http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of "sleep and memory" research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.

https://www.zora.uzh.ch/id/eprint/77770/1/physiol_rev_93.pdf

About sleep's role in memory

Journal of Statistical Mechanics: theory and experiment

How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further.

/pdf/highly-nonrandom-features-of-synaptic-connectivity-in-local-26pvb3bjhl.pdf

Highly nonrandom features of synaptic connectivity in local cortical circuits.

Neurodegenerative diseases of the central nervous system are often associated with impaired learning and memory, eventually leading to dementia An important aspect in pre-clinical research is the exploration of strategies to re-establish learning ability and access to long-term memories By using a mouse model that allows temporally and spatially restricted induction of neuronal loss, we show here that environmental enrichment reinstated learning behaviour and re-established access to long-term memories after significant brain atrophy and neuronal loss had already occurred Environmental enrichment correlated with chromatin modifications (increased histone-tail acetylation) Moreover, increased histone acetylation by inhibitors of histone deacetylases induced sprouting of dendrites, an increased number of synapses, and reinstated learning behaviour and access to long-term memories These data suggest that inhibition of histone deacetylases might be a suitable therapeutic avenue for neurodegenerative diseases associated with learning and memory impairment, and raises the possibility of recovery of long-term memories in patients with dementia

Recovery of learning and memory is associated with chromatin remodelling

In the framework of an associative memory model, we study the interplay between synaptic deletion and compensation, and memory deterioration, a clinical hallmark of Alzheimer's disease. Our study is motivated by experimental evidence that there are regulatory mechanisms that take part in the homeostasis of neuronal activity and act on the neuronal level. We show that following synaptic deletion, synaptic compensation can be carried out efficiently by a local, dynamic mechanism, where each neuron maintains the profile of its incoming post-synaptic current. Our results open up the possibility that the primary factor in the pathogenesis of cognitive deficiencies in Alzheimer's disease (AD) is the failure of local neuronal regulatory mechanisms. Allowing for neuronal death, we observe two pathological routes in AD, leading to different correlations between the levels of structural damage and functional decline.

/pdf/neuronal-based-synaptic-compensation-a-computational-study-3f4w1o9cp5.pdf

Neuronal-based synaptic compensation: A computational study in alzheimer's disease

Since their conception half a century ago, Hebbian cell assemblies have become a basic term in the neurosciences, and the idea that learning takes place through synaptic modifications has been accepted as a fundamental paradigm. As synapses undergo continuous metabolic turnover, adopting the stance that memories are engraved in the synaptic matrix raises a fundamental problem: How can memories be maintained for very long time periods? We present a novel solution to this long-standing question, based on biological evidence of neuronal regulation mechanisms that act to maintain neuronal activity. Our mechanism is developed within the framework of a neural model of associative memory. It is operative in conjunction with random activation of the memory system and is able to counterbalance degradation of synaptic weights and normalize the basins of attraction of all memories. Over long time periods, when the variance of the degradation process becomes important, the memory system stabilizes if its synapses are appropriately bounded. Thus, the remnant memory system is obtained by a dynamic process of synaptic selection and growth driven by neuronal regulatory mechanisms. Our model is a specific realization of dynamic stabilization of neural circuitry, which is often assumed to take place during sleep.

/pdf/memory-mainetenance-via-neuronal-regulation-2r2ck5aqwg.pdf

Memory mainetenance via neuronal regulation

We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal synaptic learning curve. This learning function is based on recent experimental findings. It includes potentiation for short time delays between pre - and postsynaptic neuronal spiking, and depression for spiking events occuring in the reverse order. The coupling between the dynamics of the synaptic learning and of the neuronal activation leads to interesting results. We find that the cell assembly can fire asynchronously, but may also function in complete synchrony, or in distributed synchrony. The latter implies spontaneous division of the Hebbian cell assembly into groups of cells that fire in a cyclic manner. We invetigate the behavior of distributed synchrony both by simulations and by analytic calculations of the resulting synaptic distributions.

/pdf/distributed-synchrony-of-spiking-neurons-in-a-hebbian-cell-3bfbgx5skk.pdf

Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly

Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multimodular associative mem...

Associative memory in a multimodular network

Hebbian learning, the paradigm of memory formation, needs further mechanisms to guarantee creation and maintenance of a viable memory system. One such proposed mechanism is Hebbian unlearning, a process hypothesized to occur during sleep. It can remove spurious states and eliminate global correlations in the memory system. However, the problem of spurious states is unimportant in the biologically interesting case of memories that are sparsely coded on excitatory neurons. Moreover, if some memories are anomalously strong and have to be weakened to guarantee proper functioning of the network, we show that it is advantageous to do that by neuronal regulation (NR) rather than synaptic unlearning. Neuronal regulation can account for dynamical maintenance of memory systems that undergo continuous synaptic turnover. This neuronal-based mechanism, regulating all excitatory synapses according to neuronal average activity, has recently gained strong experimental support. NR achieves synaptic maintenance over short time scales by preserving the average neuronal input field. On longer time scales it acts to maintain memories by letting the stronger synapses grow to their upper bounds. In ageing, these bounds are increased to allow stronger values of remaining synapses to overcome the loss of synapses that have perished.

Neuronal regulation versus synaptic unlearning in memory maintenance mechanisms.

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