14 papers · ranked by Valyu relevance
Tibin John, Yajun Zhou, Ayman Aljishi, Bastian Rieck + 2 more
Time is a critical component of memory and yet how the hippocampus incorporates temporal information with the sensory contents of memories remains unclear. We hypothesized that the hippocampus can learn arbitrary sequences through rapid changes in the tuning and population geometry of individual neurons to mirror the…
I. Cone, H. Z. Shouval
The ability to express and learn temporal sequences is an essential part of learning and memory. Learned temporal sequences are expressed in multiple brain regions and as such there may be common design in the circuits that mediate it. This work proposes a substrate for such representations, via a biophysically…
Yiren Ren, Vishwadeep Ahluwalia, Claire Arthur, Thackery Brown
Statistical learning—the ability to extract patterns from noisy continuous experiences—is fundamental to human cognition. Yet, how contextual factors shape this process remains poorly understood. Music is an important example of such contextual factors, because it is ubiquitous in human experience and provides a rich…
Ai-Su Li, Jan Theeuwes, Dirk van Moorselaar
Through statistical learning, humans are able to extract temporal regularities, using the past to predict the future. Evidence suggests that learning relational structures makes it possible to anticipate the imminent future; yet, the neural dynamics of predicting the future and its time-course remain elusive. To…
Xianhui He, Philipp K. Büchel, Simon Faghel-Soubeyrand, Janina Klingspohr + 2 more
Experiences reshape our internal representations of the world. However, the neural and cognitive dynamics of this process are largely unknown. Here, we investigated how sequence learning reorganizes neural representations and how sleep-dependent consolidation contributes to this transformation. Using high-density…
Kristjan Kalm, Dennis Norris
We contrast two accounts of how novel sequences are learned. The first is that learning changes the signal-to-noise ratio (SNR) of existing neural representations by reducing noise or increasing signal gain. Alternatively, learning might cause the initial representation of the sequence to be recoded into more efficient…
Charlotte Volk, Christopher C. Pack, Shahab Bakhtiari
Generalization of visual perceptual learning (VPL) to unseen conditions varies across tasks. Previous work suggests that training curriculum may be integral to generalization, yet a theoretical explanation is lacking. We propose an explanatory theory of visual learning generalization and curriculum effects by…
S. Coppolino, M. Migliore
In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce…
Linxing Preston Jiang, Rajesh P. N. Rao
We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the cortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form…
Barna Zajzon, Renato Duarte, Abigail Morrison
To acquire statistical regularities from the world, the brain must reliably process, and learn from, spatiotemporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in…
Noémi Éltető, Dezső Nemeth, Karolina Janacsek, Peter Dayan
Humans can implicitly learn complex perceptuo-motor skills over the course of large numbers of trials. This likely depends on our becoming better able to take advantage of ever richer and temporally deeper predictive relationships in the environment. Here, we offer a novel characterization of this process, fitting a…
Matthew Farrell, Cengiz Pehlevan
Understanding how neural circuits generate sequential activity is a longstanding challenge. While foundational theoretical models have shown how sequences can be stored as memories with Hebbian plasticity rules, these models considered only a narrow range of Hebbian rules. Here we introduce a model for arbitrary…
Michael Bale, Malamati Bitzidou, Anna Pitas, Leonie Brebner + 4 more
The world around us is replete with stimuli that unfold over time. When we hear an auditory stream like music or speech or scan a texture with our fingertip, physical features in the stimulus are concatenated in a particular order, and this temporal patterning is critical to interpreting the stimulus. To explore the…
Michael J. Lee, James J. DiCarlo
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming…