Every mathematical framework is developed around some basic computational item, for example, sets, numbers, and vectors. This is the starting point for a computational view of the brain as well, the basic units have to be specified. While in the ethereal world of mathematics the basic components can be arbitrarily assumed, even invented from scratch, the case of the brain is constrained by its biophysical structure. The current view today is dominated by the paradigm constructed by Ramón y Cajal, where the neuronal cell is the basic computational device of the brain. Enormous progress has been achieved in characterizing the computational properties of the brain under this paradigm, which will partly be reviewed in this chapter, limiting ourselves to those aspects that are useful for understanding the representational power of the brain. However, like any scientific paradigm, the so called “neuron dogma” might possibly change in the future, there are scholars for example (London and Häusser, Annu Rev Neurosci 28:503–5032, 2005) that argue that dendrites display autonomous computational capabilities, and might be a better candidate for the title of basic computational unit. Despite many attempts, it is still difficult to spell out what the most specific function of the neuron as a computational device is, and how that makes it so different from other man-made computational devices. We favor an idea advanced by Turing (1948, Intelligent machinery. Technical report, National Physical Laboratory, London, reprinted in Ince DC (ed) Collected Works of A. M. Turing: Mechanical Intelligence, Edinburgh University Press, 1969) long ago: neurons have no special built in function, but that of being able to learn virtually any function, by experience. Plasticity is the term in neuroscience that includes the biological mechanisms that explain how neurons work as extraordinary learning machines. The last section of this chapter deals with a special organization of neurons, that has long been held to deserve a specific computational description (Stevens, What form should a cortical theory take? In: Koch C, Davis J (eds) Large-scale neuronal theories of the brain. MIT, Cambridge, pp 239–255, 1994), and seems to be the privileged site of semantic representations: the cortex.
The Computational Units of the Brain
De La Cruz V. M.
2016-01-01
Abstract
Every mathematical framework is developed around some basic computational item, for example, sets, numbers, and vectors. This is the starting point for a computational view of the brain as well, the basic units have to be specified. While in the ethereal world of mathematics the basic components can be arbitrarily assumed, even invented from scratch, the case of the brain is constrained by its biophysical structure. The current view today is dominated by the paradigm constructed by Ramón y Cajal, where the neuronal cell is the basic computational device of the brain. Enormous progress has been achieved in characterizing the computational properties of the brain under this paradigm, which will partly be reviewed in this chapter, limiting ourselves to those aspects that are useful for understanding the representational power of the brain. However, like any scientific paradigm, the so called “neuron dogma” might possibly change in the future, there are scholars for example (London and Häusser, Annu Rev Neurosci 28:503–5032, 2005) that argue that dendrites display autonomous computational capabilities, and might be a better candidate for the title of basic computational unit. Despite many attempts, it is still difficult to spell out what the most specific function of the neuron as a computational device is, and how that makes it so different from other man-made computational devices. We favor an idea advanced by Turing (1948, Intelligent machinery. Technical report, National Physical Laboratory, London, reprinted in Ince DC (ed) Collected Works of A. M. Turing: Mechanical Intelligence, Edinburgh University Press, 1969) long ago: neurons have no special built in function, but that of being able to learn virtually any function, by experience. Plasticity is the term in neuroscience that includes the biological mechanisms that explain how neurons work as extraordinary learning machines. The last section of this chapter deals with a special organization of neurons, that has long been held to deserve a specific computational description (Stevens, What form should a cortical theory take? In: Koch C, Davis J (eds) Large-scale neuronal theories of the brain. MIT, Cambridge, pp 239–255, 1994), and seems to be the privileged site of semantic representations: the cortex.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
