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.

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.

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.

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.

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 . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/023200 doi: bioRxiv preprint first posted online Jul. 24, 2015; 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 neuron's 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 model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Echo State Networks (ESN) are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantitatively determine the influence of the most relevant network parameters, we analyzed the impact of reservoir substructures on stability in hierarchically clustered ESNs, as they allow a smooth transition from highly structured to increasingly homogeneous reservoirs. Previous studies used the largest eigenvalue of the reservoir connectivity matrix (spectral radius) as a predictor for stable network dynamics. Here, we evaluate the impact of clusters, hierarchy and intercluster connectivity on the predictive power of the spectral radius for stability. Both hierarchy and low relative cluster sizes extend the range of spectral radius values, leading to stable networks, while increasing intercluster connectivity decreased maximal spectral radius.

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.

Gain modulability indicated by dendritic morphology• Pyramidal cell-like shapes optimally receptive to modulation • All dendritic subdomains required for gain modulation, partial illumination is insufficient• Computational optogenetic models improve and refine experimental protocols PLOS 1/19 39 the balance of excitatory and inhibitory currents locally in dendrites, to act as a 40 synthetic substitute for the effect of excitatory and inhibitory presynaptic input. This 41 raises the prospect of a viable experimental method with which to investigate the 42 mechanisms of neuronal gain modulation in the whole cell, as opposed to studying 43 somatic effects alone. 44Here we demonstrate through a computational model that neuronal gain modulation 45 can be determined by cell morphology, by means of a set of dendritic morphological 46 features which mediate between attenuation and shunting to modulate neuronal output. 47 The local interaction of competing excitatory and inhibitory inputs is sensitive to the 48 placement of dendritic sections. This indicates that gain modulation can be achieved by 49 altering the overall balance of excitation and inhibition that a neuron receives, rather 50 than being dependent on the statistical properties of the synaptic input. As 51 experimental validation of our work would require optogenetics, we tested our 52 PLOS 2/19 hypothesis in detailed, biophysical models of opsin-transfected neurons, using 53 experimentally fitted models of channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR), 54 which when activated produced excitatory and inhibitory photocurrents. 55This study proposes a new perspective on the contribution of dendritic morphology 56 to the characteristics of neurons, relating the shape of their dendritic arbors directly to 57 their functional role within the neural circuit. We show that all dendritic subdomains 58 are required for gain modulation to occur, suggesting that distinct innervation through 59 synaptic targeting by discrete presynaptic populations could be an effective mechanism 60 by which a neuron's output can be quickly gated between high gain and low gain modes. 61 By incorporating kinetic opsin models within our detailed, compartmental neuron 62 modeling approach, we make predictions which are directly testable by optogenetic 63 experiments. In particular, our model leads to the proposal of a new illumination 64 protocol for a more naturalistic method of neuronal photostimulation -in which rather 65 than simply imposing spikes or shutting down neuronal activity entirely, it is possible to 66 increase or decrease the gain of a neuron's response to existing inputs. 67 We would also like to clarify the meaning of our terminology for the "in vitro" and 68 "in vivo" simulations: "in vitro" refers to "a single point injection, as per patch clamp 69 experiments", and "in vivo" refers to "multiple synaptic-like activity". In this way we 70 emphasize the significant difference whether the driving input was a single point or 71 multiple synaptic sites. 72 Results 73De...