4,314 research outputs found
Cortical patterns and gamma genesis are modulated by reversal potentials and gap-junction diffusion
In this chapter we describe a continuum model for the cortex that includes both axon-to-dendrite chemical synapses and direct neuron-to-neuron gap-junction diffusive synapses. The effectiveness of chemical synapses is determined by the voltage of the receiving dendrite V relative to its Nernst reversal potential Vrev. Here we explore two alternative strategies for incorporating dendritic reversal potentials, and uncover surprising differences in their stability properties and model dynamics. In the “slow-soma” variant, the (Vrev - V) weighting is applied after the input flux has been integrated at the dendrite, while for “fast-soma”, the weighting is applied directly to the input flux, prior to dendritic integration. For the slow-soma case, we find that–-provided the inhibitory diffusion (via gap-junctions) is sufficiently strong–-the cortex generates stationary Turing patterns of cortical activity. In contrast, the fast-soma destabilizes in favor of standing-wave spatial structures that oscillate at low-gamma frequency ( 30-Hz); these spatial patterns broaden and weaken as diffusive coupling increases, and disappear altogether at moderate levels of diffusion. We speculate that the slow- and fast-soma models might correspond respectively to the idling and active modes of the cortex, with slow-soma patterns providing the default background state, and emergence of gamma oscillations in the fast-soma case signaling the transition into the cognitive state
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations
The abundance of instructional videos and their narrations over the Internet
offers an exciting avenue for understanding procedural activities. In this
work, we propose to learn video representation that encodes both action steps
and their temporal ordering, based on a large-scale dataset of web
instructional videos and their narrations, without using human annotations. Our
method jointly learns a video representation to encode individual step
concepts, and a deep probabilistic model to capture both temporal dependencies
and immense individual variations in the step ordering. We empirically
demonstrate that learning temporal ordering not only enables new capabilities
for procedure reasoning, but also reinforces the recognition of individual
steps. Our model significantly advances the state-of-the-art results on step
classification (+2.8% / +3.3% on COIN / EPIC-Kitchens) and step forecasting
(+7.4% on COIN). Moreover, our model attains promising results in zero-shot
inference for step classification and forecasting, as well as in predicting
diverse and plausible steps for incomplete procedures. Our code is available at
https://github.com/facebookresearch/ProcedureVRL.Comment: Accepted to CVPR 202
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