Abstract
To analyse the working of the brain it is our intention here to develop a framework that encompasses its most important aspects in functional terms. This intention seems solvable only if one chooses a task that makes use of all parts of the brain, and keeps the level of the description and dimensionality of this task manageable. As a result, although the description should include the numerous details that we know, not every single one needs quantifying. Our view is that the organization of behavior is the task per se that has to be solved by brains. We have therefore chosen this task as the framework for explaining how the brain works, knowing that it has gaps which—so we hope—may be filled by realistic hypotheses. Our emphasis is on the development of principles and strategies. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, we have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also systems and signal theory. The latter provides a well-tested system of concepts, and opens the way to physical laws and limits.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aflalo TN, Graciano MSA (2006) Possible origines of the complex topograhic organization of motor cortex: a reduction of a multidimentional space onto a two dimensional array. J Neurosci 26:6288–6297
Aflalo TN, Graciano MSA (2011) Organization of the macaque extrastriate visual cortex reexamined using the principle of spatial continuity function. J Neurophysiol 1:305–320
Ay N, Bertschinger N, Der R, Güttler F, Olbrich E (2008) Predictive information and explorative behavior of autonomous robots. Eur Phys J B 63:329–339
Ballard DH (1997) Natural computation. MIT Press, Cambridge
Binder JR, Desai R (2011) Neurobiology of sementic memory. Trends Cogn Sci 15:527–536
Birbaumer N, Schmidt RF (2010) Biologische Psychologie. Springer, New York
Byrne P, Becker S, Burgess P (2007) Remembering the past and imagining the future: a neural model of spatial memory and imagery. Psychol Rev 114:340–375
Cang J, Feldheim DA (2013) Developmental mechanism of topographic map formation and alignement. Annu Rev Neurosci 36:51–77
Cisek P, Puskas GA, El-Murr S (2009) Decisions in changing conditions: the urgency-gating model. J Neurosci 29(37):11560–11571
Cisek P, Kalaska JF (2010) Neural mechanisms for interacting with a world full of choices. Annu Rev Neurosci 33:269–299
Cisek P, Pastor-Bernier A (2014) On the challenges and mechanisms of embodied decisions. Philos Trans R Soc B 369
Conrad M (1982) Bootstrapping model of the origin of life. Biosystems 15:209–219
Dabaghian Y, Cowan A, Frank L (2007) Topological coding in hippocampus. arXiv:Quant-Ph/0701128v1
Dabaghian Y, Memoli F, Frank L, Carlsson G (2012) A topological paradigm for hippocampal spatial map formation using persistent homology. PLoS Comput Biol 8(8):e1002581
Deco G, Rolls ET (2005) Attention, short term memory, and action selection: a unifying theory. Prog Neurobiol 76:236–256
Dehaene S (2014) Denken: Wie das Gehirn Bewußtsein schafft A. Knaus Verlag, München
Der R, Ay N (2009) Roboter mit Entdeckerlust in Technik u. Computer
Dinse HR, Krüger K, Best J (1990) A temporal structure of cortical information processing. Concepts Neurosci 1(2):199–238
Douglas RJ, Martin KAC (2004) Neuronal circuits of the neocortex. Annu Rev Neurosci 27:419–451
Dudel J, Menzel R, Schmidt PF (1996) Neurowissenschaft. Springer, New York
Duncan J (2010) The multiple-demand (MD) system of the primate brain: mental programs for intelligent behavior. Trend Cogn Sci 14(4):172–179
Ebeling W, Feistel R (1982) Physik der Selbstorganization und Evolution. Akademie-Verlag, Berlin
Eigen M, Gardiner W, Schuster P, Winkler-Oswatitisch R (1986) Vom Ursprung der genetischen Information Spektrum der Wissenschaft: Evolution, pp 61–80
Eliasmith C, Terence C, Choo X, Bekolay T, Dewolfe T, Tang C, Rasmussen D (2012) Largescale model of the functioning brain. Science 338:1202–1205
Eskandar EN, Richmond BJ, Optican LM (1992) Role of inferior temporal neurons in visual memory. I. Temporal encoding of information about visual images, recalled images, and behavioral context. J Neurophysiol 68:1277–1295
Friston KJ (2011) Functional and effective connectivity: a review. Brain Connect 1(1):13–36
Friston K, Kiebel S (2009) Predictive coding under the free energy principle. Philos Trans R Soc 364:1211–1221
Graziano M (2006) The organization of behavioral repertoire in motor cortex. Annu Rev Neurosci 29:105–134
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6(7):e159
Han CE, Yoo SW, Seo SW, Na DL, Seong J-K (2013) Cluster-based statistics for brain connectivity in correlation with behavioral measures. PLoS ONE 8(8):e72332
Hassabis D, Maguire EA (2009) The construction system of the brain. Philos Trans R Soc B 364:1263–1271
Hassabis D, Maguire EA (2007) Deconstructing eposodic memory with construction. Trends Cogn Sci 11(7):299–306
Hasselmo ME (2005) A model of prefrontal cortical mechanisms for goal directed behavior. J Cogn Neurosci 17(7):1–14
Hasselmo ME (2009) A model of episodic memory: mental time travel along encoded trajectories using grid cells. Neurobiol Learn Mem 92:559–573
Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New York
Heiligenberg W (1991) Neural nets in electric fish. MIT Press, Cambridge
Henke K (2010) A model for memory systems based on processing modes rather than consciousness. Nat Rev Neurosci 11:523–523
Hilberg W (2012) Wie denkt das Gehirn? Verlag für Sprache und Technik
Hinton GE, Salakhutinov RR (2006) Reducing dimensionality of data with neural networks. Science 313:504–507
Hubel WH, Wiesel TN (1974) Ordered arrangement of orientation columns in monkeys lacking visual expericence. J Comput Neurol 158:309–318
Hüsken M, Igel C, Toussaint M (GECCO 2001) Task dependent evolution of modularity in neural networks. In: Genetic and evolutionary computation conference, pp 187–193
Jarvis ED, Güntürkün O, Bruce L, Csillag A et al (2005) Avian brains and a new understanding of vertebrate brain evolution. Nat Rev Neurosci 6:151–159
Kaas J (1999) The transformation of the association cortex into sensory cortex. Brain Res Bull 50:425
Kahle T, Olbrich E, Jost J, Ay N (2008) Complexity measures from interaction structures. Phys Rev E 79:026201. arxiv.org:0806.2552
Kahnemann D, Tversky A (eds) (2000) Choices, values and frames. Cambridge University Press, Cambridge
Kandel ER, Schwartz JH, Jessell TM (2012) Neurowissenschaften Spectrum. Akademischer Verlag
Koechlin E (2014) An evolutionary computational theory of prefrontal executive function in decision-making. Philos Trans R Soc B 369:20130474
Kohonen T (1977) Associative memory. Springer, New York
Kolmogorov AN, Uspenski VA (1987 engl. Übers.) Algorithms and randomnes. Theory Prob Appl 32:389–412
Konishi M (1986) Centrally synthesized maps of sensory space. TINS 9:163–168
Konishi M, Takahashi TT, Wagner H, Sullivan WE, Carr CE (1988) Neurophysiological and anatomical substrates of sound localisation in the owl. In: Edelman GM, Gall WE, and Cowan WM (eds) Auditory function. Wiley & Sons Inc., pp 721–745
Kosslyn SM, Koenig O (1992) Wet mind. The new cognitive neuroscience. Free Press, New York
Le Roux N, Bengio Y (2010) Deep belief networks are compact universal approximators. Neural comput 22:2192–2207
Linsker R (1992) Deriving receptive fields using an optimal encoding criterion. Adv Neural Inf Process Syst 5:953–956
Mallot HP (2013) Computaional neuroscience. Springer, Heidelberg
Mallot HP, v Seelen W, Giannakopoulos F (1990) Neural mapping and space-variant image processing. Neural Netw 3:245–263
Mallot HP (1985) An overall description of retinotopic mapping in the cat’s visual cortex areas 17, 18, and 19. Biol Cybern 52:45–51
Markov T, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013) Cortical high-density counterstream architectures. Science 342:1238406
Minsky M, Papert S (1969) Perceptrons. MIT Press, Cambridge
Mullally SL, Maguire EA (2012) Memory, imagination and predicting the future: a common brain mechanism. Neuroscientist 20:220–234
Newell A, Simon HA (1972) Human probelm solving. Prentice Hall, Englewood Cliffs
Oh SW, Harris JA, Ng L, Winslow B, Cain N, Mihalas S, Wang Q, Lau C, Kuan L, Henry AM, Mortrud MT, Ouellette B, Nguyen TN, Sorensen SA, Slaughterbeck CR, Wakeman W, Li Y, Feng D, Ho A, Nicholas E, Hirokawa KE, Bohn P, Joines KM, Peng H, Hawrylycz MJ, Phillips JW, Hohmann JG, Wohnoutka P, Gerfen CR, Koch C, Bernard A, Dang C, Jones AR, Zeng H (2014) A mesoscale connectome of the mouse brain. Nature 508:207–214
Palm G (1980) On associative memory. Biol Cybern 36:19–31
Pika S, Bugnyar T (2011) The use of referential gestures in ravens (Corvus corax) in the wild. Nat Commun 2:560
Poggio T, Reichardt W (1973) Considerations on models of movement detection. Kybernetik 13:223–227
Prior H, Schwarz A, Güntürkün O (2008) Mirror-induced behavior in the magpie (Pica pica):evidence for self-recognition. PLoS Biol 6:e202
Quiroga RQ (2012) Concept cells: the building blocks of declarative memory function. Nat Rev 13:587–597
Raby CR, Alexis DM, Dickinson A, Clayton NS (2007) Planning for the future by western scrub-jays. Nature 445:919–921
Ritter H, Martinetz T, Schulten K (1990) Neuronale Netz. Addison-Wesley, Bonn
Rolls ET (2008) Memory, attention and decision-making: a unifying computational neuroscience approach. Oxford University Press, Oxford
Schacter DL, Addis DR, Buckner RL (2009) Remembering the past to imagine the future: the prospective brain. Nat Rev Neurosci 8:657–661
Schacter DL (2012) Constructive memory: past and future. Dialogues Clin Neurosci 14(1):7–18
Schöner G, Kelso JAS (1988) A dynamic theory of behavioral change. J Theor Biol 135:501–524
Shanahan M, Bingman VP, Shimizu T, Wild M, Güntürkün O (2013) Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis. Front Comput Neurosci 7:89
Shannon C, Weaver W (1963) A mathematical theory of communication. University of Illinois Press, Urbana
Slotine JJ (2006) Modular stability tools for distributed computation control. J Adap Control Signal Process 176:397–416
Sparks DL, Mays LE (1990) Signal transformation requires for the generation of saccadic eye movements. Annu Rev Neurosci 13:309–336
Sperry RW (1956) The eye and the brain. Sci Am 154(5):48–52
Steinbuch K (1961) Die Lernmatrix Kybernetik 1:36–45
Taylor AH, Miller R, Gray RD (2012) New Caledonian crows reason about hidden causal agents. Proc Natl Acad Sci USA 109:16389–16391
Tennenbaum J, Kamp C, Griffiths TC, Goodman ND (2011) How to grow a mind: statistics, structure and abstraction. Science 331:4
Tomasello M (2009) Die Ursprünge der menschlichen Kommunikation Suhrkamp
Toussaint M, v Seelen W (2007) Complex adaptation and system structure. BioSystems 90:769–782
Tovee MJ, Rolls ET, Treves A, Bellis RP (1993) Information encoding and the responses of neurons in the temporal visual cortical areas of primates. J Neurophysiol 70:640–654
van Wedeen J, Rosene DL, Ruopeng W, Guangping D, Mortazavi F, Hagmann P, Kaas JH, Tseng Wen-Yih I (2012) The geometric structure of the brain fiber pathways. Science 335:1628
von der Malsburg C (1973) Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14:85–100
von der Malsburg C, Schneider W (1986) A neural cocktail-party processor. Biol Cybern 54:29–40
Weir AA, Chappell J, Kacelnik A (2002) Shaping of hooks in New Caledonian crows. Science 297:981
Acknowledgments
We would like to thank Ms. Dr. U. Körner for many discussions on the subject and with the help in gathering the relevant literature. Her broad knowledge in neurobiology has been of great help for the text and for our conscience as well. The like is true for Dr. M. Casimir who in addition emboldened us to publish. We also thank Dr. Scharstein for his scrupulous perusal of the german manuscript and the resulting improvements. From handwritten difficultly decipherable text fragments Ms. H. Berz compiled a perusable text (german at first). We thank for her patience and her work. The english version, first typed by Ms. A. Johnson-Letzel and Ms. R. Bertgen has got its final polish by Ms. Dr. J. Büttner-Ennever. All these people we are deeply indebted to and we thank them heartily.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Seelen, W.v., Behrend, K. (2016). Principles of Neural Information Processing. In: Principles of Neural Information Processing. Cognitive Systems Monographs, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-20113-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-20113-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20112-2
Online ISBN: 978-3-319-20113-9
eBook Packages: EngineeringEngineering (R0)