Types, numbers and distribution of synapses on the dendritic tree of an identified visual interneuron in the brain of the locust

1999 ◽  
Vol 296 (3) ◽  
pp. 645-665 ◽  
Author(s):  
F. Killmann ◽  
H. Gras ◽  
F.-W. Sch�rmann
Keyword(s):  
Dendritic Tree ◽  
Visual Interneuron ◽  
The Brain

Associative Memory in a Multimodular Network

1999 ◽  
Vol 11 (7) ◽  
pp. 1717-1737 ◽  
Author(s):  
Nir Levy ◽  
David Horn ◽  
Eytan Ruppin
Keyword(s):  
Associative Memory ◽  
Memory Retrieval ◽  
Dendritic Tree ◽  
Distributed Network ◽  
Synaptic Connections ◽  
Object Knowledge ◽  
Dendritic Processing ◽  
Memory Network ◽  
Apical Dendrites ◽  
The Brain

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 memory network, whose functional goal is to store patterns with different coding levels—patterns that vary in the number of modules in which they are encoded. We show that in order to accomplish this task, synaptic inputs should be segregated into intramodular projections and intermodular projections, with the latter undergoing additional nonlinear dendritic processing. This segregation makes sense anatomically if the intermodular projections represent distal synaptic connections on apical dendrites. It is then straightforward to show that memories encoded in more modules are more resilient to focal afferent damage. Further hierarchical segregation of intermodular connections on the dendritic tree improves this resilience, allowing memory retrieval from input to just one of the modules in which it is encoded.

scholarly journals Synaptic plasticity is predicted by spatiotemporal firing rate patterns and robust to in vivo-like variability

2021 ◽  
Author(s):  
Dan B Dorman ◽  
Kim T Blackwell
Keyword(s):  
Synaptic Plasticity ◽  
Firing Rate ◽  
Dendritic Tree ◽  
Spike Timing ◽  
Synaptic Activity ◽  
Data Driven ◽  
Synaptic Inputs ◽  
The Brain

Synaptic plasticity, the experience-induced change in connections between neurons, underlies learning and memory in the brain. Most of our understanding of synaptic plasticity derives from in vitro experiments with precisely repeated stimulus patterns; however, neurons exhibit significant variability in vivo during repeated experiences. Further, the spatial pattern of synaptic inputs to the dendritic tree influences synaptic plasticity, yet is not considered in most synaptic plasticity rules. Here, we address the sensitivity of plasticity to trial-to-trial variability and delineate how spatiotemporal synaptic input patterns produce plasticity with in vivo-like conditions using a data-driven computational model with a calcium-based plasticity rule. Using in vivo spike train recordings as inputs, we show that plasticity is strongly robust to trial-to-trial variability of spike timing, and derive general synaptic plasticity rules describing how spatiotemporal patterns of synaptic inputs control the magnitude and direction of plasticity. Specifically, a high temporal input firing rate to a synapse late in a trial correlated with neighboring synaptic activity produces potentiation, while an earlier, moderate firing rate that is negatively correlated with neighboring synaptic activity produces depression. Together, our results reveal that calcium dynamics can unify diverse plasticity rules and reveal how spatiotemporal firing rate patterns control synaptic plasticity.

MODELING NEURONAL RESPONSE TO SIMULTANEOUS AND SEQUENTIAL MULTI-SITE SYNAPTIC STIMULATION

2012 ◽  
Vol 17 ◽  
pp. 1-8
Author(s):  
DAVID JOHNSTON ◽  
SIMON PETER MEKHAIL ◽  
MARY ANN GO ◽  
VINCENT R. DARIA
Keyword(s):  
Membrane Potential ◽  
Action Potential ◽  
Neuronal Response ◽  
Dendritic Tree ◽  
Synaptic Inputs ◽  
Dendritic Branches ◽  
Flow Of Information ◽  
The Brain ◽  
Simulation Package ◽  
Neuron Simulation

The flow of information in the brain theorizes that each neuron in a network receives synaptic inputs and sends off its processed signals to neighboring neurons. Here, we model these synaptic inputs to understand how each neuron processes these inputs and transmits neurotransmitters to neighboring neurons. We use the NEURON simulation package to stimulate a neuron at multiple synaptic locations along its dendritic tree. Accumulation of multiple synaptic inputs causes changes in the neuron's membrane potential leading to firing of an action potential. Our simulations show that simultaneous synaptic stimulation approaches firing of an action potential at lesser inputs compared to sequential stimulation at multiple sites distributed along several dendritic branches.

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