Language attitudes, linguistic authority and independence in 21st century Catalonia

peer-reviewedIn a context of increasing linguistic and cultural diversity and political uncertainty in Catalonia, this article reports on a research project which set out to explore the attitudes of members of independence organisations operating in the city of Girona toward the Catalan and Spanish languages. This study approaches language attitudes through the theoretical lens of linguistic authority, in particular, the concepts of anonymity and authenticity. The data, gathered from six focus groups, provide an insight on the nature of linguistic authority in contemporary Catalonia. Two themes emerge in the informants’ discussion of Catalan and Spanish: ‘twenty-first Century Catalanisme’ and ‘Embracing Linguistic Diversity’. The comments of the respondents indicate that, against the backdrop of the independence process in the region, bilingualism and multilingualism have become highly valued in the territory. In addition, this study suggests that a fuller understanding of the situation in Catalonia may be facilitated by qualitative approaches, which explore attitudes in-depth

LII.—On the development of the pedipalpi

Volume: 10Start Page: 419End Page: 42

Hybrid neuromorphic circuits exploiting non-conventional properties of RRAM for massively parallel local plasticity mechanisms

Recurrent neural networks are currently subject to intensive research efforts to solve temporal computing problems. Neuromorphic processors (NPs), composed of networked neuron and synapse circuit models, natively compute in time and offer an ultralow power solution particularly suited to emerging temporal edge-computing applications (wearable medical devices, for example). The most significant roadblock to addressing useful problems with neuromorphic hardware is the difficulty in maintaining healthy network dynamics in recurrent neural networks. In animal nervous systems, this is achieved via a multitude of adaptive homeostatic mechanisms which act over multiple time scales to counteract network instability induced via drift, component failure, or learning processes such as spike-timing dependent plasticity. One such mechanism is neuronal intrinsic plasticity (IP) where a neuron adapts its parameters which govern its excitability to fire around a target rate. The approach employed in state of the art NPs, based on a central volatile memory remotely setting model parameters, critically constrains parameter variety and bandwidth rendering realization of these essential mechanisms impossible. This paper demonstrates how reconfigurable nonvolatile resistive memories can be incorporated into neuron and synapse circuits allowing memory to be truly colocalized with the computational units in the computing fabric and facilitating the realization of massively parallel local plasticity mechanisms in neuromorphic hardware. Exploiting nonconventional programming operations of HfO2 based RRAM (stochastic SET and the RESET random variable), we propose a technologically plausible IP algorithm and demonstrate its use in the case of a recurrent neural network topology whereby the system self-organizes to sustain stable and healthy network dynamics around a target firing rate