Selective attention model with spiking elements
نویسندگان
چکیده
A new biologically plausible model of visual selective attention is developed based on synaptically coupled Hodgkin-Huxley neurons. The model is designed according to a two-layer architecture of excitatory and inhibitory connections which comprises two central neurons and a population of peripheral neurons. Two types of inhibition from the central neurons are present: fixed inhibition which is responsible for the formation of the attention focus, and short-term plastic inhibition which is responsible for the shift of attention. The regimes of synchronous dynamics associated with the development of the attentional focus are studied. In particular, the regime of partial synchronization between spiking activity of the central and peripheral neurons is interpreted as object selection to the focus of attention. It is shown that peripheral neurons with higher firing rates are selected preferentially by the attention system. The model correctly reproduces some observations concerning the mechanisms of attentional control, such as the coherence of spikes in the population of neurons included in the focus of attention, and the inhibition of neurons outside the focus of attention. Sequential selection of stimuli simultaneously present in the visual scene is demonstrated by the model in the frequency domain in both a formal example and a real image.
منابع مشابه
A spiking VLSI selective attention multi–chip system with dynamic synapses and integrate-and-fire neurons
Selective attention is the strategy used by biological sensory systems to solve the problem of limited parallel processing capacity: salient subregions of the input stimuli are serially processed, while non–salient regions are suppressed. We present an mixed mode analog/digital Very Large Scale Integration implementation of a building block for a multi–chip neuromorphic hardware model of select...
متن کاملA Unifying Mechanistic Model of Selective Attention in Spiking Neurons
Visuospatial attention produces myriad effects on the activity and selectivity of cortical neurons. Spiking neuron models capable of reproducing a wide variety of these effects remain elusive. We present a model called the Attentional Routing Circuit (ARC) that provides a mechanistic description of selective attentional processing in cortex. The model is described mathematically and implemented...
متن کاملA selective attention multi--chip system with dynamic synapses and spiking neurons
Selective attention is the strategy used by biological sensory systems to solve the problem of limited parallel processing capacity: salient subregions of the input stimuli are serially processed, while non–salient regions are suppressed. We present an mixed mode analog/digital Very Large Scale Integration implementation of a building block for a multi–chip neuromorphic hardware model of select...
متن کاملSelective Attention Model of Moving Objects
Tracking moving objects is a vital visual task for the survival of an animal. We describe oscillatory neural network models of visual attention with a central element that can track a moving target among a set of distracters on the screen. At the initial stage, the model forms the focus of attention on an arbitrary object that is considered as a target. Other objects are treated as distracters....
متن کاملReconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas.
In this computational work, we investigated gamma-band synchronization across cortical circuits associated with selective attention. The model explicitly instantiates a reciprocally connected loop of spiking neurons between a sensory-type (area MT) and an executive-type (prefrontal/parietal) cortical circuit (the source area for top-down attentional signaling). Moreover, unlike models in which ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Neural networks : the official journal of the International Neural Network Society
دوره 22 7 شماره
صفحات -
تاریخ انتشار 2009