Tag Archives: memory

General, learning, Neural Networks, Synaptic Plasticity , , ,

Language in the brain

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We need quite different functionality than statistics to model language. We may need this functionality in other areas of intelligence, but with language it is obvious. Or, for the DL community – we can model anything with statistics, of course. It will just not be a very good model …

What follows is that the synaptic plasticity that produces statistical learning does not allow us to build a human language model. Weight adjustment of connections in a graph is simply not sufficient – under any possible model of language to capture language competency.

This is where it becomes interesting. We have just stated that the synaptic plasticity hypothesis of memory is wrong, or else our mammalian brains would be incapable of producing novel sentences and novel text, something we have not memorized before.

Cellular intelligence, General, learning, network science, Neural Networks , , , , , , , , , , ,

The next paradigm: AI, Neural Networks, the Symbolic Brain

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I don’t think neural networks are still in their infancy, I think they are moribund. Some say, they hit a roadblock. AI used to be based on rules, symbols and logic. Then probability and statistics came along, and neural networks were created as an efficient statistical paradigm. But unfortunately, the old AI (GOFAI) was discontinued, it was replaced by statistics. Since then ever more powerful computers and ever more data came along, therefore statistics, neural networks (NN), machine learning seemed to create value, even though they were based on simple concepts from the late eighties, early nineties or even earlier. We failed to develop, success was easy. Some argue for hybrid models, combining GOFAI and NN. But that was tried early on, and it wasn’t very successful. What we now need is a new, and deeper understanding of what the brain actually does. Because it obviously does symbol manipulation, it does logic, it does math. Most importantly, we humans learned to speak, using nothing better than a mammalian brain (with a few specializations). I believe there is a new paradigm out there which can fulfill these needs: language, robotics, cognition, knowledge creation. I call it the vertical-horizontal model: a model of neuron interaction, where the neuron is a complete microprocessor of a very special kind. This allows to build a symbolic brain. There will be a trilogy of papers to describe this new paradigm, and a small company to build the necessary concepts. At the present time, here is a link to an early draft, hard to read, not a paper, more a collection right now, but ready for feedback at my email! I’ll soon post a summary here as well.

internal memory, learning, Neural Networks, Neuromodulation, Signal Transduction, Synaptic Plasticity , , , , , , ,

Learning in the Brain: Difference learning vs. Associative learning

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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.

General, learning, Neural Networks , , , , , ,

Why a large cortex?

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mouse

If we compare a small mouse cortex with a large human cortex, the connectivity per neuron is approximately the same (10^4/neuron SchuezPalm1989). So why did humans add so many neurons, and why did the connectivity remain constant? For the latter question we may conjecture that a maximal size is already reached in the mouse. Our superior human cognitive skills thus rest on the increased number of neurons in cortex, which means the number of modules (cortical microcolumns) went up, not the synaptic connectivity as such.

learning, network science, Neural Networks, Synaptic Plasticity , , , , , ,

Heavy-tailed distributions and hierarchical cell assemblies

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In earlier work, we meticulously documented the distribution of synaptic weights and the gain (or activation function) in many different brain areas. We found a remarkable consistency of heavy-tailed, specifically lognormal, distributions for firing rates, synaptic weights and gains (Scheler2017).

Why are biological neural networks heavy-tailed (lognormal)?

Cell assemblies: Lognormal networks support models of a hierarchically organized cell assembly (ensembles). Individual neurons can activate or suppress a whole cell assembly if they are the strongest neuron or directly connect to the strongest neurons (TeramaeJetal2012).
Storage: Sparse strong synapses store stable information and provide a backbone of information processing. More frequent weak synapses are more flexible and add changeable detail to the backbone. Heavy-tailed distributions allow a hierarchy of stability and importance.
Time delay of activation is reduced because strong synapses activate quickly a whole assembly (IyerRetal2013). This reduces the initial response time, which is dependent on the synaptic and intrinsic distribution. Heavy-tailed distributions activate fastest.
Noise response: Under additional input, noise or patterned, the pattern stability of the existing ensemble is higher (IyerRetal2013, see also KirstCetal2016). This is a side effect of integration of all computations within a cell assembly.

Why hierarchical computations in a neural network?

Calculations which depend on interactions between many discrete points (N-body problems, Barnes and Hut 1986), such as particle-particle methods, where every point depends on all others, lead to an O(N^2) calculation. If we supplant this by hierarchical methods, and combine information from multiple points, we can reduce the computational complexity to O(N log N) or O(N).

Since biological neural networks are not feedforward but connect in both forward and backward directions, they have a different structure from ANNs (artificial neural networks) – they consist of hierarchically organised ensembles with few wide-range excitability ‘hub’ neurons and many ‘leaf’ neurons with low connectivity and small-range excitability. Patterns are stored in these ensembles, and get accessed by a fit to an incoming pattern that could be expressed by low mutual information as a measure of similarity. Patterns are modified by similar access patterns, but typically only in their weak connections (else the accessing pattern would not fit).

Cellular intelligence, epigenetics, internal memory, learning, Synaptic Plasticity , , , , , ,

Epigenetics and memory

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Epigenetic modification is a powerful mechanism for the induction, the expression and persistence of long-term memory.

For long-term memory, we need to consider diverse cellular processes. These occur in neurons from different brain regions (in particular hippocampus, cortex, amygdala) during memory consolidation and recall. For instance, long-term changes in kinase expression in the proteome, changes in receptor subunit composition and localization at synaptic/dendritic membranes, epigenetic modifications of chromatin such as DNA methylation and histone methylation in the nucleus, and the posttranslational modifications of histones, including phosphorylation and acetylation, all these play a role. Histone acetylation is of particular interest because a number of known medications exist, which function as histone deacetylase inhibitors (HDACs), i.e. have a potential to increase DNA transcription and memory (more on this in a later post).

Epigenetic changes are important because they define the internal conditions for plasticity for the individual neuron. They underlie for instance, kinase or phosphatase-mediated (de)activations of enzymatic proteins and therefore influence the probability of membrane proteins to become altered by synaptic activation.

Among epigenetic changes, DNA methylation typically acts to alter, often to repress, DNA transcription at cytosine, or CpG islands in vertebrates. DNA methylation is mediated by enzymes such as Tet3, which catalyses an important step in the demethylation of DNA. In dentate gyrus of live rats, it was shown that the expression of Tet3 is greatly increased by LTP – synaptically induced memorization – , suggesting that certain DNA stretches were demethylated [5], and presumably activated. During induction of LTP by high frequency electrical stimulation, DNA methylation is changed specifically for certain genes known for their role in neural plasticity [1]. The expression of neural plasticity genes is widely correlated with the methylation status of the corresponding DNA .

So there is interesting evidence for filtering the induction of plasticity via the epigenetic landscape and modifiers of gene expression, such as HDACs. Substances which act as histone deacetylase inhibitors (HDACs) increase histone acetylation. An interesting result from research on fear suggests that HDACs increase some DNA transcription, and enhance specifically fear extinction memories [2], [3],[4]. 

Cellular intelligence, internal memory, ion channels, Neuromodulation , , , ,

Ion channel expression is not regulated by spiking behavior

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An important topic to understand intrinsic excitability is the distribution and activation of ion channels. In this respect the co-regulations between ion channels are of significant interest. MacLean et al. (2003) could show that overexpression of an A-type potassium channel by shal-RNA-injection in neurons of the stomatogastric ganglion of the lobster is compensated by upregulation of Ih such that the spiking behavior remained unaltered.

A non functional shal-mutant whose overexpression did not affect spiking had the same effect, which shows that the regulation does not happen at the site of the membrane, by measuring the spiking behavior. In this case, Ih was upregulated, even though IA activity was unaltered, and spiking behavior was increased. (This is in contrast to e.g. O’Leary et al., 2013, who assume homeostatic regulation of ion channel expression at the membrane, by spiking behavior.)

In drosophila-motoneurons the expression of shal and shaker – both responsible for IA – is reciprocally coupled. If one is reduced, the other is upregulated to a constant level of IA activity at the membrane. Other ion channels, like (INAp and IM) are again antagonistic, which means they correlate positively: if one is reduced, the other is reduced as well to achieve the same level of effect (Golowasch2014). There are a number of publications which have all documented similar effects, e.g. (MacLean et al., 2005, Schulz et al., 2007; Tobin et al., 2009; O’Leary et al., 2013).

We must assume that the expression level of ion channels is regulated and sensed inside the cell and that the levels of genes for different ion channels are coupled – by genetic regulation or on the level of RNA regulation.

To summarize: When there is high IA expression, Ih is also upregulated. When one gene responsible for IA is suppressed, the other gene is more highly expressed, to achieve the same level of IA expression. When (INap), a permanent sodium channel, is reduced, (IM), a potassium channel, is also reduced.

It is important to note that these ion channels may compensate for each other in terms of overall spiking behavior, but they have subtly different properties of activation, e.g. by the pattern of spiking or by neuromodulation. For instance, if cell A reduces ion channel currents like INap and IM, compensating to achieve the same spiking behavior, once we apply neuromodulation to muscarinic receptors on A, this will affect IM, but not INap. The behavior of cell A, crudely the same, is now altered under certain conditions.

To model this – other than by a full internal cell model – requires internal state variables which guide ion channel expression, and therefore regulate intrinsic excitability. These variables would model ion channel proteins and their respective interaction, and in this way guarantee acceptable spiking behavior of the cell. This could lead to the idea of an internal module which sets the parameters necessary for the neuron to function. Such an internal module that self-organizes its state variables according to specified objective functions could greatly simplify systems design. Instead of tuning systems parameters by outside methods – which is necessary for ion-channel based models – each neuronal unit itself would be responsible for its ion channels and be able to self-tune them separately from the whole system.

Linked to the idea of internal state variables is the idea of internal memory, which I have referred to several times in this blog. If I have an internal module of co-regulated variables, which set external parameters for each unit, then this module may serve as a latent memory for variables which are not expressed at the membrane at the present time (s. Er81). The time course of expression and activation at the membrane and of internal co-regulation need not be the same. This offers an opportunity for memory inside the cell, separated from information processing within a network of neurons.

internal memory, Synaptic Plasticity , , ,

Transmission is not Adaptation

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Current synaptic plasticity models have one decisive property which may not be biologically adequate, and which has important repercussions on the type of memory and learning algorithms in general that can be implemented: Each processing or transmission event is an adaptive learning event.

In contrast, in biology, there are many pathways that may act as filters from the use of a synapse to the adaptation of its strength. In LTP/LTD, typically 20 minutes are necessary to observe the effects. This requires the activation of intracellular pathways, often co-occurence of a GPCR activation, and even nuclear read-out.

Therefore we have suggested a different model, greatly simplified at first to test its algorithmic properties. We include intrinsic excitability in learning (LTP-IE, LTD-IE). The main innovation is that we separate learning or adaptation from processing or transmission. Transmission events leave traces at synapses and neurons that disappear over time (short-term plasticity), unless they add up over time to unusually high (low) neural activations, something that can be determined by threshold parameters. Only if a neuron engages in a high (low) activation-plasticity event we get long-term plasticity at both neurons and synapses, in a localized way. Such a model is in principle capable of operating in a sandpile fashion. We do not know yet what properties the model may exhibit. Certain hypotheses exist, concerning abstraction and compression of a sequence of related inputs, and the development of an individual knowledge.

internal memory, Neural Networks, Synaptic Plasticity , ,

Memory and the Volatility of Spines

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Memory has a physical presence in the brain, but there are no elements which permanently code for it.

Memory is located – among other places – in dendritic spines. Spines are being increased during learning and they carry stimulus or task-specific information. Ablation of spines destroys this information (Hayashi-Takagi A2015). Astrocytes have filopodia which are also extended and retracted and make contact with neuronal synapses. The presence of memory in the spine fits to a neuron-centric view: Spine protrusion and retraction are guided by cellular programs. A strict causality such that x synaptic inputs cause a new spine is not necessarily true, as a matter of fact highly conditional principles of spine formation or dissolution could hold, where the internal state of the neuron and the neuron’s history matters. The rules for spine formation need not be identical to the rules for synapse formation and weight updating (which depend on at least two neurons making contact).

A spine needs to be there for a synapse to exist (in spiny neurons), but once it is there, clearly not all synapses are alike. They differ in the amount of AMPA presence and integration, and other receptors/ion channels as well. For instance, Sk-channels serve to block off a synapse from further change, and may be regarded as a form of overwrite protection. Therefore, the existence or lack of a spine is the first-order adaptation in a spiny neuron, the second-order adaptation involves the synapses themselves.

However, spines are also subject to high variability, on the order of several hours to a few days. Some elements may have very long persistence, months in the mouse, but they are few. MongilloGetal2017 point out the fragility of the synapse and the dendritic spine in pyramidal neurons and ask what this means for the physical basis of memory. Given what we know about neural networks, for memory to be permanent, is it necessary that the same spines remain? Learning allows to operate with many random elements, but memory has prima facie no need for volatility.

It is most likely that memory is a secondary, ’emergent’ property of volatile and highly adaptive structures. From this perspective it is sufficient to keep the information alive, among the information-carrying units, which will recreate it in some form.

The argument is that the information is redundantly coded. So if part of the coding is missing, the rest still carries enough information to inform the system, which recruits new parts to carry the information. The information is never lost, because not all synapses, spines, neurons are degraded at the same time, and because internal reentrant processing keeps the information alive and recreates new redundant parts at the same time as other parts are lost. It is a dynamic cycling of information. There are difficulties, if synapses are supposed to carry the whole information. The main difficulty is: if all patterns at all times are being stored in synaptic values, without undue interference, and with all the complex processes of memory, forgetting, retrieval, reconsolidation etc., can this be fitted to a situation, where the response to a simple visual stimulus already involves 30-40% of the cortical area where there is processing going on? I have no quantitative model for this. I think the model only works if we use all the multiple, redundant forms of plasticity that the neuron possesses: internal states, intrinsic properties, synaptic and morphological properties, axonal growth, presynaptic plasticity.