Topic: convergence

Develop communication-efficient, privacy-preserving distributed learning algorithms for streaming data in edge inference networks. The PRIDeL collaboration connects Inria, Simula and NYCU, combining theory or experiments on convergence, generalization, data freshness and differential privacy. The doctorate must be defended by 31 December 2026, and the appointment must provide a research environment distinct from the doctorate. The contract begins in France, with international visits encouraged. Appointment: 1 year, 4 months; advertised start 2027-01-01. 2 788 € gross monthly.

Neural circuits are continuously under the influence of multiple chemical neuromodulators. The prevalent view is that neuromodulation increases the flexibility of circuit output. However, different modulators can have overlapping cellular and subcellular targets, and thus convergence and occlusion may limit the repertoire of possible circuit states. We propose a complementary view that convergent comodulation may result in a more consistent circuit activity that, with increasing numbers of modulators, becomes less dependent on the specific identity of the modulators involved. We examine this h

Computation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about the inputs. Past work has detailed how to optimize nonlinearities and circuit weights to maximize information, but we show that selective nonlinearities, acting together with divergent and convergent circuit structure, can improve information transmission over

Sensory neural circuits rely on a common set of motifs to process inputs, including convergence of multiple inputs to a single neuron, divergence of inputs into parallel pathways, and nonlinearities that are selective for some inputs over others. Past work has detailed how optimized response nonlinearities and synaptic weights can maximize encoded information, but these solutions depend on tightly tuned response functions and connectivities. Our study found that incorporating generic, non-invertible, selectivity-inducing nonlinearities into a circuit with divergent and convergent structure can