[MURG] cell types//brain architecture

[Eugen Leitl]

Hint: feel free to post such material even when I don't. Usually, I won't.

http://scienceweek.com/2004/sb040903-4.htm

ScienceWeek

NEUROBIOLOGY: ON NEURONAL CELL TYPES

The following points are made by Richard H. Masland (Current Biology 2004
14:497):

1) Identifying the functionally distinct types of neuron is central to any
bottom-up understanding of how the brain works. The different cell types are
the brain's elementary computational elements -- the components from which
the larger machine is made. We have known of some cell types for more than a
century, but the coverage has been spotty and anecdotal. This is changing: it
is now possible to assemble more or less complete inventories of cell types
-- the brain's parts list, upon which all understandings of brain function
depend.

2) The recognition that neurons are distinct functional entities was the
first great contribution of neurobiology's founding father, Santiago Ramon y
Cajal (1852-1934), who could make that leap because he had a method, the
Golgi stain, which shows individual neurons in spectacular isolation from
their neighbors. It was immediately apparent that neurons come in a florid
variety of shapes and the identification of neuronal types, an industry that
still flourishes, was set in motion. How are neuronal types distinguished and
why do neurobiologists care so much about them?

3) What do we mean by a cell type? This question has generated much
discussion, but the ultimate goal is simple -- to find a way to single out a
group of neurons that carry out a distinct task. In real life, we rarely know
a cell's function at the first encounter, and the strategic path is to first
identify cell types and then find out what they do. This kind of search is
based on the fundamental premise that different structure indicates different
function, "structure" broadly defined here to include both morphology and the
expression of functionally important proteins. This has proved over the years
to be a reliable rule.

4) Variation in almost any biologically important dimension can be taken as a
guide. Increasingly, cell types can be distinguished by their expression of
genes and/or proteins. Occasionally they are first distinguished by
characteristic patterns of electrical activity. But the commonest way is the
shape of the cell. This is not only because the shapes of neurons are pretty
-- which to the trained eye they are -- nor is cell shape just a convenient
taxonomic label. The deeper reason is that the shapes of neurons are a direct
reflection of their synaptic connections.

5) There are hundreds of named neuronal types in the brain. The names have
varying degrees of exactness and currency, ranging from the famously
distinctive Purkinje cell to many lesser, poorly defined cells. Like genes,
some cells appear under several names. Often, earlier nomenclatures have been
abandoned as more precise ways of classifying cells developed. In fortunate
cases a name derived from morphology, such as "sparse, wide-field
multistratified cell", is replaced by one derived from a unique
cell-type-specific protein, such as "melanopsin cell", but these are
uncommon. As for genes, names are sometimes chosen whimsically, and as for
genes it is unlikely that a standard system of naming will exist soon.(1-5)

References:

1. Blackshaw, S. and Livesey, R. (2002). Applying genomics technologies to
neural development. Curr. Opin. Neurobiol. 12, 110-114

2. Masland, R.H. (2001). The fundamental plan of the retina. Nat Neurosci. 4,
877-886

3. Rockhill, R.L., Euler, T., and Masland, R.H. (2000). Spatial order within
but not between types of retinal neurons. Proc. Natl. Acad. Sci. USA 97,
2303-2307

4. The Synaptic Organization of the Brain. (2004). Shepherd, G.M. ed. (Oxford
University Press)

5. Stevens, C.F. (1998). Neuronal diversity: Too many cell types for
comfort?. Curr. Biol. 8, R708-R710

Current Biology http://www.current-biology.com

--------------------------------

Related Material:

NEUROBIOLOGY: ON NEURONAL NETWORKS

The following points are made by S.B. Laughlin and T.J. Sejnowski (Science
2003 301:1870):

1) Neuronal networks have been extensively studied as computational systems,
but they also serve as communications networks in transferring large amounts
of information between brain areas. Recent work suggests that their structure
and function are governed by basic principles of resource allocation and
constraint minimization, and that some of these principles are shared with
human-made electronic devices and communications networks.

2) The discovery that neuronal networks follow simple design rules resembling
those found in other networks is striking because nervous systems have many
unique properties. To generate complicated patterns of behavior, nervous
systems have evolved prodigious abilities to process information. Evolution
has made use of the rich molecular repertoire, versatility, and adaptability
of cells. Neurons can receive and deliver signals at up to 10^(5) synapses
and can combine and process synaptic inputs, both linearly and nonlinearly,
to implement a rich repertoire of operations that process information (1).

3) Neurons can also establish and change their connections and vary their
signaling properties according to a variety of rules. Because many of these
changes are driven by spatial and temporal patterns of neural signals,
neuronal networks can adapt to circumstances, self-assemble, autocalibrate,
and store information by changing their properties according to experience.

4) The simple design rules improve efficiency by reducing (and in some cases
minimizing) the resources required to implement a given task. It should come
as no surprise that brains have evolved to operate efficiently. Economy and
efficiency are guiding principles in physiology that explain, for example,
the way in which the lungs, the circulation, and the mitochondria are matched
and coregulated to supply energy to muscles (2).

5) Just like the wires connecting components in electronic chips, the
connections between neurons occupy a substantial fraction of the total
volume, and the wires (axons and dendrites) are expensive to operate because
they dissipate energy during signaling. Nature has an important advantage
over electronic circuits because components are connected by wires in
three-dimensional (3D) space, whereas even the most advanced VLSI (very large
scale integration) microprocessor chips use a small number of layers of
planar wiring. [A recently produced chip with 174 million transistors has
seven layers (3).] Does 3D wiring explain why the volume fraction of wiring
in the brain (40 to 60%) is lower than in chips (up to 90%)? In chips, the
components are arranged to reduce the total length of wiring. This same
design principle has been established in the nematode worm Caenorhabditis
elegans, which has 302 neurons arranged in 11 clusters called ganglia. An
exhaustive search of alternative ganglion placements shows that the layout of
ganglia minimizes wire length (4).

6) Cortical projections in the early sensory processing areas are
topographically organized. This is a hallmark of the six-layer neocortex, in
contrast to the more diffuse projections in older three-layer structures such
as the olfactory cortex and the hippocampus. In the primary visual cortex,
for example, neighboring regions of the visual field are represented by
neighboring neurons in the cortex. Connectivity is much higher between
neurons separated by less than 1 mm than between neurons farther apart,
reflecting the need for rapid, local processing within a cortical column --
an arrangement that minimizes wire length. Because cortical neurons have
elaborately branched dendritic trees (which serve as input regions) and
axonal trees (which project the output to other neurons), it is also possible
to predict the optimal geometric patterns of connectivity (5), including the
optimal ratios of axonal to dendritic arbor volumes. These conclusions were
anticipated nearly 100 years ago by the great neuroanatomist Ramon y Cajal
(1852-1934).

7) In summary: Brains perform with remarkable efficiency, are capable of
prodigious computation, and are marvels of communication. We are beginning to
understand some of the geometric, biophysical, and energy constraints that
have governed the evolution of cortical networks. To operate efficiently
within these constraints, nature has optimized the structure and function of
cortical networks with design principles similar to those used in electronic
networks. The brain also exploits the adaptability of biological systems to
reconfigure in response to changing needs.(5)

References (abridged):

1. C. Koch, Biophysics of Computation: Information Processing in Single
Neurons (Oxford Univ. Press, New York, 1999)

2. E. R. Weibel, Symmorphosis: On Form and Function in Shaping Life (Harvard
Univ. Press, Cambridge, MA, 2000)

3. J. D. Warnock et al., IBM J. Res. Dev. 46, 27 (2002)

4. C. Cherniak, J. Neurosci. 14, 2408 (1994)

5. G. Mitchison, Proc. R. Soc. London Ser. B 245, 151 (1991)

Science http://www.sciencemag.org

-- 
Eugen* Leitl <a href="http://leitl.org">leitl</a>
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