Using a recurrent neural network of excitatory spiking neurons with adapting synapses we show that both effects could be explained by a fast and a slow component inthe synaptic adaptation.

Using a recurrent neural network of excitatory spiking neurons with adapting synapses we show that both effects could be explained by a fast and a slow component in the synaptic adaptation.

Using a recurrent neural network of excitatory spiking neurons with adapting synapses we show that both effects could be explained by a fast and a slow component in the synaptic adaptation.

Simplified models of the lateral geniculate nucles (LGN) and striate cortexillustrate the possibility that feedback to the LG N may be used for robust, low-level pattern analysis. The information fed back to the LG N is rebroadcast to cortex using the LG N's full fan-out, so the cortex-LGN-cortex pathway mediates extensive cortico-cortical communication while keeping the number of necessary connectionssmall. 1 INTRODUCTION The lateral geniculate nucleus (LGN) in the thalamus is often considered as just a relay station on the way from the retina to visual cortex, since receptive field properties ofneurons in the LGN are very similar to retinal ganglion cell receptive field properties. However, there is a massive projection from cortex back to the LGN: it is estimated that 3-4 times more synapses in the LG N are due to corticogeniculate connectionsthan those due to retinogeniculate connections [12]. This suggests some important processing role for the LGN, but the nature of the computation performed has remained far from clear. I will first briefly summarize some anatomical facts and physiological results concerning thecorticogeniculate loop, and then present a simplified model in which its function is to (usefully) mediate communication between cortical cells.

Simplified models of the lateral geniculate nucles (LGN) and striate cortex illustrate the possibility that feedback to the LG N may be used for robust, low-level pattern analysis. The information fed back to the LG N is rebroadcast to cortex using the LG N's full fan-out, so the cortex-LGN-cortex pathway mediates extensive cortico-cortical communication while keeping the number of necessary connections small.

Simplified models of the lateral geniculate nucles (LGN) and striate cortex illustrate the possibility that feedback to the LG N may be used for robust, low-level pattern analysis. The information fed back to the LG N is rebroadcast to cortex using the LG N's full fan-out, so the cortex-LGN-cortex pathway mediates extensive cortico-cortical communication while keeping the number of necessary connections small.

In single cells of the cat striate cortex, lateral inhibition across orientation and/or spatial frequency is found to enhance preexisting biases. A contrast-dependent but spatially non-selective inhibitory component is also found. Stimulation with ascending and descending contrasts reveals the latter as a response hysteresis that is sensitive, powerful and rapid, suggesting that it is active in day-to-day vision. Both forms of inhibition are not recurrent but are rather network properties. These findings suggest two fundamental inhibitory mechanisms: a global mechanism that limits dynamic range and creates spatial selectivity through thresholding and a local mechanism that specifically refines spatial filter properties.

In single cells of the cat striate cortex, lateral inhibition across orientation and/or spatial frequency is found to enhance preexisting biases. A contrast-dependent but spatially non-selective inhibitory component is also found. Stimulation with ascending and descending contrasts reveals the latter as a response hysteresis that is sensitive, powerful and rapid, suggesting that it is active in day-to-day vision. Both forms of inhibition are not recurrent but are rather network properties. These findings suggest two fundamental inhibitory mechanisms: a global mechanism that limits dynamic range and creates spatial selectivity through thresholding and a local mechanism that specifically refines spatial filter properties.

In the developing visual system in many mammalian species, there is initially a uniform, overlapping innervation of layer 4 of the visual cortex by inputs representing the two eyes. Subsequently, these inputs segregate into patches or stripes that are largely or exclusively innervated by inputs serving a single eye, known as ocular dominance patches. The ocular dominance patches are on a small scale compared to the map of the visual world, so that the initially continuous map becomes two interdigitated maps, one representing each eye. These patches, together with the layers of cortex above and below layer 4, whose responses are dominated by the eye innervating the corresponding layer 4 patch, are known as ocular dominance columns.

In the developing visual system in many mammalian species, there is initially a uniform, overlapping innervation of layer 4 of the visual cortex by inputs representing the two eyes. Subsequently, these inputs segregate into patches or stripes that are largely or exclusively innervated by inputs serving a single eye, known as ocular dominance patches. The ocular dominance patches are on a small scale compared to the map of the visual world, so that the initially continuous map becomes two interdigitated maps, one representing each eye. These patches, together with the layers of cortex above and below layer 4, whose responses are dominated by the eye innervating the corresponding layer 4 patch, are known as ocular dominance columns.