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Friedrich T. Sommer |
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Friedrich T.
Sommer, Ph.D; (Fritz Sommer) Visiting Scholar,
University of California Berkeley |
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I am interested in the mechanisms underlying human memory and cognition. I use
computational models of the brain to study this question. An outline of the
modeling approach I use and the specific questions I address are described in
the synopsis of research. For a full list of my research publications, see the cv.
Recent Publications
M. Rehn, F.
T. Sommer: Early sensory representation in cortex optimizes information content
in sparse sets of neurons.
(2005) submitted
M. Rehn, F. T.
Sommer: Rank-based processing of visual input.
(2005) submitted to Neurocomputing
F. T. Sommer, P. Kanerva: Can neural models of cognition benefit from the advantages of connectionism?
Behavoral Brain Sciences (2005) in press
F. T. Sommer, T.
Wennekers: Synfire chains with conductance-based neurons: internal timing and
coordination with timed input.
Neurocomputing 65-66 (2005) 449 - 454 pdf
D. George, F. T. Sommer: Computing with inter-spike inverval codes in
networks of integrate and fire neurons.
Neurocomputing 65-66 (2005) 414 - 420 pdf
L. M. Martinez, Q.
Wang, R. C. Reid, C. Pillai, J.-M.
Alonso, F. T. Sommer, J. A. Hirsch: Receptive field structure varies with layer
in the primary visual cortex.
Nature Neuroscience 8 (12) (2005) 372 - 379 pdf
A. Knoblauch, F. T.
Sommer: Spike-timing dependent plasticity can form "zero-lag" links
for cortical oscillations.
Neurocomputing 52-54 (2004) 301 - 306 pdf
M. Rehn, F. T.
Sommer: A network for the rapid formation of binary sparse representations of
sensory input.
Technical Report RNI-04-1 (2004)
G. Glatting, F. M.
Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B. Landwehrmeyer, S. N.
Reske: Improving binding potential analysis in [11C]raclopide PET studies using
cluster analysis.
Medical Physics 31 (4) (2004) 902-906 pdf
J. A. Hirsch, L. M. Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer:
Functionally distinct inhibitory neurons at the first stage of visual cortical
processing.
Nature Neuroscience 6 (12) (2003) 1300 - 1308 pdf
F. T. Sommer, T. Wennekers: Models of distributed associative memory networks
in the brain
Theory in Biosciences (122) (2003) 70 - 86 pdf
Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data modeling in
functional neuroimaging
MIT Press, Boston, MA (2003) link
to publisher (table of contents, etc.)
A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction delays, and
inter-areal phase relations of spike activity in a model of reciprocally
connected areas
Neurocomputing (52-54) (2003) 301-306 pdf
V. Schmitt, R. Koetter, F. T. Sommer: The impact of thalamo-cortical
projections on activity spread in cortex
Neurocomputing (52-54) (2003) 919-924 pdf
Synopsis of Research
Computational models of the brain
Behavior can be linked to computations in the
brain and studying abstract neural networks can reveal the basic types of
computation possible in nerve tissue. However, abstract neural networks are
only crude models of the biophysics of the brain. They can provide a
"functional skeleton" in a brain model but more details have to be
included in order to also quantitatively describe the (bio)physics of
neurons, synapses, etc., as observed in Neuro-physiology and -anatomy. There is
an fundamental difference between models of physics and computational brain
models: physics models have usually a single interface to an experimental
domain, computational brain models have two, they should reflect biophysics and
behavior as well (Sommer et al. 2003).
The program of neuroscience is to reveal how
physiological/anatomical features of the brain relate to its function.
Computational brain models can establish and test hypothesized links between
biophysical features and function but in this role they are torn between two
extremes. In order to rule out beforehand as little as possible, experimental
neuroscience explores the full diversity of aspects and a computational brain
model should reflect the experimental findings as detailed as possible. On the
other hand a computational theory of a behavior is basically an algorithm and
the cleanest form to instantiate such a hypothesis is by the simplest neural
network that can do the job efficiently and is not incompatible with
neurobiology (following Occam's razor principle).
Obviously, no single computational brain model can escape
this dilemma. The way I study associative memory function of the brain is to
investigate a chain of models that vary in the faithfulness of the biophysical
description. The starting point of the chain is an abstract neural network
model corresponding to the hypothetical function. Features can be added
to the abstract model step by step, reflecting neurobiological features. Thus
the computational function can be first analyzed in the abstract model. The
predictions of biophysical brain properties arising from the functional
hypothesis can be assessed in the more detailed models. Qualitative changes in
the model behavior induced by certain model features can be easily traced in
the chain of models.
Models for local circuits in the brain
W. James, F. Hayek and D. O. Hebb postulated theories of memory and mental
association involving distributed neural representations and synaptic
plasticity. The most elaborated theory (Hebb, 1949) predicts a concrete
synaptic learning rule, the Hebb rule, for learning (distributed) neural
representations of mental entities (thoughts, percepts) called cell assemblies. The direct
physiological confirmation of local
learning rules in synapses as proposed in Hebb's theory was a huge success.
In other respects, however, the predictive value of Hebb's theory was limited
because without a mathematical formulation the predictions were qualitative and
could not be tested in experiments.
Neuronal
associative memories are abstract neural networks that implement the basic
mechanisms of learning and association as postulated in Hebb's theory. Neural
associative memories have been proposed as computational models for local
strongly connected cortical circuits (Palm, 1982, Amit 1989). The computational
function is the storage and error-tolerant recall of distributed activity
patterns. The memory recall is called associative pattern completion if
it involves the completion of a noisy pattern according involving memory.
Another recall variant possible in associative memories is pattern recognition
(Palm & Sommer 1992) where inputs are just classified as "known"
or "unknown".
A number of different abstract models of associative memory
has been proposed in the literature. My choice of an abstract model as starting
point in a chain of computational brain models relies on the assumption that
nature has implemented the hypothetic computational function efficiently.
- How to measure the efficiency of associative memories?
Information capacity has become the standard measure for the efficiency of associative memories. However, the traditional measures do not take into account all relevant flows of information during learning and retrieval. In particular, they neglect the loss due to retrieval errors as well as the information contained in the noisy patterns during pattern completion tasks. For definitions of information capacity that take into account all these factors see (Sommer 1993, Palm & Sommer 1996).
- Why to use sparse memory representations?
(Olshausen & Field 1996, Bell & Sejnowski 1996) studied optimal coding
strategies of natural visual scenes. They found that optimal coding of natural
statistics onto sparse representations
yields neural codes that are in good agreement with the receptive field
properties of neurons in primary visual cortex.
Another argument for sparse memory representations in the
brain follows from the analysis of learning in associative memory. Elisabeth
Gardner's work (1988) revealed a striking match between sparse memory
representations and local learning (Sommer 1993). Based
on her results one can conclude that first, local learning rules store sparse
memory patterns more efficiently than nonsparse patterns and second, only for
sparse patterns local learning cannot be outperformed by nonlocal learning.
Thus, sparse representations of memories naturally arise from optimal local
synaptic learning, a property of synaptic plasticity well confirmed in
physiological studies. Gardner's analysis allows this deep fundamental insight,
however, it is not constructive, for instance, it only takes into account the
learning process and not the recall process. Thus, the question remains:
- What concrete associative memory models process sparse memory representations efficiently?
A general analysis of local learning rules --assessing the capacity of storage
and retrieval in a pattern
association task-- is described in (Sommer 1993;
Palm & Sommer 1996). For sparse memory patterns, the
analysis characterizes the class of efficient local learning rules. How different superposition schemes for memory traces
(in particular, linear superposition as in the Hopfield model and clipped
superposition as in the Willshaw model) compare in terms of efficiency in
sparse pattern recognition is
analyzed in (Palm & Sommer 1992; Sommer
1993).
The analyses of sparse associative memory indicate that the
classical Willshaw-Steinbuch model (Steinbuch, 1961; Willshaw et al, 1969) is
among the most efficient models. However, (Palm & Sommer
1992; Sommer 1993) also shows for this model that
the learning provides a higher capacity than the retrieval, i.e., the retrieval
in the original model is an information bottleneck. This result raises the question
whether the Willshaw model can be improved by modified retrieval.
A modification of the autoassociative Willshaw model
employing iterative retrieval was
analyzed in (Sommer 1993; Schwenker,
Sommer and Palm 1996). It is shown that the modified retrieval retains the
asymptotic information capacity of the original model. However, for (large)
finite-sized networks iterative retrieval has the following advantages:
1) A significant increase in recall precision. 2) The asymptotic capacity value
can be reached in networks of already moderate sizes -- the original model does
not reach asymptotic performance at practical network sizes. 3) Iterative retrieval
is fast. The typical number of required iteration steps is low (<4).
In bidirectional associative memories (Kosko 1988) with
sparse patterns, naive iterative retrieval does not provide the same
improvement as for autoassociation. (Sommer & Palm 1998,
Sommer & Palm 1999) explain why and suggest a novel and
very efficient iterative retrieval in bidirectional associative memories,
called crosswise bidirectional retrieval (see also below).
Having identified efficient instances of sparse associative
memory models these can be used in models of neuronal circuits of the
brain.
- What are the properties of cell assemblies?
If Hebb's theory were true and brain function would be based on cell assemblies, what would their properties be, i.e., how many cells do typically form an assembly and how many assemblies "fit" in a local circuit of cortical tissue? (Sommer 2000) analyzes a model of a square millimeter of cortex (number of neurons and connection densities were taken from neuroanatomical studies, cell excitability was estimated based on physiological studies). The study reveals that the local synapses are used most efficiently if the size of the assemblies is a few hundred cells and the number of assemblies is in the range between ten and sixty thousand. Due to the incomplete connectivity in the network there arises an interesting extension in functionality: A small set of assemblies (~5) can be recalled simultaneously and not just a single one as in classical associative memories.
- How is associative memory reflected in temporal structure of neural activity?
Simulation studies with associative networks of conduction-based spiking
neurons (two-compartment neurons a la Pinsky & Rinzel, 1994) are described
in (Sommer & Wennekers 2000, Sommer
& Wennekers 2001). It is revealed that associative memory recall can be
completed extremely fast, that is, in 25-60ms. Gamma-oscillations can indicate
iterative recall (that reaches higher retrieval precision) with latencies of
60-260ms.
Organization of meso- and macroscopic activity patterns in the brain
While neural network models described in the previous section help understanding computations of local brain circuits, cognitive functions ultimately rely on the meso- and macroscopic organization of neural activity in the brain. The studies in this section address how macroscopic activity flow can establish cooperative interactions even between remote brain regions.
- Can large cell assemblies be integrated by cortico-cortical projections?
Reciprocal connectivity is the most common
type of cortico-cortical projections reported by neuroanatomical tracer studies.
Thus it is likely that reciprocal connections play an important role in
large-scale integration of neural representations or cell assemblies. (Sommer & Wennekers 2003) lay out how bidirectional
association in reciprocal projections could provide such an integration and how
this ties into earlier work about distributed representations, such as the
theories of Wickelgren, Edelman, Damasio, Mesulam and others.
Macroscopically distributed cell assemblies would easily
form, if already a single reciprocal connection would express associative
memory function. In (Sommer & Wennekers, 2000) a
bidirectional associative memory model with conductance-based neurons is
investigated that, in fact, performs efficiently. A more abstract model
that is very robust with respect to cross talk --and therefore might be a good
computational model of a cortico-cortical projection-- is proposed in (Sommer & Palm 1998, Sommer, Wennekers
& Palm 1998, Sommer & Palm1999).
- What causes coherent oscillations in distant brain regions? Does it require learning?
In recordings of neuronal activity, coherent oscillations mostly occur in phase, even if the recording sites in cortex are far apart of each other. For fast (gamma range) oscillations this finding is puzzling given the large delay times reported in long-range projections. Modeling studies using reciprocal excitatory couplings with such delay times predict anti-phase rather than in-phase correlation. In (Knoblauch & Sommer 2002, Knoblauch & Sommer 2003) the conditions are studied under which reciprocal cortical connections with realistic delays can express coherent gamma oscillations. It is demonstrated that learning based on spike-timing dependent synaptic plasticity (Markram et al. 1997, Poo et al. 1998) can provide robust zero lag coherence over long-range projections -- zero-lag links.
- How can macroscopic activity patterns form through cortico-cortical connections?
Neuronography experiments (MCulloch et al, Pribham et al) revealed that epileptiform activity elicited by local application of strychnine entails persistent patterns of activity involving the activity of many brain areas. (Sommer & Koetter 1997, Koetter & Sommer 2000) investigates in a computer model the relation between the anatomy of cortico-cortical projections and the expression of persistent macroscopic activity patterns. In the model the connection weights between brain areas can be either simple cortex connectivity schemes such as nearest neighbor connections or data about cortico-cortical projections gathered by neuroanatomical tracer studies and collatedin the CoCoMac database. The comparison between different connectivity schemes shows that neuroanatomical data can best explain the measured activity patterns. It is concluded that long-range connections are crucial in the formation of patterns that have been observed experimentally. Furthermore, the simulations indicate multisynaptic reverberating activity propagation and clearly rule out the hypothesis that just monosynaptic spread would produce the patterns -- as was speculated in the experimental literature. (V. Schmitt et al 2003) investigates the influence of thalamocortical connections in a similar model.
- How to reveal organization of neuronal activity by Neuroimaging?
Imaging methods like positron emission
tomography (PET) and functional magnetic resonance (fMRI) provide the first
(albeit indirect) windows to macroscopic activation patterns in the working
brain. The spatio-temporal data sets provided by this methods are usually
searched for functional activity using regression analysis based on temporal
shapes that are estimated based on the timing in the experimental paradigm.
However, in short-lasting events and in most cognitive tasks the temporal shape
cannot be reliably predicted. In these cases the detection of functional
activity requires analysis methods based on weaker assumptions about the signal
course. (Baune et al. 1999) describes a new cluster
analysis method for detecting regions of fMRI activation. The method requires
no information about the time course of the activation and is applied to detect
timing differences in the activation of supplementary motor cortex and motor
cortex during a voluntary movement task.
(Wichert et al. 2003) describes the extension of the method
of Baune et al. for event-related designs. A new method of experimental
design/data processing is proposed that yields volumes of data where all slices
are perfectly timed. This avoids the artifacts introduced by usual data
preprocessing methods based on phase-shifting. In (Wichert
et al. 2003) the exploratory method is applied to reveal functional
activity during a n-back working memory task.
In (Baune et al., 2001, Ruckgaber et al.., 2001) a cluster analysis method was developed
to detect microgilia activation which is a very sensitive indicator for brain
lesions.
Mathematical analysis of associative memories
- Bayesian theory of associative memory
An attempt to tame the zoo of associative memory models proposed in the literature is the Bayesian theory of associative memory described in (Sommer & Dayan 1998). In this theory the optimal retrieval dynamics can be derived from the uncertainties about the input pattern and the synaptic weights. Our analysis explains the success of many model modifications proposed on heuristic basics, for instance, addition of a ferromagnetic term, of site-dependent thresholds, diagonal terms, various threshold strategies, etc.
- Combinatorial analysis of the Willshaw model
The full combinatorial analysis of the finite Willshaw model can be found in (Sommer & Palm 1999). It predicts distributions of the dendritic potentials and retrieval errors for arbitrary network sizes and all possible types of input noise.
- Signal-to-noise analysis of local synaptic learning rules
A general signal-to-noise analysis of local learning rules is given in (Sommer 1993; Palm & Sommer 1996). The final result is basically one formula, equation (3.23) in (Palm & Sommer 1996) calculating the S/N for arbitrary learning rules, sparseness levels and input errors. These papers also contain the full information-theoretical treatment of learning and retrieval in associative memories that lead to new definitions of information capacity.
- Asymptotic analysis of sparse Hopfield- and Willshaw-models performing pattern recognition
The asymptotic analysis of the sparse Hopfield and Willshaw model is provided in (Palm & Sommer 1992). We use elementary analysis information theory and can avoid the cumbersome Replica trick used in the earlier analysis of the Hopfield model (Tsodyks & Feigelman, 1988).
Selected Publications by Theme
(for a complete listing, see cv)
Mechanisms of memory in realistic neural networks
Long-term memory
F. T. Sommer, T. Wennekers : Associative memory in networks of
spiking neurons
Neural Networks 14 (6-7) Special Issue: Spiking Neurons in Neuroscience and
Technology (2001) 825 - 834 pdf
F. T. Sommer, T. Wennekers: Modeling studies on the
computational function of fast temporal structure in cortical circuit activity
Journal of Physiology - Paris 94 (5/6) (2000) 473-488 pdf
F. T. Sommer: On cell assemblies in a cortical column
Neurocomputing (32-33) (2000) 517 - 522 pdf
T. Wennekers, F. T. Sommer: Gamma-oscillations support
optimal retrieval in associative memories of two-compartment neurons
Neurocomputing 26-27 (1999) 573 - 578 pdf
T.Wennekers, F.T.Sommer, G.Palm: Iterative
Retrieval in Associative Memories by Threshold Control of Different Neural
Models
In: Supercomputers in Brain Research: From Tomography to Neural Networks World
Scientific Publishing Comp (1995) 301-319 ps
Short-term memory
A. Knoblauch, T. Wennekers, F. T. Sommer: Is voltage
dependent synaptic transmission in NMDA receptors a robust mechanism for
working memory?
Neurocomputing (44-46) (2002) 19-24 pdf
U. Vollmer, F. T. Sommer: Coexistence of short and long term
memory in a model network of realistic neurons
Neurocomputing (38-40) (2001) 1031 - 1036 pdf
Physiology and information processing in early vision
J. A. Hirsch, L. M.
Martinez, C. Pillai, J.-M. Alonso, Q. Wang, F. T. Sommer: Functionally distinct
inhibitory neurons at the first stage of visual cortical processing.
Nature Neuroscience 6 (12) (2003) 1300 - 1308 pdf
Large-scale integration of cortical representations
F. T. Sommer, T.
Wennekers : Models of distributed associative memory
networks in the brain
Theory in Biosciences (122) (2003) 70 - 86 pdf
Associative memory in reciprocal cortico-cortical projections
F. T. Sommer, T. Wennekers : Associative memory in a pair
of cortical cell groups with reciprocal projections
Neurocomputing (38-40) (2001) 1575 - 1580 pdf
F. T. Sommer, T. Wennekers, G. Palm: Bidirectional completion
of cell assemblies in the cortex
Computational Neuroscience: Trends in Research 1998, Plenum Press, New York,
(1998) ps
Large-scale integration relying on oscillations
A. Knoblauch, F. T. Sommer: Spike-timing dependent plasticity
can form "zero-lag" links for cortical oscillations
submitted to Neurocomputing (2003) pdf
A. Knoblauch, F. T. Sommer: Synaptic plasticity, conduction
delays, and inter-areal phase relations of spike activity in a model of
reciprocally connected areas
Neurocomputing (52-54) (2003) 301-306 pdf
Large-scale models of cortical activity spread
V. Schmitt, R.
Koetter, F. T. Sommer: The impact of thalamo-cortical
projections on activity spread in cortex
Neurocomputing (2003) (52-54) (2003) 919-924 pdf
R. Kötter and F. T. Sommer: Global relationship
between anatomical connectivity and activity propagation in the cerebral cortex
Phil. Trans. R. Soc. Lond. B (355) (2000) 127 - 134 pdf
F.T.Sommer, R. Kötter: Simulating a Network of Cortical Areas
Using Anatomical Connection Data in the Cat
Computational Neuroscience: Trends in Research 1997, Plenum Press, New York
(1997) 511-517 ps
R. Koetter, P. Nielsen, J. Dyhrfjeld, F. T. Sommer, G. Northoff: Multi-level
integration of quantitative neuroanatimical data
Chapter in Computational Neuroanatomy: Principles and Methods.
Ed.: G. A. Ascoli, Humana Press Inc., Totowa, NJ (2002)
Theory of sparse associative memory
Bayesian theory of autoassociative memory
F. T. Sommer, P. Dayan: Bayesian Retrieval in Associative
Memories with Storage Errors
IEEE Transactions on Neural Networks 9 (4) (1998) 705-713 pdf
Bidirectional sparse associative memory
F. T. Sommer, G. Palm: Improved Bidirectional Retrieval of
Sparse Patterns Stored by Hebbian Learning
Neural Networks 12 (2) (1999) 281 - 297 pdf
F. T. Sommer, G. Palm: Bidirectional Retrieval from
Associative Memory
Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, MA
(1998) 675 - 681 pdf
Analysis of recurrent sparse autoassociative memories
F.Schwenker, F.T.Sommer, G.Palm: Iterative Retrieval of
sparsely coded associative memory patterns
Neural Networks 9 (1996) 445-455 ps
Analysis of sparse pattern recognition
G.Palm, F.T.Sommer: Information capacity in recurrent
Mc.Culloch-Pitts networks with sparsely coded memory states
Network 3 (1992) 177-186 pdf
G.Palm, F.T.Sommer: Information and pattern capacities in neural associative
memories with feedback for sparse memory patterns
In: Neural Network Dynamics, Springer New York (1992). Eds.: J.G.Taylor,
E.R.Caianello, R.M.J.Cotterill, J.W.Clark, 3-18
Analysis of local learning rules
G.Palm, F.T.Sommer: Associative data Storage and Retrieval
in Neural Nets
In: Models of Neural Networks III, Springer New York (1996) Eds: E.Domany,
J.L.van Hemmen, K.Schulten, 79-118 ps
Book, PhD-Thesis (in german)
F. T. Sommer: Theorie neuronaler Assoziativspeicher -
Lokales Lernen und iteratives Retrieval von Information
Verlag Hänsel-Hohenhausen (1993) ISBN 3-89349-901-6 ps
Neuroimaging
Edited book
Eds: F. T. Sommer, A. Wichert: Exploratory analysis and data
modeling in functional neuroimaging
MIT Press, Boston, MA (2003) link
to publisher (table of contents, etc.)
General issues of Neuroimaging
F. T. Sommer, J. A. Hirsch, A. Wichert: Theories, data
analysis and simulation models in neuroimaging - an overview
In Exploratory analysis and data modeling in functional neuroimaging.
Eds.: F.T. Sommer and A. Wichert, MIT Press, Boston, MA (2003)
pdf
V. Schmitt, A. Wichert, J. Grothe, F. T. Sommer: The brain positioning software
In: A practical guide of neuroscience databases and associated tools, Ed. R.
Koetter, Kluwer, NY (2002)
Unsupervised method of detecting functional activity in Neuroimaging
A. Wichert, B. Abler, J. Grothe, H. Walter, F. T. Sommer: Exploratory
analysis of event-related fMRI demonstrated in a working memory study
In Exploratory analysis and data modeling in functional neuroimaging.
Eds.: F.T. Sommer and A. Wichert, MIT Press, Boston, MA
(2003) pdf
A. Wichert, H. Walter, G. Groen, A. Baune, J. Grothe, A. Wunderlich, F. T.
Sommer: Detection of delay selective activity during a working memory task by
explorative data analysis
Neuroimage (13) (2001) 282
A. Baune, F. T. Sommer, M. Erb, D. Wildgruber, B. Kardatzki, G. Palm, W.
Grodd: Dynamical Cluster Analysis of Cortical fMRI Activation
NeuroImage 6 (5) (1999) 477 - 489 pdf
Analysis techniques in Positron Emission Tomography
G. Glatting, F. M. Mottaghy, J. Karitzky, A. Baune, F. T. Sommer, G. B.
Landwehrmeyer, S. N. Reske: Improving binding potential analysis in
[11C]raclopide PET studies using cluster analysis.
Medical Physics 31 (4) (2004) 902-906 pdf
A. Baune, A. Wichert, G. Glatting, F. T. Sommer: Dynamical
cluster analysis for the detection of microglia activation
in Artificial Neural Nets and Genetic Algorithms. Eds. V. Kurkova, N. C.
Stelle, R. Neruda, M. Karny. Springer, Wien (2001) 442 - 445
J. Ruckgaber, G. Glatting, J. Karitzky, A. Baune, F. T. Sommer, B. Neumaier, S.
N. Reske: Clusteranalyse in der
Positronen-Emissions-Tomographie des Hirns mit C-11-PK11195
Nuklearmedizin (40) (2001) A95
Neural associative memories in information technology
G. Palm, F. Schwenker, F. T. Sommer, A. Strey: Neural
associative memory
In Associative Processing and Processors, Eds. A. Krikelis and C. C. Weems,
IEEE CS Press, Los Alamitos, CA, USA (1997) 307-326 ps
F.T.Sommer, F.Schwenker, G.Palm: Assoziative Speicher als Module in
informationsverarbeitenden Systemen
In: Contributions to the Workshop Aspekte Neuronalen Lernens, Eds. L.Cromme, J.
Wille, T. Kolb Tech Report, TU Cottbus M-01/1995 (1995)
G.Palm, F.Schwenker, F.T.Sommer: Associative memory and sparse similarity
preserving codes
In: From Statistics to Neural Networks: Theory and Pattern Recognition
Applications, Ed. V.Cherkassky, Springer NATO ASI Series F, New York (1994)
282-302
Earlier publications on storage of hydrogen in metal lattices
Workshops and Teaching Courses
Recent workshops:
CNS 2002 workshop: Neural assemblies
NIPS 2000 workshop: Explorative analysis and data modeling
in functional neuroimaging
Graduate teaching (US):
Theoretical and computational neuroscience (teaching participation at course MCB262/PSYCH290P, UC Berkeley)
Graduate teaching (semester courses held at University of Ulm):
Information retrieval and associative
memory
Computational Neuroscience
Theoretical methods for the interpretation of medical
functional imaging data
Information Retrieval
Associative memories: conventional and neuronal
Neural Cell Assemblies
Friedrich T. Sommer -- last update: August, 2003