Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
An introduction to linear algebra in parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
1994 Special Issue: Modeling visual recognition from neurobiological constraints
Neural Networks - Special issue: models of neurodynamics and behavior
Stable and rapid recurrent processing in realistic autoassociative memories
Neural Computation
On decoding the responses of a population of neurons from short time windows
Neural Computation
Methods in Neuronal Modeling: From Ions to Networks
Methods in Neuronal Modeling: From Ions to Networks
The Neurophysiology of Backward Visual Masking: Information Analysis
Journal of Cognitive Neuroscience
Towards Novel Neuroscience-Inspired Computing
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
| Hi-index | 0.00 |
The speed of processing in the cortex can be fast. For example, the latency of neuronal responses in the visual system increases by only approximately 10-20 ms per area in the ventral pathway sequence V1 to V2 to V4 to Inferior Temporal visual cortex. Since individual neurons can be regarded as relatively slow computing elements, this may imply that such rapid processing can only be based on the feedforward connections across cortical areas. In this paper, we study this problem by using computer simulations of networks of spiking neurons. We evaluate the speed with which different architectures, namely feed-forward and recurrent architectures, retrieve information stored in the synaptic efficacy. Through the implementation of continuous dynamics, we found that recurrent processing can take as little as 10-15 ms per layer. This is much faster than obtained with simpler models of cortical processing that are based on simultaneous updating of the firing rate of the individual units. These findings suggest that cortical information processing can be very fast even when local recurrent circuits are critically involved.