Menander magis orator: η πρόσληψη του Μενάνδρου και η ρητορική παιδεία κατά την πρώιμη αυτοκρατορική περίοδο

Οι κωμωδίες του Μενάνδρου ήταν οι δημοφιλέστερες στους κύκλους των πεπαιδευμένων κατά την αυτοκρατορική εποχή, καθώς χρησιμοποιούνταν ως εργαλείο για την ηθική και ρητορική εκπαίδευση των νέων, ως πρότυπο ηθοποιίας για τους ρήτορες, καθώς και ως μέσο διασκέδασης της μορφωμένης ελίτ στα συμπόσια. Διερευνάται η πρόσληψη της ρητορικής τέχνης και της ηθικοδιδακτικής αξίας του κυριότερου εκπροσώπου της νέας κωμωδίας από Έλληνες και Ρωμαίους συγγραφείς της πρώιμης αυτοκρατορικής περιόδου, οι οποίοι είναι ενεργοί ρήτορες, ρητοροδιδάσκαλοι, φιλόσοφοι, λογοτέχνες, επιστήμονες, που έχουν αφομοιώσει επιτυχώς τη ρητορική εκπαίδευση της εποχής τους, ή θεωρητικοί της ρητορικής. Πρόκειται για τον Διονύσιο Αλικαρνασσέα (c.60 π.Χ.-c.7 μ.Χ.), τον Δημήτριο (c.1ος μ.Χ.) τον Κοϊντιλιανό (c.35-96 μ.Χ.), τον Δίωνα Χρυσόστομο (c.40-c.120 μ.Χ.), τον Πλούταρχο (c.46-c.125 μ.Χ.), τον Επίκτητο (c.50-c.135 μ.Χ.), τον Αίλιο Θέωνα (c.117-c.138 μ.Χ.), τον Αίλιο Αριστείδη (117-c.189 μ.Χ.), τον Λουκιανό (c.120-c.180 μ.Χ.), τον Γαληνό (129-c.199 μ.Χ.) και τον Ερμογένη (c.160-c.225 μ.Χ.). Αρχικά, επιχειρείται να αναδειχθεί ο ρόλος της ηθικής θεματολογίας και των γνωμών του Μενάνδρου στη ρητορική παιδεία και η αξία των μενάνδρειων έργων για τη ρητορική ἠθοποιία και την επίδραση που άσκησαν στις declamations. Έπειτα αντικείμενο πραγμάτευσης αποτελούν οι υφολογικές αρετές του Μενάνδρου (πρέπον / decorum, ἑλληνισμόs / latinitas, σαφήνεια / explanatio, κόσμηση / ornatus), οι μορφές του ύφους του (γοργότης / celeritas, ἀφέλεια / simplicitas) και η ικανότητά του στην υφολογική μίμησιν και εύρεσιν. Επίσης, παρουσιάζονται τα φιλοσοφικά στοιχεία των μενάνδρειων κωμωδιών, που αξιοποιούνταν στο πλαίσιο των διατριβών, και πώς αυτά αξιολογούνται τον 1ο και 2ο αι. μ.Χ., ώστε να αποτελέσουν ένα είδος μενάνδρειας χρηστομάθειας. Τέλος, καταδεικνύεται η αξία του εκλεπτυσμένου χιούμορ της μενάνδρειας κωμωδίας για τη ρητορική. Η πρόσληψη του Μενάνδρου από Έλληνες και Ρωμαίους συγγραφείς της πρώιμης αυτοκρατορικής περιόδου δείχνει ότι οι κωμωδίες του ήταν περισσότερο διαδεδομένες για τη ρητορική και κατ’ επέκταση εκπαιδευτική τους αξία παρά για τη θεατρική ή κωμική τους διάσταση.Comedies of Menander were the most popular within the well educated people during the imperial period, since they were used as a tool for the moral and rhetorical education of young people, as a model of ethopoiia for orators and as a mean of cultivated elite’s entertainment at dinner parties. The present study investigates the reception of rhetorical art and moral value of the major representative of New Comedy from Greek and Roman authors in the early Empire, who were orators, professional teachers of rhetoric, philosophers, satirists and scientists, who had fully absorbed the rhetorical education of their age, or others who had composed rhetorical treatises. They were Dionysius Halicarnasseus (c.60 B.C. - c.7 A.D.) Demetrius (c.1st A.D.), Quintilian (c.35-96 A.D.), Dio Chrysostom (c.40-c.120 A.D.), Plutarch (c.46-c.125 A.D.), Epictetus (c.50-c.135 A.D.), Aelius Theon (c.117-c.138 A.D.), Aelius Aristides (117-c.189 A.D.), Lucian (c.120-c.180 A.D.), Galen (129-c.199 A.D.) and Hermogenes (c.160-c.225 A.D.). First of all, it is attempted to prove the role of moral content and maxims of the menandrian comedies in the rhetorical paideia, their importance about the rhetorical ethopoiia and their influence on declamationes. The next topics of the study are the stylistic virtues of Menander (propriety - πρέπον / decorum, purity - ἑλληνισμόs / latinitas, clarity -σαφήνεια / explanatio, ornament - κατασκευή / ornatus), the types of style (rapidity - γοργότης – celeritas, simplicity - ἀφέλεια / simplicitas) and the menandrian ability in the stylistic imitation (μίμησις / imitatio) and invention (εὕρεσις / inventio). Furthermore, the philosophical elements of menandrian comedies used in philosophical dissertations (diatribe - διατριβὴ) are discussed, as well as the value added to them during the 1st and 2nd century AD, in the purpose of utilizing them as a kind of menandrian chrestomathy. The study finally aims at proving the rhetorical impact of Menander’s refined humor. The reception of Menander from Greek and Roman writers in the early Empire demonstrates that his plays were widespread in the Greco-Roman world more for their rhetorical and therefore educational impact than for their theatrical and comical dimension

Online Spatio-Temporal Learning with Target Projection

Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks. However, BPTT introduces severe limitations, such as the requirement to propagate information backwards through time, the weight symmetry requirement, as well as update-locking in space and time. These problems become roadblocks for AI systems where online training capabilities are vital. Recently, researchers have developed biologically-inspired training algorithms, addressing a subset of those problems. In this work, we propose a novel learning algorithm called online spatio-temporal learning with target projection (OSTTP) that resolves all aforementioned issues of BPTT. In particular, OSTTP equips a network with the capability to simultaneously process and learn from new incoming data, alleviating the weight symmetry and update-locking problems. We evaluate OSTTP on two temporal tasks, showcasing competitive performance compared to BPTT. Moreover, we present a proof-of-concept implementation of OSTTP on a memristive neuromorphic hardware system, demonstrating its versatility and applicability to resource-constrained AI devices.Comment: Accepted at AICAS 202

High-performance deep spiking neural networks with 0.3 spikes per neuron

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks (SNNs) than artificial neural networks (ANNs). This is puzzling given that theoretical results provide exact mapping algorithms from ANNs to SNNs with time-to-first-spike (TTFS) coding. In this paper we analyze in theory and simulation the learning dynamics of TTFS-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of SNN mappings solve this problem at initialization, only the one with a constant slope of the neuron membrane potential at threshold guarantees the equivalence of the training trajectory between SNNs and ANNs with rectified linear units. We demonstrate that training deep SNN models achieves the exact same performance as that of ANNs, surpassing previous SNNs on image classification datasets such as MNIST/Fashion-MNIST, CIFAR10/CIFAR100 and PLACES365. Our SNN accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation. We show that fine-tuning SNNs with our robust gradient descent algorithm enables their optimization for hardware implementations with low latency and resilience to noise and quantization

Efficient Biologically Plausible Adversarial Training

Artificial Neural Networks (ANNs) trained with Backpropagation (BP) show astounding performance and are increasingly often used in performing our daily life tasks. However, ANNs are highly vulnerable to adversarial attacks, which alter inputs with small targeted perturbations that drastically disrupt the models' performance. The most effective method to make ANNs robust against these attacks is adversarial training, in which the training dataset is augmented with exemplary adversarial samples. Unfortunately, this approach has the drawback of increased training complexity since generating adversarial samples is very computationally demanding. In contrast to ANNs, humans are not susceptible to adversarial attacks. Therefore, in this work, we investigate whether biologically-plausible learning algorithms are more robust against adversarial attacks than BP. In particular, we present an extensive comparative analysis of the adversarial robustness of BP and Present the Error to Perturb the Input To modulate Activity (PEPITA), a recently proposed biologically-plausible learning algorithm, on various computer vision tasks. We observe that PEPITA has higher intrinsic adversarial robustness and, with adversarial training, has a more favourable natural-vs-adversarial performance trade-off as, for the same natural accuracies, PEPITA's adversarial accuracies decrease in average by 0.26% and BP's by 8.05%

Dynamic Event-based Optical Identification and Communication

Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic optical flow computed with spiking neurons. The system is embedded in a simulated drone and evaluated in an asset monitoring use case. It is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range.Comment: 10 pages, 7 figures and 1 tabl

Biologically-inspired training of spiking recurrent neural networks with neuromorphic hardware

Recurrent spiking neural networks (SNNs) are inspired by the working principles of biological nervous systems that offer unique temporal dynamics and event-based processing. Recently, the error backpropagation through time (BPTT) algorithm has been successfully employed to train SNNs offline, with comparable performance to artificial neural networks (ANNs) on complex tasks. However, BPTT has severe limitations for online learning scenarios of SNNs where the network is required to simultaneously process and learn from incoming data. Specifically, as BPTT separates the inference and update phases, it would require to store all neuronal states for calculating the weight updates backwards in time. To address these fundamental issues, alternative credit assignment schemes are required. Within this context, neuromorphic hardware (NMHW) implementations of SNNs can greatly benefit from in-memory computing (IMC) concepts that follow the brain-inspired collocation of memory and processing, further enhancing their energy efficiency. In this work, we utilize a biologically-inspired local and online training algorithm compatible with IMC, which approximates BPTT, e-prop, and present an approach to support both inference and training of a recurrent SNN using NMHW. To do so, we embed the SNN weights on an in-memory computing NMHW with phase-change memory (PCM) devices and integrate it into a hardware-in-the-loop training setup. We develop our approach with respect to limited precision and imperfections of the analog devices using a PCM-based simulation framework and a NMHW consisting of in-memory computing cores fabricated in 14nm CMOS technology with 256×256 PCM crossbar arrays. We demonstrate that our approach is robust even to 4-bit precision and achieves competitive performance to a floating-point 32-bit realization, while simultaneously equipping the SNN with online training capabilities and exploiting the acceleration benefits of NMHW

Neuromorphic Optical Flow and Real-time Implementation with Event Cameras

Optical flow provides information on relative motion that is an important component in many computer vision pipelines. Neural networks provide high accuracy optical flow, yet their complexity is often prohibitive for application at the edge or in robots, where efficiency and latency play crucial role. To address this challenge, we build on the latest developments in event-based vision and spiking neural networks. We propose a new network architecture, inspired by Timelens, that improves the state-of-the-art self-supervised optical flow accuracy when operated both in spiking and non-spiking mode. To implement a real-time pipeline with a physical event camera, we propose a methodology for principled model simplification based on activity and latency analysis. We demonstrate high speed optical flow prediction with almost two orders of magnitude reduced complexity while maintaining the accuracy, opening the path for real-time deployments

Neuromorphic Optical Flow and Real-time Implementation with Event Cameras

Optical flow provides information on relative motion that is an important component in many computer vision pipelines. Neural networks provide high accuracy optical flow, yet their complexity is often prohibitive for application at the edge or in robots, where efficiency and latency play crucial role. To address this challenge, we build on the latest developments in event-based vision and spiking neural networks. We propose a new network architecture, inspired by Timelens, that improves the state-of-the-art self-supervised optical flow accuracy when operated both in spiking and non-spiking mode. To implement a real-time pipeline with a physical event camera, we propose a methodology for principled model simplification based on activity and latency analysis. We demonstrate high speed optical flow prediction with almost two orders of magnitude reduced complexity while maintaining the accuracy, opening the path for real-time deployments.Comment: Accepted for IEEE CVPRW, Vancouver 2023. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media. Copyright 2023 IEE