3,331 research outputs found
Synchronized Oscillations During Cooperative Feature Linking in a Cortical Model of Visual Perception
A neural network model of synchronized oscillator activity in visual cortex is presented in order to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these recent experiments, it was reported that synchronization of oscillatory firing responses to moving bar stimuli occurred not only for nearby neurons, but also occurred between neurons separated by several cortical columns (several mm of cortex) when these neurons shared some receptive field preferences specific to the stimuli. These results were obtained not only for single bar stimuli but also across two disconnected, but colinear, bars moving in the same direction. Our model and computer simulations obtain these synchrony results across both single and double bar stimuli. For the double bar case, synchronous oscillations are induced in the region between the bars, but no oscillations are induced in the regions beyond the stimuli. These results were achieved with cellular units that exhibit limit cycle oscillations for a robust range of input values, but which approach an equilibrium state when undriven. Single and double bar synchronization of these oscillators was achieved by different, but formally related, models of preattentive visual boundary segmentation and attentive visual object recognition, as well as nearest-neighbor and randomly coupled models. In preattentive visual segmentation, synchronous oscillations may reflect the binding of local feature detectors into a globally coherent grouping. In object recognition, synchronous oscillations may occur during an attentive resonant state that triggers new learning. These modelling results support earlier theoretical predictions of synchronous visual cortical oscillations and demonstrate the robustness of the mechanisms capable of generating synchrony.Air Force Office of Scientific Research (90-0175); Army Research Office (DAAL-03-88-K0088); Defense Advanced Research Projects Agency (90-0083); National Aeronautics and Space Administration (NGT-50497
CompILE: Compositional Imitation Learning and Execution
We introduce Compositional Imitation Learning and Execution (CompILE): a
framework for learning reusable, variable-length segments of
hierarchically-structured behavior from demonstration data. CompILE uses a
novel unsupervised, fully-differentiable sequence segmentation module to learn
latent encodings of sequential data that can be re-composed and executed to
perform new tasks. Once trained, our model generalizes to sequences of longer
length and from environment instances not seen during training. We evaluate
CompILE in a challenging 2D multi-task environment and a continuous control
task, and show that it can find correct task boundaries and event encodings in
an unsupervised manner. Latent codes and associated behavior policies
discovered by CompILE can be used by a hierarchical agent, where the high-level
policy selects actions in the latent code space, and the low-level,
task-specific policies are simply the learned decoders. We found that our
CompILE-based agent could learn given only sparse rewards, where agents without
task-specific policies struggle.Comment: ICML (2019
Optimal stimulation protocol in a bistable synaptic consolidation model
Consolidation of synaptic changes in response to neural activity is thought
to be fundamental for memory maintenance over a timescale of hours. In
experiments, synaptic consolidation can be induced by repeatedly stimulating
presynaptic neurons. However, the effectiveness of such protocols depends
crucially on the repetition frequency of the stimulations and the mechanisms
that cause this complex dependence are unknown. Here we propose a simple
mathematical model that allows us to systematically study the interaction
between the stimulation protocol and synaptic consolidation. We show the
existence of optimal stimulation protocols for our model and, similarly to LTP
experiments, the repetition frequency of the stimulation plays a crucial role
in achieving consolidation. Our results show that the complex dependence of LTP
on the stimulation frequency emerges naturally from a model which satisfies
only minimal bistability requirements.Comment: 23 pages, 6 figure
- …