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 graded1,2,3,4. We propose that the neocortex combines digital selection of an active set of neurons with analogue response by dynamically varying the positive feedback inherent in its recurrent connections. Strong positive feedback causes differential instabilities that drive the selection of a set of active neurons under the constraints embedded in the synaptic weights. Once selected, the active neurons generate weaker, stable feedback that provides analogue amplification of the input. Here we present our model of cortical processing as an electronic circuit that emulates this hybrid operation, and so is able to perform computations that are similar to stimulus selection, gain modulation and spatiotemporal pattern generation in the neocortex.
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Leopold, D. A. & Logothetis, N. K. Activity changes in early visual cortex reflect monkeys percepts during binocular rivalry. Nature 379, 549–553 ( 1996).
Treue, S. & Maunsell, J. H. Attentional modulation of visual motion processing in cortical areas MT and MST. Nature 382, 539–541 (1006).
Treue, S., Martinez, S. & Trujillo, J. C. Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399, 575–579 (1999).
Reynolds, J. H., Chelazzi, L. & Desimone, R. Competitive mechanisms subserve attention in macaque areas V2 and V4. J. Neurosci. 19, 1736– 1753 (1999).
Ben-Yishai, R., Lev Bar-Or, R. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Nat. Acad. Sci. USA 92, 3844– 3848 (1995).
Hansel, D. & Sompolinsky, H. in Methods in Neuronal Modeling (eds Koch, C. & Segev, I.) 499–567 (MIT Press, Cambridge, Massachusetts, 1998).
Salinas, E. & Abbott, L. F. A model of multiplicative neural responses in parietal cortex. Proc. Natl Acad. Sci. USA 93, 11956–11961 (1996).
Georgopoulos, A. P., Taira, M. & Lukashin, A. Cognitive neurophysiology of the motor cortex. Science 260, 47–52 ( 1993).
Camperi, M. & Wang, X. J. A model of visuospatial working memory in prefrontal cortex: recurrent network and cellular bistability. J. Comput. Neurosci. 5, 383–405 (1998).
Zhang, K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: A theory. J. Neurosci. 16, 2112–2126 (1996).
Kopecz, K. & Schoner, G. Saccadic motor planning by integrating visual information and pre-information on neural dynamical fields. Biol. Cybern. 73, 49–60 (1995).
Amari, S.-I. & Arbib, M. A. in Systems Neuroscience (ed. Metzler, J.) 119–165 (Academic Press, New York, 1977).
Seung, H. S. & Sompolinsky, H. Simple models for reading neuronal population codes. Proc. Natl Acad. Sci. USA 90, 10749–10753 (1993).
Abbott, L. F. Decoding neuronal firing and modelling neural networks. Q. Rev. Biophys. 27, 291–331 ( 1994).
Andersen, R. A., Essick, G. E. & Siegel, R. M. Encoding of spatial location by posterior parietal neurons. Science 230, 456– 458 (1985).
Amari, S.-I. Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybert. 27, 77–87 (1977).
Lazzaro, J., Ryckebusch, S., Mahowald, M. A. & Mead, C. A. in Advances in Neural Information Processing Systems (ed. Touretzky, D. S.) 703–711 (Morgan Kaufmann, San Mateo, California, 1989).
De Weerth, P. & Morris, T. G. CMOS current mode winner-take-all circuit with distributed hysteresis. Electron. Lett. 31, 1051–1053 (1995).
Indiveri, G. Winner-take-all networks with lateral excitation. Analogue Integrated Circuits Signal Process 13, 185–193 (1997).
Amit, D. J. The Hebbian paradigm reintegrated: Local reverberations as internal representations. Behav. Brain Sci. 18, 617– 657 (1995).
Lee, D. & Seung, H. S. Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791 (1999).
Sarpeshkar, R., Lyon, R. F. & Mead, C. A low-power wide-dynamic-range analogue vlsi cochlea. Analogue Integrated Circuits Signal Process. 16, 245–274 (1998).
Andreou, A. G. & Boahen, K. A. Synthetic neural circuits using current-domain signal representations. Neural Computat. 1 (1989).
Mead, C. & Delbrück, T. Scanners for visualizing activity of analogue VLSI circuitry. Analogue Integrated Circuits Signal Process. 1, 93–106 ( 1991).
Hahnloser, R. H. R. About the piecewise analysis of networks of linear threshold neurons. Neural Net. 11, 691–697 (1998).
Horn, R. A. & Johnson, C. R. Matrix Analysis (Cambridge Univ. Press, New York, 1985).
Hopfield, J. J. & Tank, D. W. Neural computations of decisions in optimization problems. Biol. Cybern. 52, 141–152 (1985).
We acknowledge the support of the Swiss National Science Foundation SPP Program, Lucent Technologies and MIT. We thank A. Andreou, J. Kramer, D. Lee, C. Brody, M. Fee, D. Tank, P. Sinha, S. Roweis and S.-C. Liu for discussions about the circuits and current-mode design.
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Hahnloser, R., Sarpeshkar, R., Mahowald, M. et al. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000). https://doi.org/10.1038/35016072
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