Convergence

…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.

…with existing approaches, and analyses convergence to explain the network's capacity advantages and efficient implementation.

…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

Emotions significantly shape behavior, with mood---a prolonged emotional state---affecting decision-making processes by biasing perceptions of future outcomes. Recent studies suggest that mood functions as an extrapolation mechanism, tracking deviations between recent experiences and expectations to predict and inform future behavior. Understanding this mechanism has important implications for both biological systems and artificial agents. In this study, we incorporate a mood-like mechanism, modeled as momentum, into the policy dynamics of a Reinforcement Learning (RL) agent. Specifically, we

…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