Topic: top-down feedback

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding in

Learning in neural networks requires assigning the right values to thousands to trillions or more of individual connections, so that the network as a whole produces the desired behavior. Neuroscientists have gained insights into this “credit assignment” problem through decades of experimental, modeling, and theoretical studies. This has suggested key roles for synaptic eligibility traces and top-down feedback signals, among other factors. Here we study the potential contribution of another type of signaling that is being revealed in greater and greater fidelity by ongoing molecular and genomic

A central problem in biological learning is how information about the outcome of a decision or behavior can be used to reliably guide learning across distributed neural circuits while obeying biological constraints. This “credit assignment” problem is commonly solved in artificial neural networks through supervised gradient descent and the backpropagation algorithm. In contrast, biological learning is typically modelled using unsupervised Hebbian learning rules. While these rules only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflec

The exclusive attribution of working memory (WM) retention to persistent frontal activity [1,2] has been questioned by implications of spike-silent WM [3] in early visual cortex (EVC) [4,5,6]. Spike-silent WM might partly be explained by short-term plasticity (STP) [7], where perceptual encoding leaves a neural trace that can be retrieved using perturbation methods [8,9]. However, STP traces cannot easily withstand continual visual stimulation. In contrast, $modulatory$ top-down feedback [10] (i.e., altering spiking propensity without inducing spiking directly [11]) from frontal regions to EVC