View PDF ↗
PDF Viewer

Loading PDF...

This may take a moment

Open Full PDF

BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

Founder's Pitch

"Exploring second-order dynamics and state leakage in RNNs to enhance hippocampal-like replay for complex path integration tasks."

Neuroscience AIScore: 2View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

0/4 signals

0

Quick Build

1/4 signals

2.5

Series A Potential

0/4 signals

0

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.

References (95)

[1]
Sufficient conditions for offline reactivation in recurrent neural networks
2025Nanda H Krishna, C. Bredenberg et al.
[2]
Dynamics of Adaptive Continuous Attractor Neural Networks
2024Yujun Li, Tianhao Chu et al.
[3]
Learning grid cells by predictive coding
2024Mufeng Tang, Helen Barron et al.
[4]
Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences
2024Toshitake Asabuki, T. Fukai
[5]
Sequential predictive learning is a unifying theory for hippocampal representation and replay
2024Daniel Levenstein, A. Efremov et al.
[6]
On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding
2024Dehong Xu, Ruiqi Gao et al.
[7]
Firing rate adaptation affords place cell theta sweeps, phase precession, and procession
2024Tianhao Chu, Zilong Ji et al.
[8]
Preparatory activity and the expansive null-space
2024M. Churchland, K. Shenoy
[9]
Predictive sequence learning in the hippocampal formation
2024Yusi Chen, Huanqiu Zhang et al.
[10]
Explaining the flaws in human random generation as local sampling with momentum
2024Lucas Castillo, Pablo León-Villagrá et al.
[11]
Biologically-Plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-Encoded Distributions
2024P. M. Furlong, Kathryn Simone et al.
[12]
RatInABox: A toolkit for modelling locomotion and neuronal activity in continuous environments
2023Tom M George, Mehul Rastogi et al.
[13]
Neural Sampling in Hierarchical Exponential-family Energy-based Models
2023Xingsi Dong, Si Wu
[14]
Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors
2023Huilin Zhao, Sungchil Yang et al.
[15]
Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule
2023M. Saponati, M. Vinck
[16]
A Survey on Masked Autoencoder for Visual Self-supervised Learning
2023Chaoning Zhang, Chenshuang Zhang et al.
[17]
Self-Consuming Generative Models Go MAD
2023Sina Alemohammad, Josue Casco-Rodriguez et al.
[18]
Barcoding of episodic memories in the hippocampus of a food-caching bird
2023Selmaan N. Chettih, Emily L. Mackevicius et al.
[19]
Sequential Memory with Temporal Predictive Coding
2023Mufeng Tang, Helen C. Barron et al.
[20]
Emergence of a predictive model in the hippocampus.
2023Adam M. P. Miller, Alexander D. Jacob et al.

Showing 20 of 95 references