Monthly Archives: August 2016

Neural Networks, Neuromodulation, Synaptic Plasticity

Dopamine and Neuromodulation

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Some time ago, I suggested that equating dopamine with reward learning was a bad idea. Why?
First of all, because it is a myopic view of the role of neuromodulation in the brain, (and also in invertebrate animals). There are at least 4 centrally released neuromodulators, they all act on G-protein-coupled receptors (some not exclusively), and they all have effects on neural processing as well as memory. Furthermore there are myriad neuromodulators which are locally  released, and which have similar effects, all acting through different receptors, but on the same internal pathways, activating G-proteins.

Reward learning means that reward increases dopamine release, and that increased dopamine availability will increase synaptic plasticity.

That was always simplistic and like any half-truth misleading.

Any neuromodulator is variable in its release properties. This results from the activity of its NM-producing neurons, such as in locus ceruleus, dorsal raphe, VTA, medulla etc., which receive input, including from each other, and secondly from control of axonal and presynaptic release, which is independent of the central signal. So there is local modulation of release. Given a signal which increases e.g. firing in the VTA, we still need to know which target areas are at the present time responsive, and at which synapses precisely the signal is directed. It depends on the local state of the network, how the global signal is interpreted.

Secondly, the activation of G-protein coupled receptors is definitely an important ingredient in activating the intracellular pathways that are necessary for the expression of plasticity. Roughly, a concurrent activation of calcium and cAMP/PKA (within 10s or so) has been found to be supportive or necessary of inducing synaptic plasticity. However, dopamine, like the other centrally released neuromodulators, acts through antagonistic receptors, increasing or decreasing PKA, increasing or reducing plasticity. It is again local computation which will decide the outcome of NM signaling at each site.

So, is there a take-home message, rivaling the simplicity of dopamine=reward?

NMs alter representations (=thought) and memorize them (=memory) but the interpretation is flexible at local sites (=learn and re-learn).

Dopamine alters thought and memory in a way that can be learned and re-learned.

Back in 1995 I came up with the idea of analysing neuromodulators like dopamine as a method of introducing global parameters into neural networks, which were considered at the time to admit only local, distributed computations. It seemed to me then, as now, that the capacity for global control of huge brain areas (serotonergic, cholinergic, dopaminergic and noradrenergic systems), was really what set neuromodulation apart from the neurotransmitters glutamate and GABA. There is no need to single out dopamine as the one central signal, which induces simple increases in its target areas, when in reality changes happen through antagonistic receptors, and there are many central signals.  Also, the concept of hedonistic reward is badly defined and essentially restricted to Pavlovian conditioning for animals and addiction in humans.

Since the only known global parameter in neural networks at the time occurred in reinforcement learning, some people created a match, using dopamine as the missing global reinforcement signal (Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science. 1997). That could not work, because reinforcement learning requires proper discounting within a decision tree. But the idea stuck. Ever since I have been upset at this primitive oversimplification. Bad ideas in neuroscience.

Scheler, G and Fellous, J-M: Dopamine modulation of prefrontal delay activity- reverberatory activity and sharpness of tuning curves.  Neurocomputing, 2001.

Scheler, G. and Schumann, J: Presynaptic modulation as fast synaptic switching: state-dependent modulation of task performance. Proceedings of the International Joint Conference on Neural Networks 2003, Volume: 1. DOI: 10.1109/IJCNN.2003.1223347

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Theories, Models and Data

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In the modern world, a theory is a mathematical model, and a mathematical model is a theory. A theory described in words is not a theory, it is an explanation or an opinion.

The interesting thing about mathematical models is that they go far beyond data reproduction. A theoretical model of a biological structure or process may be entirely hypothetical, or it may use a certain amount of quantitative data from experiments, integrate it into a theoretical framework and ask questions that result from the combined model.

A Bayesian model in contrast is a purely data-driven construct which usually requires additional quantitative values (‘priors’) which have to be estimated. A dynamical model of metabolic or protein signaling processes in the cell assumes only a simple theoretical structure, kinetic rate equations, and then proceeds to fill the model with data (many estimated) and analyses the results. A neural network model takes a set of data and performs a statistical analysis to cluster the patterns for similarity, or to assign new patterns to previously established categories. Similarly, high-throughput or other proteomic data are usually analysed for outliers and variance with statistical significance with respect to a control data set. Graph analysis of large-scale datasets for a cell type, brain regions, neural connections etc. also aim to reproduce the dataset, to visualize it, and to provide quantitative and qualitative measures of the resulting natural graph.
All these methods primarily attempt to reproduce the data, and possibly make predictions concerning missing data or the behavior of a system that is created from the dataset.

Theoretical models can do more.

A theoretical model can introduce a hypothesis on how a biological system functions, or even, how it ought to function. It may not even need detailed experimental data, i.e. experiments and measurements, but it certainly needs observations and outcomes. It should be specific enough to spur new experiments in order to verify the hypothesis.
In contrast to Popper, a hypothetical model should not be easily falsifiable. If that were the case, it would probably be an uninteresting, highly specific model, for which experiments can be easily performed to falsify the model. A theoretical model should be general enough to explain many previous observations and open up possibilities for many new experiments, which support, modify and refine the model. The model may still be wrong, but at least it is interesting.
It should not be easy to decide which of several hypothetical models covers the complex biological reality best. But if we do not have models of this kind, and level of generality, we cannot guide our research towards progress in answering pressing needs in society, such as in medicine. We then have to work with old, outdated models and are condemned to accumulate larger and larger amounts of individual facts for which there is no use. Those facts form a continuum without a clear hierarchy, and they become quickly obsolete and repetitive, unless they are stored in machine-readable format, where they become part of data-driven analysis, no matter their quality and significance. In principle, such data can be accumulated and rediscovered by theoreticians which look for confirmation of a model. But they only have significance after the model exists.

Theories are created, they cannot be deduced from data.