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SORN: A Self-Organizing Recurrent Neural Network
TLDR
This work introduces SORN, a self-organizing recurrent network that combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. Expand
Untangling Perceptual Memory: Hysteresis and Adaptation Map into Separate Cortical Networks
TLDR
It is found that although affecting the authors' perception concurrently, hysteresis and adaptation map into distinct cortical networks: a widespread network of higher-order visual and fronto-parietal areas was involved in perceptual stabilization, while adaptation was confined to early visual areas. Expand
Fading memory and time series prediction in recurrent networks with different forms of plasticity
TLDR
It is demonstrated that the combination of STDP and IP shapes the network structure and dynamics in ways that allow the discovery of patterns in input time series and lead to good performance in time series prediction. Expand
Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network
TLDR
It is demonstrated that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. Expand
Does the Cerebral Cortex Exploit High-Dimensional, Non-linear Dynamics for Information Processing?
TLDR
A framework is presented which establishes links between the various manifestations of cortical dynamics by assigning specific coding functions to low-dimensional dynamic features such as synchronized oscillations and phase shifts on the one hand and high-dimensional non-linear, non-stationary dynamics on the other. Expand
Stimulus complexity shapes response correlations in primary visual cortex
TLDR
It is argued that, in a functional model of visual perception, featuring probabilistic inference over a hierarchy of features, inferences about high-level features modulate inferences in the fine structure of SCCs as stimulus identity and, more importantly, stimulus complexity varies. Expand
Predictive Coding in Cortical Microcircuits
TLDR
In the presence of structured external input, STDP changes the connectivity matrix of the network such that the recurrent connections capture the particularities of the input stimuli, allowing the network to anticipate future inputs. Expand
Emerging Bayesian Priors in a Self-Organizing Recurrent Network
TLDR
The role of local plasticity rules in learning statistical priors in a self-organizing recurrent neural network (SORN) is explored and a novel connection between low level learning mechanisms and high level concepts of statistical inference is suggested. Expand
Where’s the noise? Key features of neuronal variability and inference emerge from self-organized learning
TLDR
This study demonstrates that the key features of neural variability can be accounted for in a completely deterministic network model through self-organization and shows that the notorious variability in neural recordings does not need to be seen as evidence for a noisy brain. Expand
Orienting Towards Ensembles: From Single Cells to Neural Populations
The tradition of single-cell electrophysiology has taught us a great deal about the response properties of individual cells in primary sensory cortices. However, the study of neurons in isolationExpand
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