16 papers · ranked by Valyu relevance
Alain de Cheveigné
Predictive coding is an influential concept in sensory and cognitive neuroscience. It is often understood as involving top-down prediction of bottom-up sensory patterns, but the term also applies to feed-forward predictive mechanisms, for example in the retina. Here, I discuss a recent model of low-level predictive…
Alain de Cheveigné
Predictive coding is an influential concept in sensory and cognitive neuroscience. It is often understood as involving top-down prediction of bottom-up sensory patterns, but the term also applies to feed-forward predictive mechanisms, for example in the retina. Here, I discuss a recent model of low-level predictive…
Samer Nour Eddine, Trevor Brothers, Lin Wang, Michael Spratling + 1 more
The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive…
Dirk Gütlin, Ryszard Auksztulewicz
This study explores whether predictive coding (PC) inspired Deep Neural Networks can serve as biologically plausible neural network models of the brain. We compared two PC-inspired training objectives, a predictive and a contrastive approach, to a supervised baseline in a simple Recurrent Neural Network (RNN)…
Qingqing Zhang, Sverre Grødem, Alexa Gracias, Kristian K. Lensjø + 3 more
Predictive coding is a theoretical framework that can explain how animals build internal models of their sensory environments by predicting sensory inputs. Predictive coding may capture either spatial or temporal relationships between sensory objects. While the original theory by Rao and Ballard, 1999 described spatial…
Beren Millidge, Mufeng Tang, Mahyar Osanlouy, Nicol S. Harper + 1 more
One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of…
Srihita Rudraraju, Michael E. Turvey, Bradley H. Theilman, Timothy Q. Gentner
Predictive coding posits that sensory signals are compared to internal models, with resulting prediction-error carried in the spiking responses of single neurons. Despite its proposal as a general cortical mechanism, including for speech processing, whether or how predictive coding functions in single-neuron responses…
Mingfang(Lucy) Zhang, Sander M. Bohte
Predictive coding is a prominent theoretical framework for understanding the hierarchical sensory processing in the brain, yet how it could be implemented in networks of cortical neurons is still unclear. While most existing works have taken a hand-wiring approach to creating microcircuits that match experimental…
Bin Wang, Nicholas J Audette, David M Schneider, Johnatan Aljadeff
Neural circuits construct internal ‘world-models’ to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. Here, to understand how the brain generates predictions for complex…
Amber Marijn Brands, Paulo Ortiz, Iris Isabelle Anna Groen
Predictive coding is a leading theory of cortical function which posits that the brain continually makes predictions of incoming sensory stimuli using a hierarchical network of top-down and bottom-up connections. This theory is supported by prior work showing that PredNet, a deep learning network designed according to…
J. P. Fiorenza, L. Le Donne, C. Schmidt-Samoa, E.-C. Heide + 7 more
Predictive-coding like theories agree in describing top-down communication through the cortical hierarchy as a transmission of predictions generated by internal models of the inputs. With respect to the bottom-up connections, however, these theories differ in the neural processing strategies suggested for updating the…
Ibrahim C. Hashim, Mario Senden, Rainer Goebel
Hierarchical predictive coding (hPC) proposes that the cortex continuously generates predictions of incoming sensory stimuli. Deep neural networks inspired by hPC are frequently used to probe the neurocomputational mechanisms suggested by the theory in silico and to generate hypotheses for experimental investigations.…
Antonino Greco, Clara Rastelli, Leonardo Bonetti, Christoph Braun + 1 more
A longstanding question in cognitive science is whether the human brain learns sensory regularities that are irrelevant to ongoing behavior, a phenomenon known as incidental associative learning. Here, we provide evidence at the single subject level that humans indeed acquired such incidental associations and reveal…
David G. Lee, Caroline A. McLachlan, Ramon Nogueira, Osung Kwon + 6 more
Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying…
Byron H. Price, Cambria M. Jensen, Anthony A. Khoudary, Jeffrey P. Gavornik
Repeated exposure to visual sequences changes the form of evoked activity in the primary visual cortex (V1). Predictive coding theory provides a potential explanation for this, namely that plasticity shapes cortical circuits to encode spatiotemporal predictions and that subsequent responses are modulated by the degree…
Benedetta Candelori, Giampiero Bardella, Indro Spinelli, Surabhi Ramawat + 3 more
Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity remains a challenge, requiring a delicate…