Hippocampal Spine Head Sizes Are Highly Precise

Bartol, Thomas M.; Bromer, Cailey; Kinney, Justin P.; Chirillo, Michael A.; Bourne, Jennifer N.; Harris, Kristen M.; Sejnowski, Terrence J.

doi:10.1101/016329

Cited by 19 publications

(18 citation statements)

References 38 publications

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“…In this case, it would not only matter how many synapses are realised but also which ones. Interestingly, recent experiments suggest that the spine volumes, which are related to synapse stability [39,40], are very similar for synapses between the same axon and dendrite [41], supporting the homogeneity assumption of our model. However, in general, we expect that qualitatively the here shown prolongation of storage time by the collective dynamics of multiple synapses, is unaffected by such synapse differences, as long as they also implement the experimentally measured bimodal distributions.…”

Section: Discussionsupporting

confidence: 84%

Formation and maintenance of robust long-term information storage in the presence of synaptic turnover

Fauth

Wörgötter

Tetzlaff

2015

Preprint

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A long-standing problem is how memories can be stored for very long times despite the volatility of the underlying neural substrate, most notably the high turnover of dendritic spines and synapses. To address this problem, here we are using a generic and simple probabilistic model for the creation and removal of synapses. We show that information can be stored for several months when utilizing the intrinsic dynamics of multi-synapse connections. In such systems, single synapses can still show high turnover, which enables fast learning of new information, but this will not perturb prior stored information (slow forgetting), which is represented by the compound state of the connections. The model matches the time course of recent experimental spine data during learning and memory in mice supporting the assumption of multi-synapse connections as the basis for long-term storage. Author SummaryIt is widely believed that information is stored in the connectivity, i.e. the synapses of neural networks. Yet, the morphological correlates of excitatory synapses, the dendritic spines, have been found to undergo a remarkable turnover on daily basis. This poses the question, how information can be retained on such a variable substrate.In this study, using connections with multiple synapses, we show that connections which follow the experimentally measured bimodal distribution in the number of synapses can store information orders of magnitude longer than the lifetime of a single synapse. This is a consequence of the underlying bistable collective dynamic of multiple synapses: Single synapses can appear and disappear without disturbing the memory as a whole.Furthermore, increasing or decreasing neural activity changes the distribution of the number of synapses of multi-synaptic connections such that only one of the peaks remains. This leads to a desirable property: information about these altered activities can be stored much faster than it is forgotten. Remarkably, the resulting model dynamics match recent experimental data investigating the long-term effect of learning on the dynamics of dendritic spines.

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Section: Discussionsupporting

confidence: 84%

Formation and maintenance of robust long-term information storage in the presence of synaptic turnover

Fauth

Wörgötter

Tetzlaff

2015

Preprint

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“… capacity: the maximum number of AMPARs that can be inserted, roughly equivalent to PSD size or number of receptor "slots" in the PSD. This combination of binary and continuously variable attributes makes it possible to model synapses where potentiation behaves like a bistable switch (Bortolotto, Bashir, Davies, & Collingridge, 1994;Jalil et al, 2015;Westmark et al, 2010), yet synaptic strength is variable with many distinct levels (Bartol et al, 2015).…”

Section: Simulation Targetsmentioning

confidence: 99%

A computational model of systems memory consolidation and reconsolidation

Helfer

Shultz

2019

Hippocampus

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In the mammalian brain, newly acquired memories depend on the hippocampus for maintenance and recall, but over time these functions are taken over by the neocortex through a process called systems memory consolidation. However, reactivation of a well-consolidated memory can return it to a hippocampus-dependent state. This is normally followed by a restoration of hippocampusindependence, a phenomenon known as systems memory reconsolidation. The neural mechanisms underlying systems memory consolidation and reconsolidation are poorly understood. Here, we propose a neural model based on well-documented mechanisms of synaptic plasticity and stability and describe a computational implementation that demonstrates the model's ability to account for a number of findings from the systems consolidation and reconsolidation literature.

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“…According to a recent study, an optimistic estimate is that a single synapse can store 4.6 bits of information [9] . Furthermore, if there are about 1 000 synapses for each neuron and there are about 100 billion neurons in the brain, we have in total 10 11 × 1 000 = 10 14 or 100 trillion synapses.…”

Section: Variety Generated By Human Brainmentioning

confidence: 99%

“…We can now compute two types of variety, one for a brain that has already matured and cannot make substantial changes easily (i.e., it cannot suddenly replace its memories with memories of another person), and the other for all possible sets of lifelong experiences that a mature brain could potentially encounter (all different lives that a person could theoretically live). Given that 4.6 bits of information can be stored per synapse [9] , this would set the upper bound of the total theoretical variety that a mature adult human brain can generate without further learning to 4.6 × 10 14 bits. …”

Section: Variety Generated By Human Brainmentioning

confidence: 99%

Why deep neural nets cannot ever match biological intelligence and what to do about it?

Nikolić

2017

Int. J. Autom. Comput.

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Abstract:The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory, biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent. This means that the current approaches to artificial intelligence, such as deep architectures of neural networks, will not suffice with fixed network architectures. Rather, they will need to be equipped with intelligent mechanisms that rapidly alter the architectures of those networks.

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Hippocampal Spine Head Sizes Are Highly Precise

Cited by 19 publications

References 38 publications

Formation and maintenance of robust long-term information storage in the presence of synaptic turnover

Formation and maintenance of robust long-term information storage in the presence of synaptic turnover

A computational model of systems memory consolidation and reconsolidation

Why deep neural nets cannot ever match biological intelligence and what to do about it?

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