Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations

In this paper, we describe a new neuro-inspired, hardware-friendly readout stage for the liquid state machine (LSM), a popular model for reservoir computing. Compared to the parallel perceptron architecture trained by the p-delta algorithm, which is the state of the art in terms of performance of readout stages, our readout architecture and learning algorithm can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation. Inspired by the nonlinear properties of dendrites in biological neurons, our readout stage incorporates neurons having multiple dendrites with a lumped nonlinearity. The number of synaptic connections on each branch is significantly lower than the total number of connections from the liquid neurons and the learning algorithm tries to find the best 'combination' of input connections on each branch to reduce the error. Hence, the learning involves network rewiring (NRW) of the readout network similar to structural plasticity observed in its biological counterparts. We show that compared to a single perceptron using analog weights, this architecture for the readout can attain, even by using the same number of binary valued synapses, up to 3.3 times less error for a two-class spike train classification problem and 2.4 times less error for an input rate approximation task. Even with 60 times larger synapses, a group of 60 parallel perceptrons cannot attain the performance of the proposed dendritically enhanced readout. An additional advantage of this method for hardware implementations is that the 'choice' of connectivity can be easily implemented exploiting address event representation (AER) protocols commonly used in current neuromorphic systems where the connection matrix is stored in memory. Also, due to the use of binary synapses, our proposed method is more robust against statistical variations.Comment: 14 pages, 19 figures, Journa

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Parametric, Optimization-Based Study on the Feasibility of a Multisegment Antisolvent Crystallizer for in Situ Fines Removal and Matching of Target Size Distribution

Peer reviewedPostprin

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

Scalable Digital Architecture of a Liquid State Machine

Liquid State Machine (LSM) is an adaptive neural computational model with rich dynamics to process spatio-temporal inputs. These machines are extremely fast in learning because the goal-oriented training is moved to the output layer, unlike conventional recurrent neural networks. The capability to multiplex at the output layer for multiple tasks makes LSM a powerful intelligent engine. These properties are desirable in several machine learning applications such as speech recognition, anomaly detection, user identification etc. Scalable hardware architectures for spatio-temporal signal processing algorithms like LSMs are energy efficient compared to the software implementations. These designs can also naturally adapt to dierent temporal streams of inputs. Early literature shows few behavioral models of LSM. However, they cannot process real time data either due to their hardware complexity or xed design approach. In this thesis, a scalable digital architecture of an LSM is proposed. A key feature of the architecture is a digital liquid that exploits spatial locality and is capable of processing real time data. The quality of the proposed LSM is analyzed using kernel quality, separation property of the liquid and Lyapunov exponent. When realized using TSMC 65nm technology node, the total power dissipation of the liquid layer, with 60 neurons, is 55.7 mW with an area requirement of 2 mm^2. The proposed model is validated for two benchmark. In the case of an epileptic seizure detection an average accuracy of 84% is observed. For user identification/authentication using gait an average accuracy of 98.65% is achieved

Deep Liquid State Machines with Neural Plasticity and On-Device Learning

The Liquid State Machine (LSM) is a recurrent spiking neural network designed for efficient processing of spatio-temporal streams of information. LSMs have several inbuilt features such as robustness, fast training and inference speed, generalizability, continual learning (no catastrophic forgetting), and energy efficiency. These features make LSM’s an ideal network for deploying intelligence on-device. In general, single LSMs are unable to solve complex real-world tasks. Recent literature has shown emergence of hierarchical architectures to support temporal information processing over different time scales. However, these approaches do not typically investigate the optimum topology for communication between layers in the hierarchical network, or assume prior knowledge about the target problem and are not generalizable. In this thesis, a deep Liquid State Machine (deep-LSM) network architecture is proposed. The deep-LSM uses staggered reservoirs to process temporal information on multiple timescales. A key feature of this network is that neural plasticity and attention are embedded in the topology to bolster its performance for complex spatio-temporal tasks. An advantage of the deep-LSM is that it exploits the random projection native to the LSM as well as local plasticity mechanisms to optimize the data transfer between sequential layers. Both random projections and local plasticity mechanisms are ideal for on-device learning due to their low computational complexity and the absence of backpropagating error. The deep-LSM is deployed on a custom learning architecture with memristors to study the feasibility of on-device learning. The performance of the deep-LSM is demonstrated on speech recognition and seizure detection applications

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

QSPR modeling of selectivity at infinite dilution of ionic liquids

PTDC/EQU-EQU/30060/2017 UIDB/50006/2020 UIDP/50006/2020The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.publishersversionpublishe