The basal and distal apical dendrites ofpyramidal cells occupy distinct cortical layers and are targeted by axons originating in different cortical regions. Hence, apical and basal dendrites receive information from distinct sources. Physiological evidence suggests that this anatomically observed segregation ofinput sources may have functional significance. This possibility has been explored in various connectionist models that employ neurons with functionally distinct apical and basal compartments. A neuron in which separate sets ofinputs can be integrated independently has the potential to operate in a variety ofways not possible for the conventional neuron model, in which all inputs are treated equally. This article thus considers how functionally distinct apical and basal dendrites can contribute to the information-processing capacities of single neurons and, in particular, how information from different cortical regions could have disparate effects on neural activity and learning.
Andersen, R. A.
, Essick, G. K., & Siegel, R. M. (1985). Encoding of spatial location by posterior parietal neurons. Science, 230(4724), 456-458.
2.
Balkenius, C.
(1995). Multi-modal sensing for robot control. In L. F. Niklasson & M. B. Bodén (Eds.), Current trends in connectionism (pp. 203-216). Hillsdale, NJ: Lawrence Erlbaum.
3.
Barbas, H.
, & Rempel-Clower, N. (1997). Cortical structure predicts the pattern of corticocortical connections. Cerebral Cortex, 7, 635-646.
4.
Barlow, H. B.
(1994). What is the computational goal of the neocortex? In C. Koch & J. L. Davis (Eds.), Large scale neuronal theories ofthe brain (chap. 1). Cambridge, MA: MIT Press.
5.
Becker, S.
(1996). Mutual information maximization: Models of cortical self-organization. Network: Computation in Neural Systems, 7, 7-31.
6.
Becker, S.
, & Hinton, G. E. (1992). A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355(6356):161-163.
7.
Braitenberg, V.
(1978). Cortical architectonics: General and areal. In M.A.B. Brazier & H. Petsche (Eds.), Architectonics ofthe cerebral cor tex (pp. 443-465). New York: Raven Press.
8.
Brotchie, P. R.
, Andersen, R. A., Snyder, L. H., & Goodman, S. J. (1995). Head position signals used by parietal neurons to encode locations of visual stimuli. Nature, 375(6528), 232-235.
9.
Budd, J.M.L.
(1998). Extrastriate feedback to primary visual cortex in primates: A quantitative analysis of connectivity. Proceedings ofthe Royal Society ofLondon (Series B), 265(1400), 1037-1044.
10.
Cauller, L. J.
(1995). Layer I of primary sensory neocortex: Where top-down converges upon bottom-up. Behavioural Brain Research, 71(1-2), 163-170.
11.
Crick, F.
, & Asanuma, C. (1986). Certain aspects of the anatomy and physiology of the cerebral cortex. In D. E. Rumelhart, J. L. McClelland, & The PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructures ofcognition: Psychological and biological models (Vol. 2, pp. 333-371). Cambridge, MA: MIT Press.
12.
Crick, F.
, & Koch, C. (1998). Constraints on cortical and thalamic projections: The no-strong-loops hypothesis. Nature, 391, 245-250.
13.
Dayan, P.
, & Hinton, G. E. (1996). Varieties of Helmholtz machine. Neural Networks, 9(8), 1385-1403.
14.
de Sa, V. R.
(1994). Learning classification with unlabeled data. In J.D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems (Vol. 6, pp. 112-119). San Francisco: Morgan Kaufmann.
15.
de Sa, V. R.
, & Ballard, D. (1998). Category learning through multimodality sensing. Neural Computation, 10(5), 1097-1117.
16.
De Weerd, P.
, Peralta, M. R., Desimone, R., & Ungerleider, L. G. (1999). Loss of attentional stimulus selection after extrastriate cortical lesions in macaques. Nature Neuroscience, 2(8), 753-758.
17.
Der, R.
, & Smyth, D. (1998). Local online learning of coherent information. Neural Networks, 11(5), 909-925.
18.
Desimone, R.
, & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review ofNeuroscience, 18, 193-222.
19.
Durbin, R.
, & Rumelhart, D. E. (1989). Product units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation, 1, 133-142.
20.
Ebdon, M. (1996). Towards a general theory ofcerebral neocortex. Unpublished doctoral thesis, University of Sussex.
21.
Eglen, S.
, Bray, A., & Stone, J. (1997). Unsupervised discovery of invariances. Network: Computation in Neural Systems, 8(4), 441-452.
22.
Felleman, D. J.
, & Van Essen, D. C. (1991). Distributed hierarchical processing in primate cerebral cortex. Cerebral Cortex, 1, 1-47.
23.
Fiala, J.
, & Harris, K. (1999). Dendritic structure and spines. In G. Stuart, N. Spruston, & M. Häusser (Eds.), Dendrites (pp. 1-34). Oxford, UK: Oxford University Press.
24.
Friston, K. J.
, & Büchel, C. (2000). Attentional modulations of effective connectivity from V2 to V5/MT in humans. Proceedings ofthe National Academy ofScience (USA), 97(13), 7591-7596.
25.
Fukushima, K.
(1987). Neural network model for selective attention in visual pattern recognition and associative recall. Applied Optics, 26(23), 4985-4992.
26.
Grossberg, S.
, & Raizada, R. (2000). Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex. Vision Research, 40(10-12), 1413-1432.
27.
Häusser, M.
(2001). Synaptic function: Dendritic democracy. Current Biology, 11(l), 10-12.
28.
Häusser, M.
, Spruston, N., & Stuart, G. J. (2000). Diversity and dynamics of dendritic signalling. Science, 290(5492), 739-744.
29.
Hebb, D. O.
(1949). The organization ofbehavior: A neuropsychological theory. New York: John Wiley.
30.
Hetherington, P. A.
, & Shapiro, M. L. (1993). Simulating Hebb cell assemblies: The necessity for partitioned dendritic trees and a post-not-pre LTD rule. Network: Computation in Neural Systems, 4, 135-153.
31.
Hinton, G. E.
, Dayan, P., Frey, B. J., & Neal, R. M. (1995). The wakesleep algorithm for unsupervised neural networks. Science, 268(5214), 1158-1161.
32.
Hinton, G. E.
, & Ghahramani, Z. (1997). Generative models for discovering sparse distributed representations. Philosophical Transactions ofthe Royal Society ofLondon (Series B), 352(1358), 1177-1190.
33.
Hopfield, J.
(1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings ofthe National Academy ofScience (USA), 79(8), 2554-2558.
34.
Hupé, J. M.
, James, A. C., Payne, B. R., Lomber, S. G., Girard, P., & Bullier, J. (1998). Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature, 394(6695), 784-787.
35.
Itti, L.
, & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3), 194-202.
36.
Johnson, R. R.
, & Burkhalter, A. (1997). A polysynaptic feedback circuit in rat visual cortex. Journal of Neuroscience, 17(18), 7129-7140.
37.
Kanwisher, N.
, & Wojciulik, E. (2000). Visual attention: Insights from brain imaging. Nature Reviews Neuroscience, 1(2), 91-100.
38.
Kastner, S.
, Pinsk, M. A., De Weerd, P., Desimone, R., & Ungerleider, L. G. (1999). Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron, 22(4), 751-761.
39.
Kastner, S.
, & Ungerleider, L. G. (2000). Mechanisms of visual attention in the human cortex. Annual Review ofNeuroscience, 23, 315-341.
40.
Kay, J.
, & Phillips, W. A. (1997). Activation functions, computational goals and learning rules for local processors with contextual guidance. Neural Computation, 9(4), 895-910.
41.
Koch, C.
, & Segev, I. (2000). The role of single neurons in information processing. Nature Neuroscience(suppl.), 3, 1171-1177.
42.
Koch, C.
, & Ullman, S. (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4), 219-227.
43.
Koerner, E.
, Tsujino, H., & Masutani, T. (1997). A cortical-type modular neural network for hypothetical reasoning. Neural Networks, 10(5), 791-814.
44.
Kohonen, T.
(1972). Correlation matrix memories. IEEE Transactions on Computers, 21, 353-359.
45.
Körding, K. P.
, & König, P. (2000a). A learning rule for dynamic recruitment and decorrelation. Neural Networks, 13(1), 1-9.
46.
Körding, K. P.
, & König, P. (2000b). Learning with two sites of synaptic integration. Network: Computation in Neural Systems, 11(1), 25-39.
47.
Körding, K. P.
, & König, P. (2000c). A spike based learning rule for generation of invariant representations. Journal of Physiology (Paris), 94(5-6), 539-548.
48.
Körding, K. P.
, & König, P. (2000d). Two sites of synaptic integration: Relevant for learning. In International joint conference on neural networks. Washington, DC: IEEE Computer Society.
49.
Körding, K. P.
, & König, P. (2001a). Neurons with two sites of synaptic integration learn invariant representations. Neural Computation, 13(12), 2823-2849.
50.
Körding, K. P.
, & König, P. (2001b). Supervised and unsupervised learning with two sites of synaptic integration. Journal ofComputa tional Neuroscience, 11(3), 207-215.
51.
Lamme, V.A.F.
, & Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23(11), 571-579.
52.
Lamme, V.A.F.
, Supèr, H., & Spekreijse, H. (1998). Feedforward, horizontal, and feedback processing in the visual cortex. Current Opinion in Neurobiology, 8(4), 529-535.
53.
Larkum, M. E.
, Zhu, J. J., & Sakmann, B. (1999). A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature, 398(6725), 338-341.
54.
Larkum, M. E.
, Zhu, J. J., & Sakmann, B. (2001). Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons. Journal of Physiology, 533(2), 447-466.
55.
Lee, D. K.
, Itti, L., Koch, C., & Braun, J. (1999). Attention activates winner-take-all competition among visual filters. Nature Neuroscience, 2(4), 375-381.
56.
Lee, T. S.
, Mumford, D., Romero, R., & Lamme, V.A.F. (1998). The role of primary visual cortex in higher level vision. Vision Research, 38, 2429-2454.
57.
Levy, N.
, Horn, D., & Ruppin, E. (1999). Associative memory in a multimodular network. Neural Computation, 11, 1717-1737.
58.
Luck, S. J.
, Chelazzi, L., Hillyard, S. A., & Desimone, R. (1997). Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. Journal of Neurophysiology, 77, 24-42.
59.
Magee, J. C.
, & Johnston, D. (1997). A synaptically controlled, associative signal for Hebbian plasticity in hippocampal neurons. Science, 275(5297), 209-213.
60.
Markram, H.
, Lubke, J., Frotscher, M., & Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275(5297), 213-215.
61.
McAdams, C. J.
, & Maunsell, J.H.R. (2000). Attention to both space and feature modulates neuronal responses in macaque area V4. Journal of Neurophysiology, 83(3), 1751-1755.
62.
McClelland, J. L.
, & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception, part 1: An account of basic findings. Psychological Review, 88, 375-407.
63.
Mel, B. W.
(1993). Synaptic integration in an excitable dendritic tree. Journal of Neurophysiology, 70(3), 1086-1101.
64.
Mel, B. W.
(1994). Information processing in dendritic trees. Neural Computation, 6, 1031-1085.
65.
Mel, B. W.
(1999). Why have dendrites? A computational perspective. In G. Stuart, N. Spruston, & M. Häusser (Eds.), Dendrites (pp. 271-289). Oxford, UK: Oxford University Press.
66.
Mel, B. W.
, & Koch, C. (1990). Sigmapi learning: On radial basis functions and cortical associative learning. In D. S. Touretzsky (Ed.), Advances in neural information processing systems (Vol. 2, pp. 474-481). San Francisco: Morgan Kaufmann.
67.
Miller, R.
(1996). Neural assemblies and laminar interactions in the cerebral cortex. Biological Cybernetics, 75, 253-261.
68.
Möller, R.
, & Groß, H.-M. (1994). Perception through anticipation. In Proceedings of PerAc ‘94–from perception to action (pp. 408-411). Los Alamitos, CA: IEEE Computer Society Press.
69.
Möller, R.
, & Groß, H.-M. (1995). Possible functional roles of bipartite dendrites of pyramidal cells. In Proceedings ofthe Third Annual SNN Symposium on Neural Networks (pp. 51-54). New York: SpringerVerlag.
70.
Mountcastle, V. B.
(1998). Perceptual neuroscience: The cerebral cortexCambridge, MA: Harvard University Press.
71.
Mumford, D.
(1992). On the computational architecture of the neocortex II: The role of cortico-cortical loops. Biological Cybernetics, 66, 241-251.
72.
Mumford, D.
(1994). Neuronal architectures for pattern-theoretic problems. In C. Koch & J. L. Davis (Eds.), Large-scale neuronal theories ofthe brain (pp. 125-152). Cambridge, MA: MIT Press.
73.
Olshausen, B. A.
, Anderson, C. H., & Van Essen, D. C. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. Journal of Neuroscience, 13(11), 4700-4719.
74.
Olson, C. R.
(2001). Object-based vision and attention in primates. Current Opinion in Neurobiology, 11(2), 171-179.
75.
Olson, I. R.
, Chun, M. M., & Allison, T. (2001). The contextual guidance of attention: Human intracranial event-related potential evidence for feedback modulation in anatomically early temporally late stages of visual processing. Brain, 124(7), 1417-1425.
76.
O’Reilly, R. C.
(1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8(5), 895-938.
77.
O’Reilly, R. C.
(1998). Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences, 2(11), 455-462.
78.
Paré, D.
, Lang, E. J., & Destexhe, A. (1998). Inhibitory control of somatodendritic interactions underlying action potentials in neocortical pyramidal neurons in vivo: An intracellular and computational study. Neuroscience, 84(2), 377-402.
79.
Phillips, W. A.
, Kay, J., & Smyth, D. (1995). The discovery of structure by multi-stream networks of local processors with contextual guidance. Network: Computation in Neural Systems, 6(2), 225-246.
80.
Phillips, W. A.
, & Singer, W. (1997). In search of common foundations for cortical computation. Behavioural and Brain Sciences, 20(4), 657-722.
81.
Rao, R.P.N.
(1999). An optimal estimation approach to visual perception and learning. Vision Research, 39(11), 1963-1989.
82.
Rao, R. P. N.
, & Ballard, D. H. (1997). Dynamical model of visual recognition predicts neural response properties in the visual cortex. Neural Computation, 9(4), 721-763.
83.
Rao, R.P.N.
, & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptivefield effects. Nature Neuroscience, 2(1), 79-87.
84.
Reynolds, J. H.
, Chelazzi, L., & Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19, 1736-1753.
85.
Reynolds, J. H.
, Pasternak, T., & Desimone, R. (2000). Attention increases sensitivity of V4 neurons. Neuron, 26, 703-714.
86.
Robert, A. (1999). Lamination and within-area integration in the neocortex. Unpublished doctoral thesis, University of California, San Diego.
87.
Rockland, K. S.
(1998). Complex microstructures of sensory cortical connections. Current Opinion in Neurobiology, 8, 545-551.
88.
Rolls, R. T.
, & Treves, A. (1998). Neural networks and brain function. Oxford, UK: Oxford University Press.
89.
Rumelhart, D. E.
, McClelland, J. L., & The PDP Research Group (Eds.). (1986). Parallel distributed processing: Explorations in the microstructures ofcognition: Foundations (Vol. 1). Cambridge, MA: MIT Press.
90.
Ryder, D.
, & Favorov, O. (2001). The new associationism: A neural explanation for the predictive powers of the cerebral cortex. Brain and Mind, 2(2), 161-194.
91.
Salinas, E.
, & Abbott, L. F. (1996). A model of multiplicative neural responses in parietal cortex. Proceedings ofthe National Academy of Science (USA), 93, 11956-11961.
92.
Salinas, E.
, & Abbott, L. F. (1997). Invariant visual perception from attentional gain fields. Journal of Neurophysiology, 77(6), 3267-3272.
93.
Salinas, E.
, & Sejnowski, T. J. (2001). Gain modulation in the central nervous system: Where behavior, neurophysiology and computation meet. The Neuroscientist, 7(5), 430-440.
94.
Salinas, E.
, & Thier, P. (2000). Gain modulation: A major computational principle of the central nervous system. Neuron, 27, 15-21.
95.
Sánchez-Montañés, M. A.
, Verschure, P.F.M.J., & König, P. (2000). Local and global gating of synaptic plasticity. Neural Computation, 12(3), 519-529.
96.
Schroeder, C. E.
, Mehta, A. D., & Foxe, J. J. (2001). Determinants of attentional control in cortical processing: Evidence from human and monkey electrophysiologic investigations. Frontiers in Bioscience, 6, 672-684.
97.
Segev, I.
(1995). Dendritic processing. In M. A. Arbib (Eds.), The handbook ofbrain theory and neural networks (pp. 282-289). Cambridge, MA: MIT Press.
98.
Segev, I.
, & Rall, W. (1998). Excitable dendrites and spines: Earlier theoretical insights elucidate recent direct observations. Trends in Neurosciences, 21(11), 453-460.
99.
Shao, Z.
, & Burkhalter, A. (1996). Different balance of excitation and inhibition in forward and feedback circuits of rat visual cortex. Journal of Neuroscience, 16(22), 7353-7365.
100.
Shepherd, G. M.
, & Brayton, R. K. (1987). Logic operations are properties of computer-simulated interactions between excitable dendritic spines. Neuroscience, 21, 151-166.
101.
Siegel, M.
, Körding, K. P., & König, P. (2000). Integrating top-down and bottom-up sensory processing by somatodendritic interactions. Journal ofComputational Neuroscience, 8, 161-173.
102.
Spratling, M. W. (1999). Artificial ontogenesis: A connectionist model of development. Unpublished doctoral thesis, University of Edinburgh.
103.
Spratling, M. W.
, & Hayes, G. M. (2000). Learning synaptic clusters for non-linear dendritic processing. Neural Processing Letters, 11(1), 17-27.
104.
Spruston, N.
, Jaffe, D. B., & Johnston, D. (1994). Dendritic attenuation of synaptic potentials and currents: The role of passive membrane properties. Trends in Neuroscience, 17, 161-166.
105.
Stuart, G.
, & Häusser, M. (2001). Dendritic coincidence detection of EPSPs and action potentials. Nature Neuroscience, 4(1), 63-71.
106.
Stuart, G.
, Schiller, J., & Sakmann, B. (1997). Action potential initiation and propagation in rat neocortical pyramidal neurons. Journal of Physiology, 505(3), 617-632.
107.
Stuart, G.
, Spruston, N., & Musser, M. (Eds.). (1999). Dendrites. Oxford, UK: Oxford University Press.
108.
Stuart, G.
, Spruston, N., Sakmann, B., & Häusser, M. (1997). Action potential initiation and backpropagation in neurons of the mammalian CNS. Trends in Neurosciences, 20(3), 125-131.
109.
Wickelgren, W. A.
(1992). Webs, cell assemblies, and chunking in neural nets. Concepts in Neuroscience, 3(1), 1-53.
110.
Willshaw, D. J.
, Longuet-Higgins, H. C., & Buneman, O. P. (1969). Non-holographic associative memories. Nature, 222(197), 960-962.
111.
Yuste, R.
, Gutnick, M. J., Saar, D., Delaney, K. R., & Tank, D. W. (1994). Ca2+ accumulations in dendrites of neocortical pyramidal neurons: an apical band and evidence for two functional compartments. Neuron, 13, 23-43.
112.
Zipser, K.
, Lamme, V.A.F., & Schiller, P. H. (1996). Contextual modulation in primary visual cortex. Journal of Neuroscience, 16(22), 7376-7389.
