SAM: a unified self-adaptive multicompartmental spiking neuron model for learning with working memory

Working memory is a fundamental feature of biological brains for perception, cognition, and learning. In addition, learning with working memory, which has been show in conventional artificial intelligence systems through recurrent neural networks, is instrumental to advanced cognitive intelligence. However, it is hard to endow a simple neuron model with working memory, and to understand the biological mechanisms that have resulted in such a powerful ability at the neuronal level. This article presents a novel self-adaptive multicompartment spiking neuron model, referred to as SAM, for spike-based learning with working memory. SAM integrates four major biological principles including sparse coding, dendritic non-linearity, intrinsic self-adaptive dynamics, and spike-driven learning. We first describe SAM’s design and explore the impacts of critical parameters on its biological dynamics. We then use SAM to build spiking networks to accomplish several different tasks including supervised learning of the MNIST dataset using sequential spatiotemporal encoding, noisy spike pattern classification, sparse coding during pattern classification, spatiotemporal feature detection, meta-learning with working memory applied to a navigation task and the MNIST classification task, and working memory for spatiotemporal learning. Our experimental results highlight the energy efficiency and robustness of SAM in these wide range of challenging tasks. The effects of SAM model variations on its working memory are also explored, hoping to offer insight into the biological mechanisms underlying working memory in the brain. The SAM model is the first attempt to integrate the capabilities of spike-driven learning and working memory in a unified single neuron with multiple timescale dynamics. The competitive performance of SAM could potentially contribute to the development of efficient adaptive neuromorphic computing systems for various applications from robotics to edge computing

A gaussian process emulator for estimating the volume of tissue activated during deep brain stimulation

The volume of tissue activated (VTA) is a well-established approach to model the direct effects of deep brain stimulation (DBS) on neural tissue and previous studies have pointed to its potential clinical applications. However, the elevated computational time required to estimate the VTA with standard techniques used in biological neural modeling limits its suitability for practical use. The goal of this project was to develop a novel methodology to reduce the computation time of VTA estimation. To that end, we built a Gaussian process emulator. It combines a field of multi-compartment axon models coupled to the stimulating electric field with a Gaussian process classifier (GPC); following the premise that computing the VTA from a field of axons is in essence a binary classification problem. We achieved a considerable reduction in the average time required to estimate the VTA, under both ideal isotropic and realistic anisotropic brain tissue conductive conditions, limiting the loss of accuracy and overcoming other drawbacks entailed by alternative methods

Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density

This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience. It provides an introductory overview on how to account for empirical data in mathematical models, implement them in software, and perform simulations reflecting experiments. This path is demonstrated with respect to four key aspects of synaptic signaling: the connectivity of brain networks, synaptic transmission, synaptic plasticity, and the heterogeneity across synapses. Each step and aspect of the modeling and simulation workflow comes with its own challenges and pitfalls, which are highlighted and addressed in detail.Comment: 35 pages, 3 figure

DEVELOPMENT OF INNOVATIVE MULTICOMPARTMENT MICROFLUIDIC PLATFORMS TO INVESTIGATE TRAUMATIC AXONAL INJURY

Compartmentalization of cell body from the axon of a neuron is an important aspect in studying the influence of microenvironments. Microenvironment is an integral part of neuronal studies involving traumatic axonal injuries (TAI). While TAI is one of the possible outcomes of various forms of traumatic insults to the central nervous system (CNS) and peripheral nervous system (PNS), many of the mechanistic details are still unknown, it can be agreed that the level of injury often dictates the functional deficit. This motivates the question, what is occurring at both the morphological and biomolecular scale in CNS and PNS axons during and throughout the recovery phase after injury? And, are there any treatment strategies that could be applied to improve the recovery and regeneration of the axons subject to TAI? Motivated by this, I propose to develop novel microfluidic platforms as a part of my master’s thesis to allow unprecedented, physiologically relevant focal and graded mechanical injury and observation to both CNS and PNS axons. My research for this thesis can be broadly classified into two fold. 1) I examined the regenerative effects of the members of the Glial cell line-derived neurotrophic factor (GDNF), a family of neurotrophic factors after axotomy. This work resulted in the discovery of the fact that GDNF is the most potent neurotrophic factor among the family of growth factors for axon regeneration in dorsal root ganglion (DRG) neurons after in vitro axotomy. It was also found that GDNF locally applied to cell body better promotes axonal regeneration in comparison to when applied locally to axons. 2) Development and refinement of existing axon injury microplatform (AIM) to closely mimic physiological conditions during traumatic injury in CNS neurons. This work was my attempt in improving already existing microfluidic compression platform. I successfully developed a displacement control injury device and demonstrated displacement control as a proof of principle. Further development of these microfluidic platforms will significantly contribute to the field of basic neuroscience, neurobiology, and biomedical engineering

From multiscale biophysics to digital twins of tissues and organs: future opportunities for in silico pharmacology

With many advancements in in silico biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications. Achieving models that characterize the link of molecular interactions to the activity and structure of a whole organ are termed multiscale biophysics. Historically, the pharmaceutical industry has worked well with in silico models by leveraging their prediction capabilities for drug testing. However, the needed higher fidelity and higher resolution of models for efficient prediction of pharmacological phenomenon dictates that in silico approaches must account for the verifiable multiscale biophysical phenomena, as a spatial and temporal dimension variation for different processes and models. The collection of different multiscale models for different tissues and organs can compose digital twin solutions towards becoming a service for researchers, clinicians, and drug developers. Our paper has two main goals: 1) To clarify to what extent detailed single- and multiscale modeling has been accomplished thus far, we provide a review on this topic focusing on the biophysics of epithelial, cardiac, and brain tissues; 2) To discuss the present and future role of multiscale biophysics in in silico pharmacology as a digital twin solution by defining a roadmap from simple biophysical models to powerful prediction tools. Digital twins have the potential to pave the way for extensive clinical and pharmaceutical usage of multiscale models and our paper shows the basic fundamentals and opportunities towards their accurate development enabling the quantum leaps of future precise and personalized medical software.Comment: 30 pages, 10 figures, 1 tabl

Local signal processing in mouse horizontal cell dendrites

Most neurons in the central nervous system have elaborate dendritic arbours which come in a large variety of sizes and morphologies (Lefebvre et al., 2015). For many decades, dendrites have been thought to simply relay presynaptic signals to the soma and to the axon terminal system by acting as “passive cables”. However, it has become clear that dendrites are capable of much more than passively integrating synaptic input, they can also act independently and modulate presynaptic signals (reviewed by Branco and Häusser, 2010). Dendritic signal processing has been reported to support sophisticated functions in the cortex, hippocampus, and cerebellum as well as in the retina. In the latter case, multiple processing within one dendrite is essential to process considerable amounts of information from the outside world but, at the same time to use space efficiently: The retina needs to be thin and transparent to reduce light scattering within the tissue. Dendritic processing has already been described in inner retinal neurons (Euler et al., 2002; Grimes et al., 2010; Oesch et al., 2005; Sivyer and Williams, 2013). In the outer retina, the horizontal cell (HC) dendrites, which are directly postsynaptic to the cone photoreceptors (cones) have recently been suggested to be plausible candidates for local signal processing (Grassmeyer and Thoreson, 2017; Jackman et al., 2011; Vroman et al., 2014) despite their involvement in global tasks such as contrast enhancement. To test this hypothesis physiologically, I used two-photon imaging to record calcium (Ca2+) signals in cones and HCs, as well as, cone glutamate release in mouse retinal slices. I used green (578 nm) and ultra violet (UV, 360 nm) light stimuli and recorded from different retinal regions to specifically activate different combinations of medium (M-) and short (S-) wavelength-sensitive opsin expressed in cones. This approach allowed to assess if signals from individual cones remain “isolated” within a local dendritic region of a HC, or if they spread across the entire dendritic tree or, in the electrically coupled HC network. In contrast to what one would expect in a purely globally acting HC (network), responses measured in neighbouring HC compartments varied markedly in their chromatic preference suggesting that HC dendrites are able to process cone input in a highly local manner. Moreover, I found local HC feedback to play a role in shaping the temporal properties of cone output

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201