Learning in the Brain: Difference learning vs. Associative learning

The feedforward/feedback learning and interaction in the visual system has been analysed as a case of “predictive coding” , the “free energy principle” or “Bayesian perception”. The general principle is very simple, so I will call it “difference learning”. I believe that this is directly comparable (biology doesn’t invent, it re-invents) to what is happening at the cell membrane between external (membrane) and internal (signaling) parameters.

It is about difference modulation: an existing or quiet state, and then new signaling (at the membrane) or by perception (in the case of vision). Now the system has to adapt to the new input. The feedback connections transfer back the old categorization of the new input. This gets added to the perception so that a percept evolves which uses the old categorization together with the new input to achieve quickly an adequate categorization for any perceptual input. There will be a bias of course in favor of existing knowledge, but that makes sense in a behavioral context.

The same thing happens at the membrane. An input signal activates membrane receptors (parameters). The internal parameters – the control structure – transfers back the stored response to the external membrane parameters. And the signal generates a suitable neuronal response according to its effect on external (bottom-up) together with the internal control structure (top-down). The response is now biased in favor of an existing structure, but it also means all signals can quickly be interpreted.

If a signal overcomes a filter, new adaptation and learning of the parameters can happen.

The general principle is difference learning, adaptation on the basis of a difference between encoded information and a new input. This general principle underlies all membrane adaptation, whether at the synapse or the spine, or the dendrite, and all types of receptors, whether AMPA, GABA or GPCR.

We are used to believe that the general principle of neural plasticity is associative learning. This is an entirely different principle and merely derivative of difference learning in certain contexts. Associative learning as the basis of synaptic plasticity goes back more than a 100 years. The idea was that by exposure to different ideas or objects, the connection between them in the mind was strengthened. And it was then conjectured that two neurons (A and B) both of which are activated would strengthen their connection (from A to B). More precisely, as was later often found, A needed to fire earlier than B, in order to encode a sequential relation.

What would be predicted by difference learning? An existing connection would encode the strength of synaptic activation at that site. As long as the actual signal matches, there is no need for adaptation. If it becomes stronger, the synapse may acquire additional receptors by using its internal control structure. This control structure may have requirements about sequentiality. The control structure may also be updated to make the new strength permanent, a new set-point parameter. On the other hand, a weaker than memorized signal will ultimately lead the synapse to wither and die.

Similar outcomes, entirely different principles. Association is encoded by any synapse, and since membrane receptors are plastic, associative learning is a restricted derivative of difference learning.

Two parts of the brain

Here is an image from the 10th art of neuroscience contest. It is striking in that it gives a view of the brain, where there are two major parts: the cerebrum and the cerebellum. Of course there are a lot of interesting structures hidden within the cerebrum, but still: Is this a von Neumann architecture, memory and processor? No, we already know that memory is an ubiquitous phenomenon and not localized to any brain structure (notwithstanding the function of the hippocampus). How about a CPU and a GPU? Closer, since the cerebellum is (a) structurally remarkably homogeneous, suitable to performing many routine operations (b) fast, by an order of magnitude faster than the cortex, (c) considered to perform ‘supportive’ functions in terms of smooth motor movements, complex perception, but also standard thought processes, always improving fast coordination and integration of information (d) also, as it mostly speeds up, improves and calibrates brain processes, an organism is able to survive without it  (e) contains very many processing units (50% of all neurons of the brain), even though its function is subsidiary to cortex, (f) has a very constrained feedback modality, a bottleneck in information transfer through deep cerebellar nuclei, even though it receives large amounts of information from the cortex. Although the cerebellum does not do graphical displays, it may “image” cortical information, i.e. represent it in a specific, simple format, and use this to guide motor movements, while providing only limit feedback about its performance. Food for thought.

Consciousness made easy

IMS Photo Contest 2017

For a long time I didn’t know what research on consciousness was to be about. Was it being able to feel and think? Was it perceptual awareness (as in ‘did you hear that sound’?) What did attention have to do with it (the searchlight hypothesis), i.e. lots of stored information is present but not ‘in consciousness’ at any given moment in time?
Finally, while discussing  the issue that no one has a good theory of anesthesia (TMK), (i.e. how it happens and why it works), it occurred to me we can simplify the question, and make it solvable in a fairly easy way:
Consciousness (C) made easy is just the difference between awake state W  and anesthesia/slow wave sleep SWS/A.

C = W – SWS/A

The difference is what makes up consciousness. We can measure this difference in a number of ways, brain imaging, neuronal spiking behavior, EEG/EcoG, LFPs, voltammetry of neurochemicals, possibly gene expression, and quantify it. Sure it is not a simple task, and people may disagree on how to integrate measurements for a solid theory of what is happening, but conceptually it is at least clearly defined.


Charles Wilson (2008), Scholarpedia, 3(6):1410.               doi:10.4249/scholarpedia.1410

An important difference is the appearance of up-and down states when unconscious. Possibly in this state only the purely mechanical coupling of the neuronal mass remains, and the fine-tuned interactions by chemical receptors and channels is simplified such that the high entropy asynchronous spiking is abolished.

It would be interesting to further investigate the soliton theory for this question.