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Repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA.

ABSTRACT: Widely available low-cost electronics encourage the development of open-source tools for neuroscientific research. In recent years, many neuroscientists recognized the open science movement for its potential to stimulate and encourage science that is less focused on money, and more on robustness, validity, questioning and understanding. Here, we wanted to contribute to this global community by creating a research platform based on a common digital kitchen scale. This everyday ordinary kitchen tool is sometimes used in neuroscience research in various ways; however, its use is limited by sampling rate and inability to store and analyze data. To tackle this problem we developed a Platform for Acoustic STArtle or PASTA. This robust and simple platform enables users to obtain data from kitchen scale load cells at a high sampling rate, store it and analyze it. Here, we used it to analyze acoustic startle and prepulse inhibition sensorimotor gating in rats treated intracerebroventricularly with streptozotocin, but the system can be easily modified and upgraded for other purposes. In accordance with open science principles, we shared complete hardware design with instructions. Furthermore, we also disclose our software codes written for PASTA data acquisition (C++, Arduino) and acoustic startle experimental protocol (Python) and analysis (ratPASTA R package-R-based Awesome Toolbox for PASTA, and pastaWRAP-Python wrapper package for ratPASTA). To further encourage the development of our PASTA platform we demonstrate its sensitivity by using PASTA-gathered data to extract breathing patterns during rat freezing behavior in our experimental protocol.

SUBMITTER: Virag D

PROVIDER: S-EPMC7858584 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA.

Virag D D Homolak J J Kodvanj I I Babic Perhoc A A Knezovic A A Osmanovic Barilar J J Salkovic-Petrisic M M

Scientific reports 20210203 1

Widely available low-cost electronics encourage the development of open-source tools for neuroscientific research. In recent years, many neuroscientists recognized the open science movement for its potential to stimulate and encourage science that is less focused on money, and more on robustness, validity, questioning and understanding. Here, we wanted to contribute to this global community by creating a research platform based on a common digital kitchen scale. This everyday ordinary kitchen to ...[more]

PMID: 33536528

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Project description:The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and with low power consumption. Real-time performance is achieved with 1 ms integration time steps, and thus applies to neural networks for which faster time scales of the dynamics can be neglected. By slowing down the simulation, shorter integration time steps and hence faster time scales, which are often biologically relevant, can be incorporated. We here describe the first full-scale simulations of a cortical microcircuit with biological time scales on SpiNNaker. Since about half the synapses onto the neurons arise within the microcircuit, larger cortical circuits have only moderately more synapses per neuron. Therefore, the full-scale microcircuit paves the way for simulating cortical circuits of arbitrary size. With approximately 80, 000 neurons and 0.3 billion synapses, this model is the largest simulated on SpiNNaker to date. The scale-up is enabled by recent developments in the SpiNNaker software stack that allow simulations to be spread across multiple boards. Comparison with simulations using the NEST software on a high-performance cluster shows that both simulators can reach a similar accuracy, despite the fixed-point arithmetic of SpiNNaker, demonstrating the usability of SpiNNaker for computational neuroscience applications with biological time scales and large network size. The runtime and power consumption are also assessed for both simulators on the example of the cortical microcircuit model. To obtain an accuracy similar to that of NEST with 0.1 ms time steps, SpiNNaker requires a slowdown factor of around 20 compared to real time. The runtime for NEST saturates around 3 times real time using hybrid parallelization with MPI and multi-threading. However, achieving this runtime comes at the cost of increased power and energy consumption. The lowest total energy consumption for NEST is reached at around 144 parallel threads and 4.6 times slowdown. At this setting, NEST and SpiNNaker have a comparable energy consumption per synaptic event. Our results widen the application domain of SpiNNaker and help guide its development, showing that further optimizations such as synapse-centric network representation are necessary to enable real-time simulation of large biological neural networks.

Dataset Information

Repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA.

Publications

Repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA.

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