Ahmed El Hady

Closed-loop neuroscience and neuroengineering

Feedback and closed-loop circuits exist in just about every part of the nervous system. It is curious, therefore, that for decades neuroscientists have been probing the nervous system in an openloop manner to understand it. Instead of the linear, reductionistic “stimulate → record response” approach, a more modern approach is taking hold: closed-loop neuroscience. It respects the inherent “loopiness” of neural circuits, and the fact that the nervous system is embodied, and embedded in an environment. Through active sensing, behaving animals can influence their environment in ways that alter subsequent sensory inputs. Therefore, loops abound not only in the nervous system itself, but through its dynamic interactions with the world. By interposing our own technology in some of these loops, we can achieve unprecedented control over the system being studied and explore the functional consequences. This Research Topic, “Closing the Loop Around Neural Systems,” presents a diverse set of recent methodological, scientific and theoretical advances from neuroscientists and neuroengineers who are pioneering closed-loop neuroscience. As shown here, cutting-edge researchers are taking advantage of real-time or “on-line” processing of large streams of neural data. This has become feasible thanks to advances in computer processing power, in electronics such as microprocessors and field-programmable gate arrays (FPGAs), and in specialized and open-sourcesoftware.Theseadvanceshaveenabledawidevariety of new neuroscience approaches to understanding, modulating, and interfacing with the nervous system—approaches in which the variables being monitored can influence the experiment in progress, just as active sensing can influence an animal’s next input. Our call for submissions to this Frontiers in Neural Circuits Research Topic yielded an overwhelming response, indicating that closing the loop around neural systems is an exciting and rapidly expanding field. Perhaps this is because of the diversity of ways in which “closed-loops” can be interpreted and implemented. This Research Topic presents seven Methods articles, 16 Original Research articles, and seven Reviews, Mini-Reviews, and Perspectives, for a total of 30 accepted papers published in Frontiers in Neural Circuits between April 2012 and October 2013. A map showing the locations of all the contributors 1 reveals

Controlling the oscillation phase through precisely timed closed-loop optogenetic stimulation: a computational study

Dynamic oscillatory coherence is believed to play a central role in flexible communication between brain circuits. To test this communication-through-coherence hypothesis, experimental protocols that allow a reliable control of phase-relations between neuronal populations are needed. In this modeling study, we explore the potential of closed-loop optogenetic stimulation for the control of functional interactions mediated by oscillatory coherence. The theory of non-linear oscillators predicts that the efficacy of local stimulation will depend not only on the stimulation intensity but also on its timing relative to the ongoing oscillation in the target area. Induced phase-shifts are expected to be stronger when the stimulation is applied within specific narrow phase intervals. Conversely, stimulations with the same or even stronger intensity are less effective when timed randomly. Stimulation should thus be properly phased with respect to ongoing oscillations (in order to optimally perturb them) and the timing of the stimulation onset must be determined by a real-time phase analysis of simultaneously recorded local field potentials (LFPs). Here, we introduce an electrophysiologically calibrated model of Channelrhodopsin 2 (ChR2)-induced photocurrents, based on fits holding over two decades of light intensity. Through simulations of a neural population which undergoes coherent gamma oscillations—either spontaneously or as an effect of continuous optogenetic driving—we show that precisely-timed photostimulation pulses can be used to shift the phase of oscillation, even at transduction rates smaller than 25%. We consider then a canonic circuit with two inter-connected neural populations oscillating with gamma frequency in a phase-locked manner. We demonstrate that photostimulation pulses applied locally to a single population can induce, if precisely phased, a lasting reorganization of the phase-locking pattern and hence modify functional interactions between the two populations.

Optogenetic stimulation effectively enhances intrinsically generated network synchrony

Synchronized bursting is found in many brain areas and has also been implicated in the pathophysiology of neuropsychiatric disorders such as epilepsy, Parkinson’s disease, and schizophrenia. Despite extensive studies of network burst synchronization, it is insufficiently understood how this type of network wide synchronization can be strengthened, reduced, or even abolished. We combined electrical recording using multi-electrode array with optical stimulation of cultured channelrhodopsin-2 transducted hippocampal neurons to study and manipulate network burst synchronization. We found low frequency photo-stimulation protocols that are sufficient to induce potentiation of network bursting, modifying bursting dynamics, and increasing interneuronal synchronization. Surprisingly, slowly fading-in light stimulation, which substantially delayed and reduced light-driven spiking, was at least as effective in reorganizing network dynamics as much stronger pulsed light stimulation. Our study shows that mild stimulation protocols that do not enforce particular activity patterns onto the network can be highly effective inducers of network-level plasticity.

Growing neuronal islands on multi-electrode arrays using an Accurate Positioning-μCP device

BACKGROUND Multi-electrode arrays (MEAs) allow non-invasive multi-unit recording in-vitro from cultured neuronal networks. For sufficient neuronal growth and adhesion on such MEAs, substrate preparation is required. Plating of dissociated neurons on a uniformly prepared MEAs surface results in the formation of spatially extended random networks with substantial inter-sample variability. Such cultures are not optimally suited to study the relationship between defined structure and dynamics in neuronal networks. To overcome these shortcomings, neurons can be cultured with pre-defined topology by spatially structured surface modification. Spatially structuring a MEA surface accurately and reproducibly with the equipment of a typical cell-culture laboratory is challenging. NEW METHOD In this paper, we present a novel approach utilizing micro-contact printing (μCP) combined with a custom-made device to accurately position patterns on MEAs with high precision. We call this technique AP-μCP (accurate positioning micro-contact printing). COMPARISON WITH EXISTING METHODS Other approaches presented in the literature using μCP for patterning either relied on facilities or techniques not readily available in a standard cell culture laboratory, or they did not specify means of precise pattern positioning. CONCLUSION Here we present a relatively simple device for reproducible and precise patterning in a standard cell-culture laboratory setting. The patterned neuronal islands on MEAs provide a basis for high throughput electrophysiology to study the dynamics of single neurons and neuronal networks.

Rats optimally accumulate and discount evidence in a dynamic environment

How choices are made within noisy environments is a central question in the neuroscience of decision making. Previous work has characterized temporal accumulation of evidence for decision-making in static environments. However, real-world decision-making involves environments with statistics that change over time. This requires discounting old evidence that may no longer inform the current state of the world. Here we designed a rat behavioral task with a dynamic environment, to probe whether rodents can optimally discount evidence by adapting the timescale over which they accumulate it. Extending existing results about optimal inference in a dynamic environment, we show that the optimal timescale for evidence discounting depends on both the stimulus statistics and noise in sensory processing. We found that when both of these components were taken into account, rats accumulated and temporally discounted evidence almost optimally. Furthermore, we found that by changing the dynamics of the environment, experimenters could control the rats’ accumulation timescale, switching them from accumulating over short timescales to accumulating over long timescales and back. The theoretical framework also makes quantitative predictions regarding the timing of changes of mind in the dynamic environment. This study establishes a quantitative behavioral framework to control and investigate neural mechanisms underlying the adaptive nature of evidence accumulation timescales and changes of mind.

Foraging as an evidence accumulation process

A canonical foraging task is the patch-leaving problem, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. Evidence accumulation models belong to the class of drift diffusion processes and have been used to model decision making in different contexts especially in cognitive and systems neuroscience. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an increment-decrement or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To account for sub-optimal decisions, we introduce an energy-dependent utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch leaving decisions.

Rats adopt the optimal timescale for evidence integration in a dynamic environment

Decision making in dynamic environments requires discounting old evidence that may no longer inform the current state of the world. Previous work found that humans discount old evidence in a dynamic environment, but do not discount at the optimal rate. Here we investigated whether rats can optimally discount evidence in a dynamic environment by adapting the timescale over which they accumulate evidence. Using discrete evidence pulses, we exactly compute the optimal inference process. We show that the optimal timescale for evidence discounting depends on both the stimulus statistics and noise in sensory processing. When both of these components are taken into account, rats accumulate and discount evidence with the optimal timescale. Finally, by changing the volatility of the environment, we demonstrate experimental control over the rats’ accumulation timescale. The mechanisms supporting integration are a subject of extensive study, and experimental control over these timescales may open new avenues of investigation.In a dynamic environment old evidence could be outdated. Here, the authors investigate the ability of rats to integrate and discount evidence provided by auditory clicks to infer a hidden, dynamic, state of the world and model the consequence of sensory noise to explain the source of errors.

Enhancing burst activation and propagation in cultured neuronal networks by photo-stimulation

Spontaneous bursting activity in cultured neuronal networks is initiated by leader neurons, which constitute a small subset of first-to-fire neurons forming a sub-network that recruits follower neurons into the burst. While the existence and stability of leader neurons is well established, the influence of stimulation on the leader-follower dynamics is not sufficiently understood. By combining multi-electrode array recordings with whole field optical stimulation of cultured Channelrhodopsin-2 transduced hippocampal neurons, we show that fade-in photo-stimulation induces a significant shortening of intra-burst firing rate peak delay of follower electrodes after offset of the stimulation compared to unperturbed spontaneous activity. Our study shows that optogenetic stimulation can be used to change the dynamical fine structure of self-organized network bursts.