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Tonically driven and self-sustaining activity in the lamprey hemicord: when can they co-exist?
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
2007 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Neurocomputing, ISSN 0925-2312, Vol. 70, no 10-12, 1882-1886 p.Article in journal (Refereed) Published
Abstract [en]

In lamprey hernisegmental preparations, two types of rhythmic activity are found: slower tonically driven activity which varies according to the external drive, and faster, more stereotypic activity that arises after a transient electrical stimulus. We present a simple conceptual model where a bistable excitable system can exhibit the two states. We then show that a neuronal network model can display the desired characteristics, given that synaptic dynamics-facilitation and saturation-are included. The model behaviour and its dependence on key parameters are illustrated. We discuss the relevance of our model to the lamprey locomotor system.

Place, publisher, year, edition, pages
2007. Vol. 70, no 10-12, 1882-1886 p.
Keyword [en]
Dynamical systems; Lamprey; Locomotion; Recurrent excitation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-6306DOI: 10.1016/j.neucom.2006.10.055ISI: 000247215300055Scopus ID: 2-s2.0-34247515220OAI: oai:DiVA.org:kth-6306DiVA: diva2:10985
Note
QC 20100715. Uppdaterad från In press till Published 20100715.Available from: 2006-11-01 Created: 2006-11-01 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Aspects of memory and representation in cortical computation
Open this publication in new window or tab >>Aspects of memory and representation in cortical computation
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [sv]

Denna avhandling i datalogi föreslår modeller för hur vissa beräkningsmässiga uppgifter kan utföras av hjärnbarken. Utgångspunkten är dels kända fakta om hur en area i hjärnbarken är uppbyggd och fungerar, dels etablerade modellklasser inom beräkningsneurobiologi, såsom attraktorminnen och system för gles kodning. Ett neuralt nätverk som producerar en effektiv gles kod i binär mening för sensoriska, särskilt visuella, intryck presenteras. Jag visar att detta nätverk, när det har tränats med naturliga bilder, reproducerar vissa egenskaper (receptiva fält) hos nervceller i lager IV i den primära synbarken och att de koder som det producerar är lämpliga för lagring i associativa minnesmodeller. Vidare visar jag hur ett enkelt autoassociativt minne kan modifieras till att fungera som ett generellt sekvenslärande system genom att utrustas med synapsdynamik. Jag undersöker hur ett abstrakt attraktorminnessystem kan implementeras i en detaljerad modell baserad på data om hjärnbarken. Denna modell kan sedan analyseras med verktyg som simulerar experiment som kan utföras på en riktig hjärnbark. Hypotesen att hjärnbarken till avsevärd del fungerar som ett attraktorminne undersöks och visar sig leda till prediktioner för dess kopplingsstruktur. Jag diskuterar också metodologiska aspekter på beräkningsneurobiologin idag.

Abstract [en]

In this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level -- and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. xiv, 99 p.
Series
Trita-NA, ISSN 0348-2952 ; 2006:17
Keyword
cerebral cortex, neural networks, attractor memory, sequence learning, biological vision, generative models, serial order, computational neuroscience, dynamical synapses
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-4161 (URN)91-7178-478-0 (ISBN)
Public defence
2006-11-13, F3, KTH, Lindstedtsvägen 26, Stockholm, 14:15
Opponent
Supervisors
Note
QC 20100916Available from: 2006-11-01 Created: 2006-11-01 Last updated: 2018-01-13Bibliographically approved
2. Computational modeling of the lamprey CPG: from subcellular to network level
Open this publication in new window or tab >>Computational modeling of the lamprey CPG: from subcellular to network level
2007 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

Due to the staggering complexity of the nervous system, computer modelling is becoming one of the standard tools in the neuroscientist's toolkit. In this thesis, I use computer models on different levels of abstraction to compare hypotheses and seek un- derstanding about pattern-generating circuits (central pattern generators, or CPGs) in the lamprey spinal cord. The lamprey, an ancient and primitive animal, has long been used as a model system for understanding vertebrate locomotion. By examining the lamprey spinal locomotor network, which is a comparatively simple prototype of pattern-generating networks used in higher animals, it is possible to obtain insights about the design principles behind the spinal generation of locomotion.

A detailed computational model of a generic spinal neuron within the lamprey locomotor CPG network is presented. This model is based, as far as possible, on published experimental data, and is used as a building block for simulations of the whole CPG network as well as subnetworks. The model construction process itself revealed a number of interesting questions and predictions which point toward new laboratory experiments. For example, a novel potential role for KNaF channels was proposed, and estimates of relative soma/dendritic conductance densities for KCaN and KNaS channels were given. Apparent inconsistencies in predicted spike widths for intact vs. dissociated neurons were also found. In this way, the new model can be of benefit by providing an easy way to check the current conceptual understanding of lamprey spinal neurons.

Network simulations using this new neuron model were then used to address aspects of the overall coordination of pattern generation in the whole lamprey spinal cord CPG as well as rhythm-generation in smaller hemisegmental networks. The large-scale simulations of the whole spinal CPG yielded several insights: (1) that the direction of swimming can be determined from only the very rostral part of the cord, (2) that reciprocal inhibition, in addition to its well-known role of producing alternating left-right activity, facilitates and stabilizes the dynamical control of the swimming pattern, and (3) that variability in single-neuron properties may be crucial for accurate motor coordination in local circuits.

We used results from simulations of smaller excitatory networks to propose plausible mechanisms for obtaining self-sustaining bursting activity as observed in lamprey hemicord preparations. A more abstract hemisegmental network model, based on Izhikevich neurons, was used to study the sufficient conditions for obtaining bistability between a slower, graded activity state and a faster, non-graded activity state in a recurrent excitatory network. We concluded that the inclusion of synaptic dynamics was a sufficient condition for the appearance of such bistability.

Questions about rhythmic activity intrinsic to single spinal neurons – NMDA-TTX oscillations – were addressed in a combined experimental and computational study. We showed that these oscillations have a frequency which grows with the concentration of bath-applied NMDA, and constructed a new simplified computational model that was able to reproduce this as well as other experimental results.

A combined biochemical and electrophysiological model was constructed to examine the generation of IP3-mediated calcium oscillations in the cytosol of lamprey spinal neurons. Important aspects of these oscillations were captured by the combined model, which also makes it possible to probe the interplay between intracellular biochemical pathways and the electrical activity of neurons.

To summarize, this thesis shows that computational modelling of neural circuits on different levels of abstraction can be used to identify fruitful areas for further experimental research, generate experimentally testable predictions, or to give insights into possible design principles of systems that are currently hard to perform experiments on.

Place, publisher, year, edition, pages
Stockholm: KTH, 2007. xii, 83 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2007:10
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-4418 (URN)978-91-7178-717-0 (ISBN)
Public defence
2007-06-14, FB42, AlbaNova, Roslagstullsbacken 21, Stockholm, 13:00
Opponent
Supervisors
Note
QC 20100714Available from: 2007-06-01 Created: 2007-06-01 Last updated: 2018-01-13Bibliographically approved

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