General features of the retinal connectome determine the computation of motion anticipation

Motion anticipation allows the visual system to compensate for the slow speed of phototransduction so that a moving object can be accurately located. This correction is already present in the signal that ganglion cells send from the retina but the biophysical mechanisms underlying this computation are not known. Here we demonstrate that motion anticipation is computed autonomously within the dendritic tree of each ganglion cell and relies on feedforward inhibition. The passive and non-linear interaction of excitatory and inhibitory synapses enables the somatic voltage to encode the actual position of a moving object instead of its delayed representation. General rather than specific features of the retinal connectome govern this computation: an excess of inhibitory inputs over excitatory, with both being randomly distributed, allows tracking of all directions of motion, while the average distance between inputs determines the object velocities that can be compensated for

Doctor of Philosophy

dissertationThe goal of this work is to construct a simulation toolset for studying and improving neuroprosthetic devices for restoring neural functionality to patients with neural disorders or diseases. This involves the construction and validation of coupled electromagnetic-neural computational models of retina and hippocampus, compiling knowledge from a broad multidisciplinary background into a single computational platform, with features specific to implant electronics, bulk tissue, cellular and neural network behavior, and diseased tissue. The application of a retina prosthetic device for restoring partial vision to patients blinded by degenerative diseases was first considered. This began with the conceptualization of the retina model, translating features of a connectome, implant electronics, and medical images into a computational model that was "degenerated." It was then applied to the design of novel electrode geometries towards increasing the resolution of induced visual percept, and of stimulation waveform shapes for increasing control of induced neural activity in diseased retina. Throughout this process, features of the simulation toolset itself were modified to increase the precision of the results, leading to a novel method for computing effective bulk resistivity for use in such multiscale modeling. This simulation strategy was then extended to the application of a hippocampus prosthetic device, which has been proposed to restore and/or enhance memory in patients with memory disorders such as Alzheimer's disease or dementia. Using this multiscale modeling approach, we are able to provide recommendations for electrode geometry, placement, and stimulation magnitude for increased safety and efficacy in future experimental trials. In attempt to model neural activity in dense hippocampal tissue, a simulation platform for considering the effects the electrical activity of neural networks have on the extracellular electric field, and therefore have on their neighboring cells, was constructed, further increasing the predictive ability of the proposed methodology for modeling electrical stimulation of neural tissue

Retinal drug delivery: rethinking outcomes for the efficient replication of retinal behavior

The retina is a highly organized structure that is considered to be "an approachable part of the brain." It is attracting the interest of development scientists, as it provides a model neurovascular system. Over the last few years, we have been witnessing significant development in the knowledge of the mechanisms that induce the shape of the retinal vascular system, as well as knowledge of disease processes that lead to retina degeneration. Knowledge and understanding of how our vision works are crucial to creating a hardware-adaptive computational model that can replicate retinal behavior. The neuronal system is nonlinear and very intricate. It is thus instrumental to have a clear view of the neurophysiological and neuroanatomic processes and to take into account the underlying principles that govern the process of hardware transformation to produce an appropriate model that can be mapped to a physical device. The mechanistic and integrated computational models have enormous potential toward helping to understand disease mechanisms and to explain the associations identified in large model-free data sets. The approach used is modulated and based on different models of drug administration, including the geometry of the eye. This work aimed to review the recently used mathematical models to map a directed retinal network.The authors acknowledge the financial support received from the Portuguese Science and Technology Foundation (FCT/MCT) and the European Funds (PRODER/COMPETE) for the project UIDB/04469/2020 (strategic fund), co-financed by FEDER, under the Partnership Agreement PT2020. The authors also acknowledge FAPESP – São Paulo Research Foundation, for the financial support for the publication of the article.info:eu-repo/semantics/publishedVersio

The iso-response method

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments

Simulating and optimizing electrical current flow and neuronal activation in retinal implants

© 2018 Dr Timothy Bede Esler[Abstract Withheld

Non-viral delivery and optimized optogenetic stimulation of retinal ganglion cells led to behavioral restoration of vision

Stimulation of retinal neurons using optogenetics via use of chanelrhodopsin-2 (ChR2) has opened up a new direction for restoration of vision for treatment of retinitis pigmentosa (RP). Here, we report non-viral in-vivo electroporation of degenerated retina of adult RP-mice with ChR2-plasmids and subsequent in-vivo imaging of retina to confirm expression. Further, we demonstrate that in addition to efficient non-viral delivery of ChR2 to a specific retinal layer, threshold level of stimulation light needs to be delivered onto the retina for achieving successful behavioral outcome. Measurement of intensity of light reaching the retina of RP-mouse models along with geometrical optics simulation of light propagation in the eye is reported in order to determine the stimulating source position for optimal light delivery to the retina. The light-guided navigation of mice with ChR2 expressing retinal ganglion cells was found to be significantly improved over a long distance in correlation with stimulation intensity

Characterization of Retinal Ganglion Cell Responses to Electrical Stimulation Using White Noise

Retinitis pigmentosa and age-related macular degeneration are two leading causes of degenerative blindness. While there is still not a definitive course of treatment for either of these diseases, there is currently the world over, many different treatment strategies being explored. Of these various strategies, one of the most successful has been retinal implants. Retinal implants are microelectrode or photodiode arrays, that are implanted in the eye of a patient, to electrically stimulate the degenerating retina. Clinical trials have shown that many patients implanted with such a device, are able to regain a certain degree of functional vision. However, while the results of these ongoing clinical trials have been promising, there are still many technical challenges that need to be overcome. One of the biggest challenges facing present implants is the inability to preferentially stimulate different retinal pathways. This is because retinal implants use large-amplitude current or voltage pulses. This in turn leads to the indiscriminate activation of multiple classes of retinal ganglion cells (RGCs), and therefore, an overall reduction in the restored visual acuity. To tackle this issue, we decided to explore a novel stimulus paradigm, in which we present to the retina, a stream of smaller-amplitude subthreshold voltage pulses. By then correlating the retinal spikes to the stimuli preceding them, we calculate temporal input filters for various classes of RGCs, using a technique called spike-triggered averaging (STA). In doing this, we found that ON and OFF RGCs have electrical filters, which are very distinct from each other. This finding creates the possibility for the selective activation of the retina through the use of STA-based waveforms. Finally, using statistical models, we verify how well these temporal filters can predict RGC responses to novel electrical stimuli. In a broad sense, our work represents the successful application of systems engineering tools to retinal prosthetics, in an attempt to answer one of the field’s most difficult questions, namely selective stimulation of the retina

Excitatory postsynaptic potentials in rat neocortical neurons in vitro. III. Effects of a quinoxalinedione non-NMDA receptor antagonist

1. Intracellular microelectrodes were used to obtain recordings from neurons in layer II/III of rat frontal cortex. A bipolar electrode positioned in layer IV of the neocortex was used to evoke postsynaptic potentials. Graded series of stimulation were employed to selectively activate different classes of postsynaptic responses. The sensitivity of postsynaptic potentials and iontophoretically applied neurotransmitters to the non-N-methyl-D-asparate (NMDA) antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) was examined. 2. As reported previously, low-intensity electrical stimulation of cortical layer IV evoked short-latency early excitatory postsynaptic potentials (eEPSPs) in layer II/III neurons. CNQX reversibly antagonized eEPSPs in a dose-dependent manner. Stimulation at intensities just subthreshold for activation of inhibitory postsynaptic potentials (IPSPs) produced long-latency (10 to 40-ms) EPSPs (late EPSPs or 1EPSPs). CNQX was effective in blocking 1EPSPs. 3. With the use of stimulus intensities at or just below threshold for evoking an action potential, complex synaptic potentials consisting of EPSP-IPSP sequences were observed. Both early, Cl(-)-dependent and late, K(+)-dependent IPSPs were reduced by CNQX. This effect was reversible on washing. This disinhibition could lead to enhanced excitability in the presence of CNQX. 4. Iontophoretic application of quisqualate produced a membrane depolarization with superimposed action potentials, whereas NMDA depolarized the membrane potential and evoked bursts of action potentials. At concentrations up to 5 microM, CNQX selectively antagonized quisqualate responses. NMDA responses were reduced by 10 microM CNQX. D-Serine (0.5-2 mM), an agonist at the glycine regulatory site on the NMDA receptor, reversed the CNQX depression of NMDA responses