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Current imaginaries for the future of food production are often set on two opposite sides, either digital technologies enabling higher productivity at a large scale, or small-scale diversified farming that minimizes the use of digital technology. However, computational agroecology is starting to explore the space for digital technologies that are adapted to complex agroecosystems. In this paper, we define a specific scale (microfarms) and farming practice (the French Method) on which these tools can be developed and tested. We show how the age old French Method, with its set of constraints, leads to original technologies and we illustrate this with some of the tools we developed. We discuss in particular three aspects: Tools, Plants, and People where computation can interact with farming practices. We also discuss the consequences of introducing digital technologies in microfarms, including potentially harmful ones.

Neuronal adaptation is the intrinsic capacity of the brain to change, by various mechanisms, its dynamical responses as a function of the context. Such a phenomena, widely observed in vivo and in vitro, is known to be crucial in homeostatic regulation of the activity and gain control. The effects of adaptation have already been studied at the single-cell level, resulting from either voltage or calcium gated channels both activated by the spiking activity and modulating the dynamical responses of the neurons. In this study, by disentangling those effects into a linear (sub-threshold) and a non-linear (supra-threshold) part, we focus on the the functional role of those two distinct components of adaptation onto the neuronal activity at various scales, starting from single-cell responses up to recurrent networks dynamics, and under stationary or non-stationary stimulations. The effects of slow currents on collective dynamics, like modulation of population oscillation and reliability of spike patterns, is quantified for various types of adaptation in sparse recurrent networks.

http://conf.lsse.kyutech.ac.jp/~iconip2007/Programs.pdf; International audience; A phenomenological model is developed where complex dynamics are the correlate of spatio-temporal memories. If resting is not a classical fixed point attractor but a Milnor attractor, multiple oscillations appear in the dynamics of a coupled system. This model can be helpful for describing brain activity in terms of well classified dynamics and for implementing human-like real-time computation.

Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is associated with both a slow oscillation (leading to the up and down states) and the presence of the theta rhythm. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks. Here to enlighten neural implication of working memory under these complicated dynamics, we propose a phenomenological network model with biologically plausible neural dynamics and recurrent connections. Each unit embeds an internal oscillation at the theta rhythm which can be triggered during up-state of the membrane potential. As a result, the resting state of a single unit is no longer a classical fixed point attractor but rather the Milnor attractor, and multiple oscillations appear in the dynamics of a coupled system. In conclusion, the interplay between the up and down states and theta rhythm endows high potential in working memory operation associated with complexity in spontaneous activities.