An Overview of In Vitro Biological Neural Networks for Robot Intelligence

Abstract

In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc. This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence. In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots. Next, we separate the intelligent behaviors into 2 parts according to whether they rely solely on the computing capacity (computing capacity-dependent) or depend also on the network plasticity (network plasticity-dependent), which are then expounded respectively, with a focus on those related to the realization of robot intelligence. Finally, the development trends and challenges of the BNN-based neurorobotic systems are discussed.

Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing

“…For small-scale synthetic data, many specialized model architectures proved to be effective on SCAN like tasks Russin et al, 2019;Gordon et al, 2020;Lake, 2019;Liu et al, 2020a;Nye et al, 2020;Chen et al, 2020). To also address natural language variations in non-synthetic tasks, some recent works exploit structure of the source input and its relation to the target side (Herzig and Berant, 2021;Weißenhorn et al, 2022), and employ sourceside parsing that can be computationally demanding for long sentences, and may have coverage challenge and not available in all languages; while we try to exploit target-side structure only for higher efficiency.…”

“…To improve compositional generalization, many recent studies propose specially-designed model architectures targeting SCAN-like synthetic datasets, and some have achieved perfect accuracy on some of these datasets Russin et al, 2019;Gordon et al, 2020;Lake, 2019;Liu et al, 2020a;Nye et al, 2020;Chen et al, 2020). However, most of these approaches have only been tested on synthetic datasets, which are constructed for a specific purpose and cannot represent the full range of challenges that a real-world semantic parser must address.…”

The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks

“…[36]) and (ii) due to external electrical stimulation, especially spatially local one, when only a part of the network is stimulated [37][38][39][40][41]. The latter is standardly used in attempts to create a hybrid bio-electronic control system for a mobile robot (e.g., [42][43][44], reviewed in [45,46]).…”

TPP-Based Microfluidic Chip Design and Fabrication Method for Optimized Nerve Cells Directed Growth

“…Cell growth regulation is one of the most promising methods in biomedical research such as tissue engineering [ 1 ], cancer research [ 2 ], and neuroscience [ 3 ]. As a typical cell growth regulation method, the directional growth of nerve cells has special significance for neurological medical research, especially for mechanism [ 4 ], intervention strategies [ 5 ], and drug testing [ 6 ] research of central nervous system (CNS) diseases (such as Alzheimer's disease, Huntington’s disease, and Parkinson’s disease).…”