Computational modeling of color perception with biologically plausible spiking neural networks.

Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons' spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (SNN)-driven model for the reconstruction of colorful images from retinal inputs. We compared our results to experimentally obtained V1 neuronal activity maps in a macaque monkey using voltage-sensitive dye imaging and used the model to demonstrate and critically explore color constancy, color assimilation, and ambiguous color perception. Our parametric implementation allows critical evaluation of visual phenomena in a single biologically plausible computational framework. It uses a parametrized combination of high and low pass image filtering and SNN-based filling-in Poisson processes to provide adequate color image perception while accounting for differences in individual perception.

Realistic retinal modeling unravels the differential role of excitation and inhibition to starburst amacrine cells in direction selectivity.

Retinal direction-selectivity originates in starburst amacrine cells (SACs), which display a centrifugal preference, responding with greater depolarization to a stimulus expanding from soma to dendrites than to a collapsing stimulus. Various mechanisms were hypothesized to underlie SAC centrifugal preference, but dissociating them is experimentally challenging and the mechanisms remain debatable. To address this issue, we developed the Retinal Stimulation Modeling Environment (RSME), a multifaceted data-driven retinal model that encompasses detailed neuronal morphology and biophysical properties, retina-tailored connectivity scheme and visual input. Using a genetic algorithm, we demonstrated that spatiotemporally diverse excitatory inputs-sustained in the proximal and transient in the distal processes-are sufficient to generate experimentally validated centrifugal preference in a single SAC. Reversing these input kinetics did not produce any centrifugal-preferring SAC. We then explored the contribution of SAC-SAC inhibitory connections in establishing the centrifugal preference. SAC inhibitory network enhanced the centrifugal preference, but failed to generate it in its absence. Embedding a direction selective ganglion cell (DSGC) in a SAC network showed that the known SAC-DSGC asymmetric connectivity by itself produces direction selectivity. Still, this selectivity is sharpened in a centrifugal-preferring SAC network. Finally, we use RSME to demonstrate the contribution of SAC-SAC inhibitory connections in mediating direction selectivity and recapitulate recent experimental findings. Thus, using RSME, we obtained a mechanistic understanding of SACs' centrifugal preference and its contribution to direction selectivity.

NeuroConstruct-based implementation of structured-light stimulated retinal circuitry.

BACKGROUND: Retinal circuitry provides a fundamental window to neural networks, featuring widely investigated visual phenomena ranging from direction selectivity to fast detection of approaching motion. As the divide between experimental and theoretical visual neuroscience is fading, neuronal modeling has proven to be important for retinal research. In neuronal modeling a delicate balance is maintained between bio-plausibility and model tractability, giving rise to myriad modeling frameworks. One biologically detailed framework for neuro modeling is NeuroConstruct, which facilitates the creation, visualization and analysis of neural networks in 3D. RESULTS: Here, we extended NeuroConstruct to support the generation of structured visual stimuli, to feature different synaptic dynamics, to allow for heterogeneous synapse distribution and to enable rule-based synaptic connectivity between cell populations. We utilized this framework to demonstrate a simulation of a dense plexus of biologically realistic and morphologically detailed starburst amacrine cells. The amacrine cells were connected to a ganglion cell and stimulated with expanding and collapsing rings of light. CONCLUSIONS: This framework provides a powerful toolset for the investigation of the yet elusive underlying mechanisms of retinal computations such as direction selectivity. Particularly, we showcased the way NeuroConstruct can be extended to support advanced field-specific neuro-modeling.

Autonomous driving controllers with neuromorphic spiking neural networks.

Autonomous driving is one of the hallmarks of artificial intelligence. Neuromorphic (brain-inspired) control is posed to significantly contribute to autonomous behavior by leveraging spiking neural networks-based energy-efficient computational frameworks. In this work, we have explored neuromorphic implementations of four prominent controllers for autonomous driving: pure-pursuit, Stanley, PID, and MPC, using a physics-aware simulation framework. We extensively evaluated these models with various intrinsic parameters and compared their performance with conventional CPU-based implementations. While being neural approximations, we show that neuromorphic models can perform competitively with their conventional counterparts. We provide guidelines for building neuromorphic architectures for control and describe the importance of their underlying tuning parameters and neuronal resources. Our results show that most models would converge to their optimal performances with merely 100-1,000 neurons. They also highlight the importance of hybrid conventional and neuromorphic designs, as was suggested here with the MPC controller. This study also highlights the limitations of neuromorphic implementations, particularly at higher (> 15 m/s) speeds where they tend to degrade faster than in conventional designs.

Walking on the thin intersectional lines of disciplines.

Elishai Ezra Tsur, a multidisciplinary researcher, talks about the challenges that conventional academic mindset brought to his professional life. He, DeWolf, and Supic introduce us with their viewpoint about "data science" and its role in their research. In their recent work published in this issue of Patterns, they tackle the inverse kinematics problem using brain-inspired neuronal architectures.

Left-prefrontal alpha-dynamics predict executive working-memory functioning in elderly people.

Recent findings suggest that electroencephalography (EEG) oscillations in the theta and alpha frequency-bands reflect synchronized interregional neuronal activity and are considered to reflect cognitive-control, and executive working memory mechanisms in humans. Above the age of 50 years, hypothesized pronounced alterations in alpha and theta-band power at resting or across different WM-functioning brain states may well be due to pre-dementia cognitive impairments, or increasing severity of age-related neurological disorders. Executive working memory (EWM) functioning was assessed in older-adult participants (54 to 83 years old) by obtaining their WM-related EEG oscillations and WM performance scores. WM performance and WM brain-state EEG were recorded during online-WM periods as well as during specific online WM events within EWM periods, and during resting offline-WM periods that preceded online-WM periods. Left-prefrontal alpha-power was enhanced during offline-WM periods versus online-WM periods and was significantly related to WM accuracy. Left-prefrontal alpha power and left prefrontal-parietal theta power anterior-posterior difference-gradient during online WM activity were related to reaction times (RT's). Importantly, during active-storage events, WM-offset offline-periods, and preparatory pre-retrieval events, excessive left-prefrontal alpha activity was related to poor EWM performance. The potential for developing targeted noninvasive cognition-enhancing interventions and developing clinical-monitoring EEG-based biomarkers of pathological cognitive-decline in elderly people is discussed.

Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.

Inverse kinematics is fundamental for computational motion planning. It is used to derive an appropriate state in a robot's configuration space, given a target position in task space. In this work, we investigate the performance of fully connected and residual artificial neural networks as well as recurrent, learning-based, and deep spiking neural networks for conventional and geometrically constrained inverse kinematics. We show that while highly parameterized data-driven neural networks with tens to hundreds of thousands of parameters exhibit sub-ms inference time and sub-mm accuracy, learning-based spiking architectures can provide reasonably good results with merely a few thousand neurons. Moreover, we show that spiking neural networks can perform well in geometrically constrained task space, even when configured to an energy-conserved spiking rate, demonstrating their robustness. Neural networks were evaluated on NVIDIA's Xavier and Intel's neuromorphic Loihi chip.

Adaptive control of a wheelchair mounted robotic arm with neuromorphically integrated velocity readings and online-learning.

Wheelchair-mounted robotic arms support people with upper extremity disabilities with various activities of daily living (ADL). However, the associated cost and the power consumption of responsive and adaptive assistive robotic arms contribute to the fact that such systems are in limited use. Neuromorphic spiking neural networks can be used for a real-time machine learning-driven control of robots, providing an energy efficient framework for adaptive control. In this work, we demonstrate a neuromorphic adaptive control of a wheelchair-mounted robotic arm deployed on Intel's Loihi chip. Our algorithm design uses neuromorphically represented and integrated velocity readings to derive the arm's current state. The proposed controller provides the robotic arm with adaptive signals, guiding its motion while accounting for kinematic changes in real-time. We pilot-tested the device with an able-bodied participant to evaluate its accuracy while performing ADL-related trajectories. We further demonstrated the capacity of the controller to compensate for unexpected inertia-generating payloads using online learning. Videotaped recordings of ADL tasks performed by the robot were viewed by caregivers; data summarizing their feedback on the user experience and the potential benefit of the system is reported.

Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation.

Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence. The Neural Engineering Framework (NEF) brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons to implement functional large-scale neural networks. Here, we present OZ, a programable analog implementation of NEF-inspired spiking neurons. OZ neurons can be dynamically programmed to feature varying high-dimensional response curves with positive and negative encoders for a neuromorphic distributed representation of normalized input data. Our hardware design demonstrates full correspondence with NEF across firing rates, encoding vectors, and intercepts. OZ neurons can be independently configured in real-time to allow efficient spanning of a representation space, thus using fewer neurons and therefore less power for neuromorphic data representation.

Neuromorphic NEF-Based Inverse Kinematics and PID Control.

Neuromorphic implementation of robotic control has been shown to outperform conventional control paradigms in terms of robustness to perturbations and adaptation to varying conditions. Two main ingredients of robotics are inverse kinematic and Proportional-Integral-Derivative (PID) control. Inverse kinematics is used to compute an appropriate state in a robot's configuration space, given a target position in task space. PID control applies responsive correction signals to a robot's actuators, allowing it to reach its target accurately. The Neural Engineering Framework (NEF) offers a theoretical framework for a neuromorphic encoding of mathematical constructs with spiking neurons for the implementation of functional large-scale neural networks. In this work, we developed NEF-based neuromorphic algorithms for inverse kinematics and PID control, which we used to manipulate 6 degrees of freedom robotic arm. We used online learning for inverse kinematics and signal integration and differentiation for PID, offering high performing and energy-efficient neuromorphic control. Algorithms were evaluated in simulation as well as on Intel's Loihi neuromorphic hardware.

Antagonistic Center-Surround Mechanisms for Direction Selectivity in the Retina.

An antagonistic center-surround receptive field is a key feature in sensory processing, but how it contributes to specific computations such as direction selectivity is often unknown. Retinal On-starburst amacrine cells (SACs), which mediate direction selectivity in direction-selective ganglion cells (DSGCs), exhibit antagonistic receptive field organization: depolarizing to light increments and decrements in their center and surround, respectively. We find that a repetitive stimulation exhausts SAC center and enhances its surround and use it to study how center-surround responses contribute to direction selectivity. Center, but not surround, activation induces direction-selective responses in SACs. Nevertheless, both SAC center and surround elicited direction-selective responses in DSGCs, but to opposite directions. Physiological and modeling data suggest that the opposing direction selectivity can result from inverted temporal balance between excitation and inhibition in DSGCs, implying that SAC's response timing dictates direction selectivity. Our findings reveal antagonistic center-surround mechanisms for direction selectivity and demonstrate how context-dependent receptive field reorganization enables flexible computations.

Distributed retrieval engine for the development of cloud-deployed biological databases.

The integration of cloud resources with federated data retrieval has the potential of improving the maintenance, accessibility and performance of specialized databases in the biomedical field. However, such an integrative approach requires technical expertise in cloud computing, usage of a data retrieval engine and development of a unified data-model, which can encapsulate the heterogeneity of biological data. Here, a framework for the development of cloud-based biological specialized databases is proposed. It is powered by a distributed biodata retrieval system, able to interface with different data formats, as well as provides an integrated way for data exploration. The proposed framework was implemented using Java as the development environment, and MongoDB as the database manager. Syntactic analysis was based on BSON, jsoup, Apache Commons and w3c.dom open libraries. Framework is available in: http://nbel-lab.com and is distributed under the creative common agreement.

Rapid development of entity-based data models for bioinformatics with persistence object-oriented design and structured interfaces.

Databases are imperative for research in bioinformatics and computational biology. Current challenges in database design include data heterogeneity and context-dependent interconnections between data entities. These challenges drove the development of unified data interfaces and specialized databases. The curation of specialized databases is an ever-growing challenge due to the introduction of new data sources and the emergence of new relational connections between established datasets. Here, an open-source framework for the curation of specialized databases is proposed. The framework supports user-designed models of data encapsulation, objects persistency and structured interfaces to local and external data sources such as MalaCards, Biomodels and the National Centre for Biotechnology Information (NCBI) databases. The proposed framework was implemented using Java as the development environment, EclipseLink as the data persistency agent and Apache Derby as the database manager. Syntactic analysis was based on J3D, jsoup, Apache Commons and w3c.dom open libraries. Finally, a construction of a specialized database for aneurysms associated vascular diseases is demonstrated. This database contains 3-dimensional geometries of aneurysms, patient's clinical information, articles, biological models, related diseases and our recently published model of aneurysms' risk of rapture. Framework is available in: http://nbel-lab.com.

Microfluidic Concentric Gradient Generator Design for High-Throughput Cell-Based Studies.

Gradients of diffusible signaling molecules play important role in various processes, ranging from cell differentiation to toxicological evaluation. Microfluidic technology provides an accurate control of tempospatial conditions. However, current microfluidic platforms are not designed to handle multiple gradients and cell populations simultaneously. Here, we demonstrate a rapidly adaptable microfluidic design able to expose multiple cell populations to an array of chemical gradients. Our design is based on pressure-equilibrated concentric channels and a pressure-dissipating control layer, facilitating the seeding of multiple cell populations in a single device. The design was numerically evaluated and experimentally validated. The device consists of 8 radiating stimuli channels and 12 circular cell culture channels, creating an array of 96 different continuous gradients that can be simultaneously monitored over time.