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1.
Neural Comput ; 36(5): 781-802, 2024 Apr 23.
Artigo em Inglês | MEDLINE | ID: mdl-38658027

RESUMO

Variation in the strength of synapses can be quantified by measuring the anatomical properties of synapses. Quantifying precision of synaptic plasticity is fundamental to understanding information storage and retrieval in neural circuits. Synapses from the same axon onto the same dendrite have a common history of coactivation, making them ideal candidates for determining the precision of synaptic plasticity based on the similarity of their physical dimensions. Here, the precision and amount of information stored in synapse dimensions were quantified with Shannon information theory, expanding prior analysis that used signal detection theory (Bartol et al., 2015). The two methods were compared using dendritic spine head volumes in the middle of the stratum radiatum of hippocampal area CA1 as well-defined measures of synaptic strength. Information theory delineated the number of distinguishable synaptic strengths based on nonoverlapping bins of dendritic spine head volumes. Shannon entropy was applied to measure synaptic information storage capacity (SISC) and resulted in a lower bound of 4.1 bits and upper bound of 4.59 bits of information based on 24 distinguishable sizes. We further compared the distribution of distinguishable sizes and a uniform distribution using Kullback-Leibler divergence and discovered that there was a nearly uniform distribution of spine head volumes across the sizes, suggesting optimal use of the distinguishable values. Thus, SISC provides a new analytical measure that can be generalized to probe synaptic strengths and capacity for plasticity in different brain regions of different species and among animals raised in different conditions or during learning. How brain diseases and disorders affect the precision of synaptic plasticity can also be probed.


Assuntos
Teoria da Informação , Plasticidade Neuronal , Sinapses , Animais , Sinapses/fisiologia , Plasticidade Neuronal/fisiologia , Espinhas Dendríticas/fisiologia , Região CA1 Hipocampal/fisiologia , Modelos Neurológicos , Armazenamento e Recuperação da Informação , Masculino , Hipocampo/fisiologia , Ratos
2.
bioRxiv ; 2024 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-38260636

RESUMO

Long-term potentiation (LTP) has become a standard model for investigating synaptic mechanisms of learning and memory. Increasingly, it is of interest to understand how LTP affects the synaptic information storage capacity of the targeted population of synapses. Here, structural synaptic plasticity during LTP was explored using three-dimensional reconstruction from serial section electron microscopy. Storage capacity was assessed by applying a new analytical approach, Shannon information theory, to delineate the number of functionally distinguishable synaptic strengths. LTP was induced by delta-burst stimulation of perforant pathway inputs to the middle molecular layer of hippocampal dentate granule cells in adult rats. Spine head volumes were measured as predictors of synaptic strength and compared between LTP and control hemispheres at 30 min and 2 hr after the induction of LTP. Synapses from the same axon onto the same dendrite were used to determine the precision of synaptic plasticity based on the similarity of their physical dimensions. Shannon entropy was measured by exploiting the frequency of spine heads in functionally distinguishable sizes to assess the degree to which LTP altered the number of bits of information storage. Outcomes from these analyses reveal that LTP expanded storage capacity; the distribution of spine head volumes was increased from 2 bits in controls to 3 bits at 30 min and 2.7 bits at 2 hr after the induction of LTP. Furthermore, the distribution of spine head volumes was more uniform across the increased number of functionally distinguishable sizes following LTP, thus achieving more efficient use of coding space across the population of synapses.

3.
Artigo em Inglês | MEDLINE | ID: mdl-29881246

RESUMO

Previous modeling studies have demonstrated that lateral inhibition contributes to enhanced precision in sensory networks. That is, inhibitory connections reduce the spread of activity and repress neighboring cells, increasing the reliability of a sensory response. However, much less is understood about how connections that spread activity might contribute to the processing of sensory stimuli in the context of a sensory discrimination task. In this work, we examine the role of excitatory connections and gap junctions in network dynamics and some contributions to sensory discrimination.

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