A remote control training system for rat navigation in complicated environment

Feng, Zhouyan; Chen, Weidong; Ye, Xuesong; Zhang, Shaomin; Zheng, Xiaojing; Wang, Peng; Jiang, Jun; Jin, Lin; Zhang, Xu; Liu, Chunqing; Liu, Fu-xin; Luo, Jianhong; Zhuang, Yueting; Zheng, Xiaoxiang

doi:10.1631/jzus.2007.a0323

Cited by 56 publications

(32 citation statements)

References 11 publications

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“…We first tested the stimulation function by navigation experiment (Feng et al, 2007) and then tested the recording function by acquiring primary motor area (M1) neuron signals of freely moving rat. At last, we tested both stimulation and recording function synchronously in real time in the same rat by the experiment of amygdaloid-kindled seizures.…”

Section: Methodsmentioning

confidence: 99%

“…Since there are four pairs of stimulation electrodes but only two DAC channels, it is necessary to use analog switches (Max4754, Maxim Inc.) to multiplex the signals. The more detailed stimulation circuits design is shown in a previous paper (Feng et al, 2007).…”

Section: Main Boardmentioning

confidence: 99%

“…The telemetry systems for electrical stimulation (Holzer and Shimoyama, 1997;Xu et al, 2004;Wang et al, 2006;Song et al, 2006;Feng et al, 2007) or neuronal activity recording (Grohrock et al, 1997;Hawley et al, 2002;Obeid et al, 2004;Chien and Jaw, 2005;Schregardus et al, 2006;Jürgens and Hage, 2006;Vyssotski et al, 2006;Ativanichayaphong et al, 2008) have been reported in recent years. In one demonstrator, electrical stimulation of the somatosensory cortex (SI) and medial forebrain bundle (MFB) was used as cues and rewards to condition distant rats to execute controlled turns remotely (Talwar et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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A portable telemetry system for brain stimulation and neuronal activity recording in freely behaving small animals

Wang

Liu

et al. 2008

Journal of Neuroscience Methods

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Section: Methodsmentioning

confidence: 99%

Section: Main Boardmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A portable telemetry system for brain stimulation and neuronal activity recording in freely behaving small animals

Wang

Liu

et al. 2008

Journal of Neuroscience Methods

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“…Then, flight control of a cyborg beetle was achieved via neural and muscular stimulation [10,11]. Despite major advances [12][13][14][15][16][17], robo-animals cannot travel far and continue to display poor performance due to some common difficulties. For example, the stimulators in robo-animals are occasionally too large to successfully implant into an animal, the communication distance between the stimulator and remote control is limited, and the control of bio-robot navigation is unstable because of the adaptation of animal neurons.…”

Section: Introductionmentioning

confidence: 99%

A robo-pigeon based on an innovative multi-mode telestimulation system

Yang

Huai

Wang

et al. 2015

BME

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Abstract. In this paper, we describe a new multi-mode telestimulation system for brain-microstimulation for the navigation of a robo-pigeon, a new type of bio-robot based on Brain-Computer Interface (BCI) techniques. The multi-mode telestimulation system overcomes neuron adaptation that was a key shortcoming of the previous single-mode stimulation by the use of non-steady TTL biphasic pulses accomplished by randomly alternating pulse modes. To improve efficiency, a new behavior model ("virtual fear") is proposed and applied to the robo-pigeon. Unlike the previous "virtual reward" model, the "virtual fear" behavior model does not require special training. The performance and effectiveness of the system to alleviate the adaptation of neurons was verified by a robo-pigeon navigation test, simultaneously confirming the practicality of the "virtual fear" behavioral model.

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“…The technology of invasive brain-machine interfaces (BMI) has been developed in decades, due to its possibility to cure neurological disabilities (Lebedev and Nicolelis, 2006;Feng et al, 2007;Ye et al, 2008). The main task of BMI system is decoding the cortical neural activity into commands of direct braincontrolled prosthetic devices.…”

Section: Introductionmentioning

confidence: 99%

Neural decoding based on probabilistic neural network

Zhang

et al. 2010

J. Zhejiang Univ. Sci. B

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Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices, such as robot arms, computer cursors, and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper, two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced, the PNN decoder and the modified PNN (MPNN) decoder. In the experiment, rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity, and pressure was recorded by a pressure sensor synchronously. After training, the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their performances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder, with a CC of 0.8657 and an MSE of 0.2563, outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance, indicating that the MPNN decoder can handle different tasks in BMI system, including the detection of movement states and estimation of continuous kinematic parameters.

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A remote control training system for rat navigation in complicated environment

Cited by 56 publications

References 11 publications

A portable telemetry system for brain stimulation and neuronal activity recording in freely behaving small animals

A portable telemetry system for brain stimulation and neuronal activity recording in freely behaving small animals

A robo-pigeon based on an innovative multi-mode telestimulation system

Neural decoding based on probabilistic neural network

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