Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit

  title={Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit},
  author={Richard Hans Robert Hahnloser and Rahul Sarpeshkar and Misha A. Mahowald and Rodney J. Douglas and H. Sebastian Seung},
Digital circuits such as the flip-flop use feedback to achieve multi-stability and nonlinearity to restore signals to logical levels, for example 0 and 1. Analogue feedback circuits are generally designed to operate linearly, so that signals are over a range, and the response is unique. By contrast, the response of cortical circuits to sensory stimulation can be both multistable and graded. We propose that the neocortex combines digital selection of an active set of neurons with analogue… 
Neuromorphic hardware databases for exploring structure-function relationships in the brain.
  • C. Breslin, A. O'Lenskie
  • Computer Science
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  • 2001
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  • Shih-Chii Liu, M. Oster
  • Computer Science, Biology
    2006 IEEE International Symposium on Circuits and Systems
  • 2006
This work demonstrates the computation of a winner-take-all computation not only within a VLSI network but also across networks of integrate-and-fire neurons in a feature competition task.
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Drivers and modulators from push-pull and balanced synaptic input.
All-optical spiking neurosynaptic networks with self-learning capabilities
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Low-power wide-dynamic-range systems are extremely hard to build. The biological cochlea is one of the most awesome examples of such a system: It can sense sounds over 12 orders of magnitude in
Simple models for reading neuronal population codes.
  • H. Seung, H. Sompolinsky
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 1993
It is found that for threshold linear networks the transfer of perceptual learning is nonmonotonic, and although performance deteriorates away from the training stimulus, it peaks again at an intermediate angle.
Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4
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Theory of orientation tuning in visual cortex.
A simple network model is analytically studied that incorporates both orientation-selective input from the lateral geniculate nucleus and orientation-specific cortical interactions, and exhibits orientation selectivity that originates from within the cortex, by a symmetry-breaking mechanism.
A model of multiplicative neural responses in parietal cortex.
  • E. Salinas, L. Abbott
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 1996
It is shown that multiplicative responses can arise in a network model through population effects and suggest that parietal responses may be based on this architecture.
CMOS current mode winner-take-all circuit with distributed hysteresis
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  • D. Amit
  • Biology
    Behavioral and Brain Sciences
  • 1995
Cognitive and neurophysiological predictions are made, many following directly from the language used to describe the activity in the experimental delay period, others from the details of how the model captures the properties of the internal representations.
Decoding neuronal firing and modelling neural networks
  • L. Abbott
  • Biology
    Quarterly Reviews of Biophysics
  • 1994
Biological neural networks are large systems of complex elements interacting through a complex array of connexions and exhibit a wide variety of dynamic behaviours on time scales ranging from milliseconds to many minutes.