1,886 research outputs found
Dopaminergic Regulation of Neuronal Circuits in Prefrontal Cortex
Neuromodulators, like dopamine, have considerable influence on the\ud
processing capabilities of neural networks. \ud
This has for instance been shown in the working memory functions\ud
of prefrontal cortex, which may be regulated by altering the\ud
dopamine level. Experimental work provides evidence on the biochemical\ud
and electrophysiological actions of dopamine receptors, but there are few \ud
theories concerning their significance for computational properties \ud
(ServanPrintzCohen90,Hasselmo94).\ud
We point to experimental data on neuromodulatory regulation of \ud
temporal properties of excitatory neurons and depolarization of inhibitory \ud
neurons, and suggest computational models employing these effects.\ud
Changes in membrane potential may be modelled by the firing threshold,\ud
and temporal properties by a parameterization of neuronal responsiveness \ud
according to the preceding spike interval.\ud
We apply these concepts to two examples using spiking neural networks.\ud
In the first case, there is a change in the input synchronization of\ud
neuronal groups, which leads to\ud
changes in the formation of synchronized neuronal ensembles.\ud
In the second case, the threshold\ud
of interneurons influences lateral inhibition, and the switch from a \ud
winner-take-all network to a parallel feedforward mode of processing.\ud
Both concepts are interesting for the modeling of cognitive functions and may\ud
have explanatory power for behavioral changes associated with dopamine \ud
regulation
Self-organization of signal transduction
We propose a model of parameter learning for signal transduction, where the
objective function is defined by signal transmission efficiency. We apply this
to learn kinetic rates as a form of evolutionary learning, and look for
parameters which satisfy the objective. This is a novel approach compared to
the usual technique of adjusting parameters only on the basis of experimental
data. The resulting model is self-organizing, i.e. perturbations in protein
concentrations or changes in extracellular signaling will automatically lead to
adaptation. We systematically perturb protein concentrations and observe the
response of the system. We find compensatory or co-regulation of protein
expression levels. In a novel experiment, we alter the distribution of
extracellular signaling, and observe adaptation based on optimizing signal
transmission. We also discuss the relationship between signaling with and
without transients. Signaling by transients may involve maximization of signal
transmission efficiency for the peak response, but a minimization in
steady-state responses. With an appropriate objective function, this can also
be achieved by concentration adjustment. Self-organizing systems may be
predictive of unwanted drug interference effects, since they aim to mimic
complex cellular adaptation in a unified way.Comment: updated version, 13 pages, 4 figures, 3 Tables, supplemental tabl
Logarithmic distributions prove that intrinsic learning is Hebbian
In this paper, we present data for the lognormal distributions of spike
rates, synaptic weights and intrinsic excitability (gain) for neurons in
various brain areas, such as auditory or visual cortex, hippocampus,
cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of
heavy-tailed, specifically lognormal, distributions for rates, weights and
gains in all brain areas examined. The difference between strongly recurrent
and feed-forward connectivity (cortex vs. striatum and cerebellum),
neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of
activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns
out to be irrelevant for this feature. Logarithmic scale distribution of
weights and gains appears to be a general, functional property in all cases
analyzed. We then created a generic neural model to investigate adaptive
learning rules that create and maintain lognormal distributions. We
conclusively demonstrate that not only weights, but also intrinsic gains, need
to have strong Hebbian learning in order to produce and maintain the
experimentally attested distributions. This provides a solution to the
long-standing question about the type of plasticity exhibited by intrinsic
excitability
Presynaptic modulation as fast synaptic switching: state-dependent modulation of task performance
Neuromodulatory receptors in presynaptic position have the ability to
suppress synaptic transmission for seconds to minutes when fully engaged. This
effectively alters the synaptic strength of a connection. Much work on
neuromodulation has rested on the assumption that these effects are uniform at
every neuron. However, there is considerable evidence to suggest that
presynaptic regulation may be in effect synapse-specific. This would define a
second "weight modulation" matrix, which reflects presynaptic receptor efficacy
at a given site. Here we explore functional consequences of this hypothesis. By
analyzing and comparing the weight matrices of networks trained on different
aspects of a task, we identify the potential for a low complexity "modulation
matrix", which allows to switch between differently trained subtasks while
retaining general performance characteristics for the task. This means that a
given network can adapt itself to different task demands by regulating its
release of neuromodulators. Specifically, we suggest that (a) a network can
provide optimized responses for related classification tasks without the need
to train entirely separate networks and (b) a network can blend a "memory mode"
which aims at reproducing memorized patterns and a "novelty mode" which aims to
facilitate classification of new patterns. We relate this work to the known
effects of neuromodulators on brain-state dependent processing.Comment: 6 pages, 13 figure
The Many Functions of Discourse Particles: A Computational Model of Pragmatic Interpretation
We present a connectionist model for the interpretation of discourse\ud
particles in real dialogues that is based on neuronal\ud
principles of categorization (categorical perception, prototype\ud
formation, contextual interpretation). It can be shown that\ud
discourse particles operate just like other morphological and\ud
lexical items with respect to interpretation processes. The description\ud
proposed locates discourse particles in an elaborate\ud
model of communication which incorporates many different\ud
aspects of the communicative situation. We therefore also\ud
attempt to explore the content of the category discourse particle.\ud
We present a detailed analysis of the meaning assignment\ud
problem and show that 80%– 90% correctness for unseen discourse\ud
particles can be reached with the feature analysis provided.\ud
Furthermore, we show that ‘analogical transfer’ from\ud
one discourse particle to another is facilitated if prototypes\ud
are computed and used as the basis for generalization. We\ud
conclude that the interpretation processes which are a part of\ud
the human cognitive system are very similar with respect to\ud
different linguistic items. However, the analysis of discourse\ud
particles shows clearly that any explanatory theory of language\ud
needs to incorporate a theory of communication processes
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