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