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- Lansner, Anders, et al.
(author)
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Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory
- 2003
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In: Theory in biosciences. - : Springer Science and Business Media LLC. - 1431-7613 .- 1611-7530. ; 122:1, s. 19-36
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Journal article (peer-reviewed)abstract
- During the last few decades we have seen a convergence among ideas and hypotheses regarding functional principles underlying human memory. Hebb's now more than fifty years old conjecture concerning synaptic plasticity and cell assemblies, formalized mathematically as attractor neural networks, has remained among the most viable and productive theoretical frameworks. It suggests plausible explanations for Gestalt aspects of active memory like perceptual completion, reconstruction and rivalry. We review the biological plausibility of these theories and discuss some critical issues concerning their associative memory functionality in the light of simulation studies of models with palimpsest memory properties. The focus is on memory properties and dynamics of networks modularized in terms of cortical minicolumns and hypercolumns. Biophysical compartmental models demonstrate attractor dynamics that support cell assembly operations with fast convergence and low firing rates. Using a scaling model we obtain reasonable relative connection densities and amplitudes. An abstract attractor network model reproduces systems level psychological phenomena seen in human memory experiments as the Sternberg and von Restorff effects. We conclude that there is today considerable substance in Hebb's theory of cell assemblies and its attractor network formulations, and that they have contributed to increasing our understanding of cortical associative memory function. The criticism raised with regard to biological and psychological plausibility as well as low storage capacity, slow retrieval etc has largely been disproved. Rather, this paradigm has gained further support from new experimental data as well as computational modeling.
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- Sandberg, Anders, et al.
(author)
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A Bayesian attractor network with incremental learning
- 2002
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In: Network. - 0954-898X .- 1361-6536. ; 13:2, s. 179-194
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Journal article (peer-reviewed)abstract
- A realtime online learning system with capacity limits needs to gradually forget old information in order to avoid catastrophic forgetting. This can be achieved by allowing new information to overwrite old, as in a so-called palimpsest memory. This paper describes an incremental learning rule based on the Bayesian confidence propagation neural network that has palimpsest properties when employed in an attractor neural network. The network does not suffer from catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits faster convergence for newer patterns.
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- Sandberg, Anders, et al.
(author)
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A palimpsest memory based on an incremental Bayesian learning rule
- 2000
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In: Neurocomputing. - 0925-2312 .- 1872-8286. ; 32, s. 987-994
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Journal article (peer-reviewed)abstract
- Capacity limited memory systems need to gradually forget old information in order to avoid catastrophic forgetting where all stored information is lost. This can be achieved by allowing new information to overwrite old, as in the so-called palimpsest memory. This paper describes a new such learning rule employed in an attractor neural network. The network does not exhibit catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits recency effects in retrieval.
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- Shiriaev, Anton, et al.
(author)
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Explicit formulas for general integrals of motion for a class of mechanical systems subject to virtual constraints
- 2004
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In: 2004 43rd IEEE conference on decision and control (CDC), vols 1-5. - New York : IEEE conference proceedings. - 0780386825 ; 2, s. 1158-1163
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Conference paper (peer-reviewed)abstract
- The paper suggests an explicit form of a general integral of motion for some classes of dynamical systems including n-degree-of-freedom mechanical systems subject to (n - 1) virtual holonomic constraints. The computation of this integral opens several possibilities for generating and further exponential orbital stabilization of solutions in nonlinear feedback systems. An illustrative example is given.
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- Andersson, Anders, et al.
(author)
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A transcriptional timetable of autumn senescence
- 2004
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In: Genome Biology. - : Springer Science and Business Media LLC. - 1465-6906 .- 1474-760X. ; 5:4, s. R24-
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Journal article (peer-reviewed)abstract
- Background We have developed genomic tools to allow the genus Populus (aspens and cottonwoods) to be exploited as a full-featured model for investigating fundamental aspects of tree biology. We have undertaken large-scale expressed sequence tag (EST) sequencing programs and created Populus microarrays with significant gene coverage. One of the important aspects of plant biology that cannot be studied in annual plants is the gene activity involved in the induction of autumn leaf senescence. Results On the basis of 36,354 Populus ESTs, obtained from seven cDNA libraries, we have created a DNA microarray consisting of 13,490 clones, spotted in duplicate. Of these clones, 12,376 (92%) were confirmed by resequencing and all sequences were annotated and functionally classified. Here we have used the microarray to study transcript abundance in leaves of a free-growing aspen tree (Populus tremula) in northern Sweden during natural autumn senescence. Of the 13,490 spotted clones, 3,792 represented genes with significant expression in all leaf samples from the seven studied dates. Conclusions We observed a major shift in gene expression, coinciding with massive chlorophyll degradation, that reflected a shift from photosynthetic competence to energy generation by mitochondrial respiration, oxidation of fatty acids and nutrient mobilization. Autumn senescence had much in common with senescence in annual plants; for example many proteases were induced. We also found evidence for increased transcriptional activity before the appearance of visible signs of senescence, presumably preparing the leaf for degradation of its components.
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- Andrén Sandberg, Ake, et al.
(author)
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Early prediction of severity in acute pancreatitis. Is this possible?
- 2002
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In: Journal of the Pancreas. - 1590-8577. ; 3:5, s. 25-116
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Journal article (peer-reviewed)abstract
- One out of ten cases of acute pancreatitis develops into severe acute pancreatitis which is a life threatening disorder with a high mortality rate. The other nine cases are self limiting and need very little therapy. The specificity of good clinical judgement on admission, concerning the prognosis of the attack, is high (high specificity) but misses a lot of severe cases (low sensitivity). The prediction of severity in acute pancreatitis was first suggested by John HC Ranson in 1974. Much effort has been put into finding a simple scoring system or a good biochemical marker for selecting the severe cases of acute pancreatitis immediately on admission. Today C-reactive protein is the method of choice although this marker is not valid until 48-72 hours after the onset of pain. Inflammatory mediators upstream from CRP like interleukin-6 and other cytokines are likely to react faster and preliminary results for some of these mediators look promising. Another successful approach has been to study markers for the activation of trypsinogen such as TAP and CAPAP. This is based on studies showing that active trypsin is the initial motor of the inflammatory process in acute pancreatitis. In the near future a combined clinical and laboratory approach for early severity prediction will be the most reliable. Clinical judgement predicts 1/3 of the severe cases on admission and early markers for either inflammation or trypsinogen activation should accurately identify 50-60% of the mild cases among the rest, thus missing only 2-4% of the remaining severe cases. One problem is that there is no simple and fast method to analyze any of these parameters.
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