A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.

Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency

Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes

There is mounting experimental evidence that brain-state specific neural mechanisms supported by connectomic architectures serve to combine past and contextual knowledge with current, incoming flow of evidence (e.g. from sensory systems). Such mechanisms are distributed across multiple spatial and temporal scales and require dedicated support at the levels of individual neurons and synapses. A prominent feature in the neocortex is the structure of large, deep pyramidal neurons which show a peculiar separation between an apical dendritic compartment and a basal dentritic/peri-somatic compartment, with distinctive patterns of incoming connections and brain-state specific activation mechanisms, namely apical-amplification, -isolation and -drive associated to the wakefulness, deeper NREM sleep stages and REM sleep. The cognitive roles of apical mechanisms have been demonstrated in behaving animals. In contrast, classical models of learning spiking networks are based on single compartment neurons that miss the description of mechanisms to combine apical and basal/somatic information. This work aims to provide the computational community with a two-compartment spiking neuron model which includes features that are essential for supporting brain-state specific learning and with a piece-wise linear transfer function (ThetaPlanes) at highest abstraction level to be used in large scale bio-inspired artificial intelligence systems. A machine learning algorithm, constrained by a set of fitness functions, selected the parameters defining neurons expressing the desired apical mechanisms.Comment: 19 pages, 38 figures, pape

Low-dimensional models of single neurons: A review

The classical Hodgkin-Huxley (HH) point-neuron model of action potential generation is four-dimensional. It consists of four ordinary differential equations describing the dynamics of the membrane potential and three gating variables associated to a transient sodium and a delayed-rectifier potassium ionic currents. Conductance-based models of HH type are higher-dimensional extensions of the classical HH model. They include a number of supplementary state variables associated with other ionic current types, and are able to describe additional phenomena such as sub-threshold oscillations, mixed-mode oscillations (subthreshold oscillations interspersed with spikes), clustering and bursting. In this manuscript we discuss biophysically plausible and phenomenological reduced models that preserve the biophysical and/or dynamic description of models of HH type and the ability to produce complex phenomena, but the number of effective dimensions (state variables) is lower. We describe several representative models. We also describe systematic and heuristic methods of deriving reduced models from models of HH type

In vitro Models for Seizure-Liability Testing Using Induced Pluripotent Stem Cells

The brain is the most complex organ in the body, controlling our highest functions, as well as regulating myriad processes which incorporate the entire physiological system. The effects of prospective therapeutic entities on the brain and central nervous system (CNS) may potentially cause significant injury, hence, CNS toxicity testing forms part of the “core battery” of safety pharmacology studies. Drug-induced seizure is a major reason for compound attrition during drug development. Currently, the rat ex vivo hippocampal slice assay is the standard option for seizure-liability studies, followed by primary rodent cultures. These models can respond to diverse agents and predict seizure outcome, yet controversy over the relevance, efficacy, and cost of these animal-based methods has led to interest in the development of human-derived models. Existing platforms often utilize rodents, and so lack human receptors and other drug targets, which may produce misleading data, with difficulties in inter-species extrapolation. Current electrophysiological approaches are typically used in a low-throughput capacity and network function may be overlooked. Human-derived induced pluripotent stem cells (iPSCs) are a promising avenue for neurotoxicity testing, increasingly utilized in drug screening and disease modeling. Furthermore, the combination of iPSC-derived models with functional techniques such as multi-electrode array (MEA) analysis can provide information on neuronal network function, with increased sensitivity to neurotoxic effects which disrupt different pathways. The use of an in vitro human iPSC-derived neural model for neurotoxicity studies, combined with high-throughput techniques such as MEA recordings, could be a suitable addition to existing pre-clinical seizure-liability testing strategies