put into the module library established by Access. According to the modular modeling method, kinematic models are established, respectively. In order to automatically establish the kinematic model of the robot, a unified expression of modules is established. According to the unified expression of the modules, the kinematics of the reconfigurable robot is analyzed. According to the characteristics of the configuration plane, the configuration plane is divided, and the expression form of the position and attitude of the configuration plane is given. Combined with the principle of neural network and multimodal information fusion, a multimodal information fusion model based on long- and short-term memory neural network is established. Aiming at the control problem of mechanically driven reconfigurable robots, a specific long- and short-term memory neural network model is designed, and the long- and short-term memory neural network algorithm is applied to the robot control problem based on multimodal information fusion. The design of the controller and driver of each joint is the basis of the distributed control system. This paper discusses the hardware design of the joint controller and driver and the realization of the position control system and discusses the method of realizing distributed control based on the Modbus protocol of RS485 communication. Through the comprehensive experiment of the configuration, the point control and the continuous path control are carried out to verify the correctness of the theoretical analysis of the system and the reliability of the system hardware and software operation.

“…One of the contemporary technologies beneficiaries is also biomedicine which deals with experiments exploiting the electrical nature of neural-to-neural communication in living organisms. The main motivation of that experiments is the need for neural coding and processing cognition, finding cures for nervous system-related diseases, or developing prostheses for people with different disabilities [5][6][7][8][9][10][11][12]. These explorations are run as in vitro or in vivo experiments, and are realized with the use of electronic circuits that either record or/and stimulate neural activity.…”

Section: Introductionmentioning

confidence: 99%

Highly Configurable 100 Channel Recording and Stimulating Integrated Circuit for Biomedical Experiments

Kmon

2021

Sensors

This paper presents the design results of a 100-channel integrated circuit dedicated to various biomedical experiments requiring both electrical stimulation and recording ability. The main design motivation was to develop an architecture that would comprise not only the recording and stimulation, but would also block allowing to meet different experimental requirements. Therefore, both the controllability and programmability were prime concerns, as well as the main chip parameters uniformity. The recording stage allows one to set their parameters independently from channel to channel, i.e., the frequency bandwidth can be controlled in the (0.3 Hz–1 kHz)–(20 Hz–3 kHz) (slow signal path) or (0.3 Hz–1 kHz)–4.7 kHz (fast signal path) range, while the voltage gain can be set individually either to 43.5 dB or 52 dB. Importantly, thanks to in-pixel circuitry, main system parameters may be controlled individually allowing to mitigate the circuitry components spread, i.e., lower corner frequency can be tuned in the 54 dB range with approximately 5% precision, and the upper corner frequency spread is only 4.2%, while the voltage gain spread is only 0.62%. The current stimulator may also be controlled in the broad range (69 dB) with its current setting precision being no worse than 2.6%. The recording channels’ input-referred noise is equal to 8.5 µVRMS in the 10 Hz–4.7 kHz bandwidth. The single-pixel occupies 0.16 mm2 and consumes 12 µW (recording part) and 22 µW (stimulation blocks).