Topic: divergence

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

Across evolution and, in particular, in brain evolutionary development we can observe how diverse adaptive biological mechanisms are displayed as a solution to environmental demands. In this talk, I will discuss some examples of emerging evolutionary developmental strategies allowing to increase brain computational capacities and how neurodevelopmental conservation, divergence, and convergence would inspire AI systems optimization.