From a Sociological Given Context to Changing Practice: Transforming Problematic Power Relations in Educational Organizations to Overcome Social Inequalities.

In 2012, the international PISA survey reinforced the observation that the French educational system is one of the most unequal among OECD countries. The observation of serious inequalities in access to educational success for pupils from disadvantaged backgrounds could lead to a pessimistic vision suggesting that any possibility of transformation of the system is doomed to failure. Thus, the fight against inequalities in access to educational success is a form of runaway object which constitutes a challenge for research which treats the social context as evolving and susceptible to significant and novel transformations. Developmental work research aims to support the work of professionals in the re-elaboration of their practices by seeking to go beyond the status quo of an unequal school. Drawing on this framework within an institutional network of schools, we seek to show how the intervention has highlighted power issues inscribed in the structures and how the actors, through their commitment in the research collaborative process, seek to go beyond the power issues inscribed in their work routines and enacted during the research process by different kinds of antagonism. We will argue that the fight against educational inequality involves overcoming systemic power relations crystallized in institution. This systemic power is expressed by a form of episodic power. Our results show restrictive and constructive effect on the expansive learning process and on the construction of a collective in the formative interventions. The restrictive side of epistemic power should be linked to systemic power which is historically inherited. We discuss the results in the light of the emergence of a fourth generation of activity theory. Our research makes it possible to make conceptual and methodological progress in the construction of a fourth generation of activity theory by showing the need for analysis and expansively learn about problematic power relations in heterogeneous collectives.

Learning through ferroelectric domain dynamics in solid-state synapses.

In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.

Plasticity in memristive devices for spiking neural networks.

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Tunable neuromimetic integrated system for emulating cortical neuron models.

Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin-Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called "dynamic-clamp," that consists of connecting artificial and biological neurons to study the function of neuronal circuits.

Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity.

We introduce and test a system for simulating networks of conductance-based neuron models using analog circuits. At the single-cell level, we use custom-designed analog circuits (ASICs) that simulate two types of spiking neurons based on Hodgkin-Huxley like dynamics: "regular spiking" excitatory neurons with spike-frequency adaptation, and "fast spiking" inhibitory neurons. Synaptic interactions are mediated by conductance-based synaptic currents described by kinetic models. Connectivity and plasticity rules are implemented digitally through a real time interface between a computer and a PCI board containing the ASICs. We show a prototype system of a few neurons interconnected with synapses undergoing spike-timing dependent plasticity (STDP), and compare this system with numerical simulations. We use this system to evaluate the effect of parameter dispersion on the behavior of small circuits of neurons. It is shown that, although the exact spike timings are not precisely emulated by the ASIC neurons, the behavior of small networks with STDP matches that of numerical simulations. Thus, this mixed analog-digital architecture provides a valuable tool for real-time simulations of networks of neurons with STDP. They should be useful for any real-time application, such as hybrid systems interfacing network models with biological neurons.