Sarah Jarvis

Prospects for Optogenetic Augmentation of Brain Function.

The ability to optically control neural activity opens up possibilities for the restoration of normal function following neurological disorders. The temporal precision, spatial resolution, and neuronal specificity that optogenetics offers is unequalled by other available methods, so will it be suitable for not only restoring but also extending brain function? As the first demonstrations of optically “implanted” novel memories emerge, we examine the suitability of optogenetics as a technique for extending neural function. While optogenetics is an effective tool for altering neural activity, the largest impediment for optogenetics in neural augmentation is our systems level understanding of brain function. Furthermore, a number of clinical limitations currently remain as substantial hurdles for the applications proposed. While neurotechnologies for treating brain disorders and interfacing with prosthetics have advanced rapidly in the past few years, partially addressing some of these critical problems, optogenetics is not yet suitable for use in humans. Instead we conclude that for the immediate future, optogenetics is the neurological equivalent of the 3D printer: its flexibility providing an ideal tool for testing and prototyping solutions for treating brain disorders and augmenting brain function.

Computational models of optogenetic tools for controlling neural circuits with light

Optogenetics is a new neurotechnology innovation based on the creation of light sensitivity of neurons using gene technologies and remote light activation. Optogenetics allows for the first time straightforward targeted neural stimulation with practically no interference between multiple stimulation points since either light beam can be finely confined or the expression of light sensitive ion channels and pumps can be genetically targeted. Here we present a generalised computational modeling technique for various types of optogenetic mechanisms, which was implemented in the NEURON simulation environment. It was demonstrated on the example of a two classical mechanisms for cells optical activation and silencing: channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR).We theoretically investigate the dynamics of the neural response of a layer 5 cortical pyramidal neuron (L5) to four different types of illuminations: 1) wide-field whole cell illumination 2) wide-field apical dendritic illumination 3) focal somatic illumination and 4) focal axon initial segment (AIS) illumination. We show that whole-cell illumination of halorhodopsin most effectively hyperpolarizes the neuron and is able to silence the cell even when driving input is present. However, when channelrhodopsin-2 and halorhodopsin are concurrently active, the relative location of each illumination determines whether the response is modulated with a balance towards depolarization. The methodology developed in this study will be significant to interpret and design optogenetic experiments and in the field of neuroengineering in general.

PyRhO: A Multiscale Optogenetics Simulation Platform

Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.

Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity

Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neurons robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the models increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Optical coactivation in cortical cells: reprogramming the excitation-inhibition balancing act to control neuronal gain in abstract and detailed models

The interplay of excitatory and inhibitory activity in neuronal populations is finely regulated within cortical layers, with their imbalance being heavily implicated as the underlying cause for many neurological disorders, such as autism, schizophrenia and epilepsy. A key regulatory mechanism is gain modulation, in which the amplitude of response changes while the cell’s selectivity remains unaffected. Previous work has addressed gain modulation by examining the interplay of excitatory and inhibitory input at the soma [1]. However, given the non-linear integration that occurs in dendritic arbors, it remains unclear how gain is modulated when the input is located at synaptic locations. For investigating and manipulating this balance of activity throughout the entire neuronal morphology, optogenetics is a powerful tool due to the fine temporal and spatial precision it provides [2]. Furthermore, due to the development of excitatory opsins, such as Channelrhodopsin-2 (ChR2), that depolarize neuronal membrane and silencing opsins, including halorhodopsin (NpHR), that hyperpolarize the membrane, disjoint subdomains of the dendritic and soma morphology can be targeted. This capability has recently been furthered by the development of co-activated opsins, such as ChR2-NpHR [3], which allow independent excitation and inhibition within the same neural population due to the different preferential excitation wavelengths of each opsin (l=490, 585nm for ChR2 and NpHR respectively). Together, these opsins provide a potential window through which to examine the interplay of competing excitatory and inhibitory inputs for differing spatial and temporal patterns of activation. We demonstrated previously that gain modulation in a detailed model of a Layer 5 Pyramidal cell using coactivated opsins is possible but highly dependent on the dendritic subdomains targeted [4,5], with whole cell illumination necessary to illicit gain modulation. In contrast, partial illumination of only the apical dendrites and soma resulted in no gain modulation. This suggests a strong link between potential for gain modulation and neuron morphology. While this result helps to untangle the relative contribution of excitatory and inhibitory influences, and warns of inadvertent errors when shallow illumination occurs experimentally. We investigate this relation by first testing optical activation in abstracted neuron morphologies that include models of ChR2 and NpHR. By creating a family of neural morphologies that extend a simple ball-and-stick neuron model, we investigate how uni-, biand multi-polar neurons vary gain modulation upon partial illumination. External driving input is provided as both current injection and as multiple synaptic-like events at locations on dendrites, rather than the soma, to mimic input conditions for both in vitro and in vivo experiments. Using these models, we identify optimal illumination strategies for each morphological class of neuron, and predict how robust neuronal response is upon partial illumination. Finally, we test detailed neuron morphologies, including stellate interneurons, to test the predictions made by our abstract models. Our results highlight the role of dendritic subdomains and the localized contribution of excitatory and inhibitory activity in gain modulation. Importantly, our model allows us to predict experimental illumination strategies that are tailored to neuronal morphology and are robust to any limitations that can occur experimentally. * Correspondence: s.jarvis@imperial.ac.uk Department of Bioengineering, Imperial College, London SW7 2AZ UK Full list of author information is available at the end of the article Jarvis et al. BMC Neuroscience 2014, 15(Suppl 1):F1 http://www.biomedcentral.com/1471-2202/15/S1/F1

Neuronal gain modulability is determined by dendritic morphology: A computational optogenetic study

The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron’s input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron’s gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells—confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functions, such as gain modulation.

PyPNS: Multiscale Simulation of a Peripheral Nerve in Python

Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modelled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.

Multiscale simulation of peripheral neural signaling

Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms to evaluate spontaneous activity patterns, stimulation efficiency, and organ responses. To reduce experimentation load and allow for a faster, more detailed analysis of both recording from and stimulation of peripheral nerves, adaptable computational models incorporating insights won in experiments will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealized extracellular space models in one environment. Two different scales of abstraction were merged. On the one hand we modeled the extracellular space in a finite element solver as a three dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed for different media (homogeneous, nerve in saline, nerve in cuff). Axons, on the other hand, were modeled at a higher level of abstraction as one dimensional chains of compartments; each consisting of lumped linear elements and, for some, channels with non-linear dynamics. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibers, we instead adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibers along the nerve with variable tortuosity, with tortuosity values fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity leads to differentiation in recorded signal shapes, with unmyelinated axons being the most affected. Tortuosity was further shown to increase the stimulation threshold. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.

Non-linear dendrites enable robust stimulus selectivity.

Hubel and Wiesel discovered that some neurons in the visual cortex respond selectively to elongated visual stimuli of a particular orientation, proposing an elegant feedforward model to account for this selectivity. Since then, there has been much experimental support for this model, however several apparently counter-intuitive recent results, from in vivo two photon imaging of the dendrites of layer 2/3 pyramidal neurons in visual and somatosensory cortex cast doubt on the basic form of the model. Firstly, the dendrites may have different stimulus tuning to that of the soma. Secondly, hyperpolarizing a cell can result in it losing its stimulus selectivity, while the dendritic tuning remains unaffected. These results demonstrate the importance of dendrites in generating stimulus selectivity. Here, we implement stimulus selectivity in a biophysical model based on the realistic morphology of a layer 2/3 neuron, that can account for both of these experimental observations, within the feedforward framework motivated by Hubel and Wiesel. We show that this new model of stimulus selectivity is robust to the loss of synapses or dendrites, with stimulus selectivity maintained up to losses of 1/2 of the synapses, or 2/7 of the dendrites, demonstrating that in addition to increasing the computational capacity of neurons, dendrites also increase the robustness of neuronal computation. As well as explaining experimental results not predicted by Hubel and Wiesel, our study shows that dendrites enhance the resilience of cortical information processing, and prompts the development of new neuromorphic chips incorporating dendritic processing into their architecture.Hearing, vision, touch-underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Non-linear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of non-preferred stimuli. Using a multi-subunit non-linear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neurons robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of synapses or dendrites loss than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites, that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially non-selective neuron can become selective when depolarized. In addition to motivating new experiments, the models increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

PyRhO: a virtual optogenetics laboratory

Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behaviour. An array of opsins have been genetically isolated from several families of organism, including algae and bacteria, with a wide range of temporal and spectral properties. In an effort to develop more effective and tailored opsins, hybrids and genetic mutants are continually being created. Experimentally characterizing these new variants is a lengthy process requiring substantial effort before they can be harnessed to address questions in neuroscience. Experimentally testing each combination of opsin and target cell type of interest is practically impossible, effectively limiting the use of optogenetics as a tool. To aid in this effort we propose PyRhO; an integrated suite of open-source, multi-scale computational tools to characterize rhodopsins, then rapidly develop and conduct virtual experiments with them in silico. From a minimal set of photocurrent data, PyRhO will fit and parameterize the Three [1], Four [1] and Six-state [2] rhodopsin models to capture the underlying biophysical photocycle which defines their kinetics. These models are then used to accurately compute the photocurrents across a range of flux, voltage and other experimental conditions for the given rhodopsin. After selecting a suitable model based on the desired balance between simulation accuracy and speed, the artificial rhodopsin can be seamlessly inserted into software such as NEURON and Brian for use in simulations from the cellular to the network level. We demonstrate the use of PyRhO in fitting models to channelrhodopsin-2 (ChR2) [3] data and present results for typical illumination strategies and experimental protocols designed to tease apart the effects of key model parameters. The tools are written in Python for easy scripting of experiments and compatibility with a large array of open-source modules and software. An accompanying GUI running in IPython [4] has also been developed to facilitate more interactive exploration of the models for both experimental and didactic purposes. Furthermore, IPython has been identified as a particularly promising medium for sharing models and reproducing results in computational neuroscience [5]. Simulations based on these virtual opsins will enable neuroscientists to gain insight into their behaviour and rapidly identify the most suitable variant for application in a particular biological system, not only guiding choice, but potentially also rhodopsin development. In this way, we expect PyRhO will help to significantly improve the effectiveness of optogenetics as a tool for transforming biological sciences.