Recent developments have used light-activated channels or transporters to modulate neuronal activity. One such genetically-encoded modulator of activity, channelrhodopsin-2 (ChR2), depolarizes neurons in response to blue light. In this work, we first conducted electrophysiological studies of the photokinetics of hippocampal cells expressing ChR2, for various light stimulations. These and other experimental results were then used for systematic investigation of the previously proposed three-state and four-state models of the ChR2 photocycle. We show the limitations of the previously suggested three-state models and identify a four-state model that accurately follows the ChR2 photocurrents. We find that ChR2 currents decay biexponentially, a fact that can be explained by the four-state model. The model is composed of two closed (C1 and C2) and two open (O1 and O2) states, and our simulation results suggest that they might represent the dark-adapted (C1-O1) and light-adapted (C2-O2) branches. The crucial insight provided by the analysis of the new model is that it reveals an adaptation mechanism of the ChR2 molecule. Hence very simple organisms expressing ChR2 can use this form of light adaptation.

Studying neuronal processes such as synaptic summation, dendritic physiology and neural network dynamics requires complex spatiotemporal control over neuronal activities. The recent development of neural photosensitization tools, such as channelrhodopsin-2 (ChR2), offers new opportunities for non-invasive, flexible and cell-specific neuronal stimulation. Previously, complex spatiotemporal control of photosensitized neurons has been limited by the lack of appropriate optical devices which can provide 2D stimulation with sufficient irradiance. Here we present a simple and powerful solution that is based on an array of high-power micro light-emitting diodes (micro-LEDs) that can generate arbitrary optical excitation patterns on a neuronal sample with micrometre and millisecond resolution. We first describe the design and fabrication of the system and characterize its capabilities. We then demonstrate its capacity to elicit precise electrophysiological responses in cultured and slice neurons expressing ChR2.

Objective. Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. Approach. We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. Main results. The classification accuracy in the offline tests reached for the seen and for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to . In addition, we show that with training, subjects’ performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. Significance. The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably.

Channelrhodopsin-2 (ChR2) has become a widely used tool for stimulating neurons with light. Nevertheless, the underlying dynamics of the ChR2-evoked spikes are still not yet fully understood. Here, we develop a model that describes the response of ChR2-expressing neurons to light stimuli and use the model to explore the light-to-spike process. We show that an optimal stimulation yield is achieved when the optical energies are delivered in short pulses. The model allows us to theoretically examine the effects of using various types of ChR2 mutants. We show that while increasing the lifetime and shuttering speed of ChR2 have limited effect, reducing the threshold irradiance by increased conductance will eliminate adaptation and allow constant dynamic range. The model and the conclusion presented in this study can help to interpret experimental results, design illumination protocols, and seek improvement strategies in the nascent optogenetic field.

The recent discovery that neurons can be photostimulated via genetic incorporation of artificial opsins is creating a revolution in the field of neural stimulation. In this paper we show its potential in the field of retinal prosthesis. We show that we need typically 100 mW cm(-2) in instantaneous light intensity on the neuron in order to stimulate action potentials. We also show how this can be reduced down to safe levels in order to negate thermal and photochromic damage to the eye. We also describe a gallium nitride LED light source which is also able to generate patterns of the required intensity in order to transfer reliable images.

Fabrication of polymer multilayer structures has previously been difficult to achieve due to the need for solvent orthogonality to ensure that sequential deposition does not damage any underlying layers. In this paper we report an alternative approach based on a stamp transfer printing process. We demonstrate its suitability for uniform and patterned multilayer deposition (c.f. figure) and use it as a means to engineer enhanced performance photodiode structures.

Stimulating neuron cells with light is an exciting new technology that is revolutionizing the neurosciences. To date, due to the optical complexity that is involved, photostimulation has only been achieved at a single site using high power light sources. Here we present a GaN based micro-light emitting diode (LED) array that can open the way to multi-site photostimulation of neuron cells. The device is a two-dimensional array of micrometre size LED emitters. Each emitter has the required wavelength, optical power and modulation bandwidth to trigger almost any photosensitizer and is individually addressable. We demonstrate micrometre resolution photoactivation of a caged fluorophore and photostimulation of sensitized living neuron cells. In addition, a complete system that combines the micro-LED array with multi-site electrophysiological recording based on microelectrode array technology and/or fluorescence imaging is presented.

Retinitis pigmentosa (RP) is a progressive retinal dystrophy that causes visual impairment and eventual blindness. Retinal prostheses are the best currently available vision-restoring treatment for RP, but only restore crude vision. One possible contributing factor to the poor quality of vision achieved with prosthetic devices is the pathological retinal ganglion cell (RGC) hyperactivity that occurs in photoreceptor dystrophic disorders. Gap junction blockade with meclofenamic acid (MFA) was recently shown to diminish RGC hyperactivity and improve the signal-to-noise ratio (SNR) of RGC responses to light flashes and electrical stimulation in the rd10 mouse model of RP. We sought to extend these results to spatiotemporally patterned optogenetic stimulation in the faster-degenerating rd1 model and compare the effectiveness of a number of drugs known to disrupt rd1 hyperactivity. We crossed rd1 mice with a transgenic mouse line expressing the light-sensitive cation channel channelrhodopsin2 (ChR2) in RGCs, allowing them to be stimulated directly using high-intensity blue light. We used 60-channel ITO multielectrode arrays to record ChR2-mediated RGC responses from wholemount, ex-vivo retinas to full-field and patterned stimuli before and after application of MFA, 18-β-glycyrrhetinic acid (18BGA, another gap junction blocker) or flupirtine (Flu, a Kv7 potassium channel opener). All three drugs decreased spontaneous RGC firing, but 18BGA and Flu also decreased the sensitivity of RGCs to optogenetic stimulation. Nevertheless, all three drugs improved the SNR of ChR2-mediated responses. MFA also made it easier to discern motion direction of a moving bar from RGC population responses. Our results support the hypothesis that reduction of pathological RGC spontaneous activity characteristic in retinal degenerative disorders may improve the quality of visual responses in retinal prostheses and they provide insights into how best to achieve this for optogenetic prostheses.