Hardware design of LIF with Latency neuron model with memristive STDP synapses

In this paper, the hardware implementation of a neuromorphic system is presented. This system is composed of a Leaky Integrate-and-Fire with Latency (LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL neuron model allows to encode more information than the common Integrate-and-Fire models, typically considered for neuromorphic implementations. In our system LIFL neuron is implemented using CMOS circuits while memristor is used for the implementation of the STDP synapse. A description of the entire circuit is provided. Finally, the capabilities of the proposed architecture have been evaluated by simulating a motif composed of three neurons and two synapses. The simulation results confirm the validity of the proposed system and its suitability for the design of more complex spiking neural network

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate

Role of the Ventral Tegmental Area and Ventral Tegmental Area Nicotinic Acetylcholine Receptors in the Incentive Amplifying Effect of Nicotine

Nicotine has multiple behavioral effects as a result of its action in the central nervous system. Nicotine strengthens the behaviors that lead to nicotine administration (primary reinforcement), and this effect of nicotine depends on mesotelencephalic systems of the brain that are critical to goal directed behavior, reward, and reinforcement. Nicotine also serves as a ‘reinforcement enhancer’ – drug administration enhances behaviors that lead to other drug and nondrug reinforcers. Although the reinforcement enhancing effects of nicotine may promote tobacco use in the face of associated negative health outcomes, the neuroanatomical systems that mediate this effect of nicotine have never been described. The ventral tegmental area (VTA) is a nucleus that serves as a convergence point in the mesotelencephalic system, plays a substantial role in reinforcement by both drug and nondrug rewards and is rich in both presynaptic and postsynaptic nicotinic acetylcholine receptors (nAChRs). Therefore, these experiments were designed to determine the role of the VTA and nAChR subtypes in the reinforcement enhancing effect of nicotine. Transiently inhibiting the VTA with a gamma amino butyric acid (GABA) agonist cocktail (baclofen and muscimol) reduced both primary reinforcement by a visual stimulus and the reinforcement enhancing effect of nicotine, without producing nonspecific suppression of activity. Intra-VTA infusions of a high concentration of mecamylamine a nonselective nAChR antagonist, or methylycaconitine, an α7 nAChR antagonist, did not reduce the reinforcement enhancing effect of nicotine. Intra-VTA infusions of a low concentration of mecamylamine and dihydro-beta-erythroidine (DHβE), a selective antagonist of nAChRs containing the *β2 subunit, attenuated, but did not abolish, the reinforcement enhancing effect of nicotine. In follow-up tests replacing systemic nicotine injections with intra-VTA infusions (70mM, 105mM) resulted in complete substitution of the reinforcement enhancing effects – increases in operant responding were comparable to giving injections of systemic nicotine. These results suggest that *β2-subunit containing nAChRs in the VTA play a role in the reinforcement enhancing effect of nicotine. However, when nicotine is administered systemically these reinforcement enhancing effects may depend on the action of nicotine at nAChRs in multiple brain nuclei

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

CMOS realization of a quantized-output classifier circuit

In this paper a CMOS implementation of a multi-input data classifier with several output levels and a different architecture is presented. The proposed circuit operates in current-mode and can classify several types of analog vector data. The classifier circuit’s new architecture consists of the interconnections of core cells each possessing a current-voltage converter, an inverter followed by a NOR gate and a voltage-current output stage. Using 0.35µm TSMC technology parameters, SPICE simulation results for a classifier with two inputs are included to verify the expected results

Multiscale computation and dynamic attention in biological and artificial intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence