Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention as a means to develop "biologically plausible" machine learning models. These networks mimic synaptic connections in the human brain and produce spike trains, which can be approximated by binary values, precluding high computational cost with floating-point arithmetic circuits. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. In this paper, the feasibility of using a convolutional spiking neural network (CSNN) as a classifier to detect anticipatory slow cortical potentials related to braking intention in human participants using an electroencephalogram (EEG) was studied. The EEG data was collected during an experiment wherein participants operated a remote controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were then measured using an EEG. The CSNN's performance was compared to a standard convolutional neural network (CNN) and three graph neural networks (GNNs) via 10-fold cross-validation. The results showed that the CSNN outperformed the other neural networks.Comment: 14 pages, 6 figures, Scientific Reports submissio

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Connecting the Brain to Itself through an Emulation.

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions

Cortical motor prosthetics: the development and use for paralysis

The emerging research field of Brain Computer Interfaces (BCIs) has created an invasive type of BCI, the Cortical Motor Prosthetic (CMP) or invasive BCI (iBCI). The goal is to restore lost motor function via prosthetic control signals to individuals who have long-term paralysis. The development of the CMP consists of two major entities: the implantable, chronic microelectrode array (MEA) and the data acquisition hardware (DAQ) specifically the decoder. The iBCI's function is to record primary motor cortex (M1) neural signals via chronic MEA and translate into a motor command via decoder extraction algorithms that can control a prosthetic to perform the intended movement. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain lost motor functioning. Thus, the iBCI is a beacon of hope that could enable individuals to independently perform daily activities and interact once again with their environment. This review seeks to accomplish two major goals. First, elaborate upon the development of the iBCI and focus on the advancements and efforts to create a viable system. Second, illustrate the exciting improvements in the iBCI's use for reaching and grasping actions and in human clinical trials. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain movement control. Despite the promise in the iBCI, many challenges, which are described in this review, persist and must be overcome before the iBCI can be a viable tool for individuals with long-term. iBCI future endeavors aim to overcome the challenges and develop an efficient system enhancing the lives of many living with paralysis. Standard terms: Intracortical Brain Computer Interface (iBCI), Intracortical Brain Machine Interface (iBMI), Cortical Motor Prosthetic (CMP), Neuromotor Prostheses (NMP), Intracortical Neural Prosthetics, Invasive Neural Prosthetic all terms used interchangeabl

Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control

WOS: 000370402900001PubMed ID: 26321943In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extra-cellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.Bogazici University BAP Grants [10XD3]; Bogazici University Life Sciences and Technologies Research Center [09K120520]This research was supported by Bogazici University BAP Grants #10XD3 and Bogazici University Life Sciences and Technologies Research Center #09K120520

Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations

SIMPEL: Circuit model for photonic spike processing laser neurons

We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber---a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different laser neuron types found in literature. The development of this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics.Comment: 16 pages, 7 figure

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

It has been widely recognized that closed-loop neuroprosthetic systems achieve more favourable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability and greater embodiment have all been reported in systems utilizing some form of feedback. However the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems

Real-time in vivo optogenetic neuromodulation and multielectrode electrophysiologic recording with NeuroRighter

Optogenetic channels have greatly expanded neuroscience’s experimental capabilities, enabling precise genetic targeting and manipulation of neuron subpopulations in awake and behaving animals. However, many barriers to entry remain for this technology – including low-cost and effective hardware for combined optical stimulation and electrophysiologic recording. To address this, we adapted the open-source NeuroRighter multichannel electrophysiology platform for use in awake and behaving rodents in both open and closed-loop stimulation experiments. Here, we present these cost-effective adaptations, including commercially available LED light sources; custom-made optical ferrules; 3D printed ferrule hardware and software to calibrate and standardize output intensity; and modifications to commercially available electrode arrays enabling stimulation proximally and distally to the recording target. We then demonstrate the capabilities and versatility of these adaptations in several open and closed-loop experiments, demonstrate spectrographic methods of analyzing the results, as well as discuss artifacts of stimulation.Emory University. School of Medicine (Emory Neurosciences Initiative seed grant)American Epilepsy SocietyEpilepsy Foundation of America (Predoctoral fellowship)National Science Foundation (U.S.) (NSF GRFP Fellowship 08-593)National Science Foundation (U.S.) (NSF IGERT Fellowship DGE-0333411)National Science Foundation (U.S.) (NSF EFRI #1238097)National Institutes of Health (U.S.) (NIH 1R01NS079757-01)American Society for Engineering Education (SMART Fellowship