scholarly journals Redundancy in synaptic connections enables neurons to learn optimally

2018 ◽  
Vol 115 (29) ◽  
pp. E6871-E6879 ◽  
Author(s):  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Synaptic Plasticity ◽  
Particle Filtering ◽  
Neuron Model ◽  
Experimental Studies ◽  
Dendritic Tree ◽  
Spike Timing ◽  
Mammalian Brain ◽  
Synaptic Connections ◽  
Synaptic Organization ◽  
Functional Benefit

Recent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Extending the proposed framework to a detailed single-neuron model of perceptual learning in the primary visual cortex, we show that the model accounts for many experimental observations. In particular, the proposed model reproduces the dendritic position dependence of spike-timing-dependent plasticity and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a conceptual framework for synaptic plasticity and rewiring.

scholarly journals Redundancy in synaptic connections enables neurons to learn optimally

2017 ◽  
Author(s):  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Synaptic Plasticity ◽  
Particle Filtering ◽  
Neuron Model ◽  
Experimental Studies ◽  
Dendritic Tree ◽  
Spike Timing ◽  
Mammalian Brain ◽  
Synaptic Connections ◽  
Synaptic Organization ◽  
Functional Benefit

AbstractRecent experimental studies suggest that, in cortical microcircuits of the mammalian brain, the majority of neuron-to-neuron connections are realized by multiple synapses. However, it is not known whether such redundant synaptic connections provide any functional benefit. Here, we show that redundant synaptic connections enable near-optimal learning in cooperation with synaptic rewiring. By constructing a simple dendritic neuron model, we demonstrate that with multisynaptic connections, synaptic plasticity approximates a sample-based Bayesian filtering algorithm known as particle filtering, and wiring plasticity implements its resampling process. Applying the proposed framework to a detailed single neuron model, we show that the model accounts for many experimental observations, including the dendritic position dependence of spike-timing-dependent plasticity, and the functional synaptic organization on the dendritic tree based on the stimulus selectivity of presynaptic neurons. Our study provides a novel conceptual framework for synaptic plasticity and rewiring.

Experimental Study of Reinforcement Learning in Mobile Robots Through Spiking Architecture of Thalamo-Cortico-Thalamic Circuitry of Mammalian Brain

Robotica ◽  
2019 ◽  
Vol 38 (9) ◽  
pp. 1558-1575
Author(s):  
Vahid Azimirad ◽  
Mohammad Fattahi Sani
Keyword(s):  
Reinforcement Learning ◽  
Motor Neurons ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Spike Timing ◽  
Mammalian Brain ◽  
Behavioral Learning ◽  
Experimental Implementation ◽  
Robotic Tasks ◽  
Izhikevich Model

SUMMARYIn this paper, the behavioral learning of robots through spiking neural networks is studied in which the architecture of the network is based on the thalamo-cortico-thalamic circuitry of the mammalian brain. According to a variety of neurons, the Izhikevich model of single neuron is used for the representation of neuronal behaviors. One thousand and ninety spiking neurons are considered in the network. The spiking model of the proposed architecture is derived and prepared for the learning problem of robots. The reinforcement learning algorithm is based on spike-timing-dependent plasticity and dopamine release as a reward. It results in strengthening the synaptic weights of the neurons that are involved in the robot’s proper performance. Sensory and motor neurons are placed in the thalamus and cortical module, respectively. The inputs of thalamo-cortico-thalamic circuitry are the signals related to distance of the target from robot, and the outputs are the velocities of actuators. The target attraction task is used as an example to validate the proposed method in which dopamine is released when the robot catches the target. Some simulation studies, as well as experimental implementation, are done on a mobile robot named Tabrizbot. Experimental studies illustrate that after successful learning, the meantime of catching target is decreased by about 36%. These prove that through the proposed method, thalamo-cortical structure could be trained successfully to learn to perform various robotic tasks.

scholarly journals A unified computational model for cortical post-synaptic plasticity

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tuomo Mäki-Marttunen ◽  
Nicolangelo Iannella ◽  
Andrew G Edwards ◽  
Gaute T Einevoll ◽  
Kim T Blackwell
Keyword(s):  
Synaptic Plasticity ◽  
Ampa Receptor ◽  
Cortical Plasticity ◽  
Experimental Studies ◽  
Spike Timing ◽  
Signalling Pathways ◽  
Synaptic Potentiation ◽  
Detailed Model ◽  
Biochemical Pathways ◽  
Cortical Areas

Signalling pathways leading to post-synaptic plasticity have been examined in many types of experimental studies, but a unified picture on how multiple biochemical pathways collectively shape neocortical plasticity is missing. We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity.

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.

scholarly journals Hippocampal Spike-Timing Correlations Lead to Hexagonal Grid Fields

2017 ◽  
Author(s):  
Mauro M Monsalve-Mercado ◽  
Christian Leibold
Keyword(s):  
Synaptic Plasticity ◽  
Pattern Formation ◽  
Entorhinal Cortex ◽  
Turing Instability ◽  
Place Cells ◽  
Spike Timing ◽  
Mammalian Brain ◽  
Inhibition Mechanism ◽  
Temporal Code ◽  
Medial Entorhinal Cortex

Space is represented in the mammalian brain by the activity of hippocampal place cells as well as in their spike-timing correlations. Here we propose a theory how this temporal code is transformed to spatial firing rate patterns via spike-timing-dependent synaptic plasticity. The resulting dynamics of synaptic weights resembles well-known pattern formation models in which a lateral inhibition mechanism gives rise to a Turing instability. We identify parameter regimes in which hexagonal firing patterns develop as they have been found in medial entorhinal cortex.

scholarly journals Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

2021 ◽  
Author(s):  
Beatriz Eymi Pimentel Mizusaki ◽  
Sally Si Ying Li ◽  
Rui Ponte Costa ◽  
Jesper Sjöström
Keyword(s):  
Synaptic Plasticity ◽  
Activity Patterns ◽  
Experimental Studies ◽  
Spike Timing ◽  
Response Amplitude ◽  
Synaptic Response ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Response Range

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.

scholarly journals Detailed dendritic excitatory/inhibitory balance through heterosynaptic spike-timing-dependent plasticity

2016 ◽  
Author(s):  
Naoki Hiratani ◽  
Tomoki Fukai
Keyword(s):  
Computational Models ◽  
Neural Circuit ◽  
Detailed Balance ◽  
Experimental Studies ◽  
Spike Timing ◽  
Experimental Results ◽  
Dendritic Branch ◽  
Synaptic Organization ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

AbstractBalance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. Here, by considering computational models of heterosynaptic spike-timing-dependent plasticity (STDP), we show that the detailed excitatory/inhibitory balance on dendritic branch is robustly achieved through heterosynaptic interaction between excitatory and inhibitory synapses. The model well reproduces experimental results on heterosynaptic STDP, and provides analytical insights. Furthermore, heterosynaptic STDP explains how maturation of inhibitory neurons modulates selectivity of excitatory neurons in critical period plasticity of binocular matching. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and resultant dendritic computation.Significance statementRecent experimental studies have revealed that relative spike timings among neighboring Glutamatergic and GABAergic synapses on a dendritic branch significantly influences changes in synaptic efficiency of these synapses. This heterosynaptic form of spike-timing-dependent plasticity (STDP) is potentially important for shaping the synaptic organization and computation of neurons, but its functional role remains elusive. Here, through computational modeling, we show that heterosynaptic plasticity causes the detailed balance between excitatory and inhibitory inputs on the dendrite, at the parameter regime where previous experimental results are well reproduced. Our result reveals a potential principle of GABA-driven neural circuit formation.

scholarly journals Pre- and postsynaptically expressed spiking-timing-dependent plasticity contribute differentially to neuronal learning

2018 ◽  
Author(s):  
Beatriz E. P. Mizusaki ◽  
Sally S. Y. Li ◽  
Rui Ponte Costa ◽  
P. Jesper Sjöström
Keyword(s):  
Synaptic Plasticity ◽  
Experimental Studies ◽  
Spike Timing ◽  
Response Amplitude ◽  
Synaptic Response ◽  
Short Term ◽  
Dependent Plasticity ◽  
Presynaptic Plasticity ◽  
Functional Consequences

AbstractA plethora of experimental studies have shown that long-term plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are unknown. However, in models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. Whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In other words, the consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we modelled spike-timing-dependent plasticity (STDP) such that it was expressed either pre- or postsynaptically, or both. We tested pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcome in a feed-forward setting, with two different learning schemes: either inputs were triggered at different latencies, or a subset of inputs were temporally correlated. Across different STDP models and learning paradigms, we found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was better at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity maintained control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by direct weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.Author summaryDifferences between functional properties of pre- or postsynaptically expressed long-term plasticity have not yet been explored in much detail. In this paper, we used minimalist models of STDP with different expression loci, in search of fundamental functional consequences. Presynaptic expression acts mostly on neurotransmitter release, thereby altering short-term synaptic dynamics, whereas postsynaptic expression affects mainly synaptic gain. We compared cases where plasticity was expressed presynaptically, postsynaptically, or both. We found that postsynaptic plasticity was more effective at changing response times, while both pre- and postsynaptic plasticity were similarly capable of detecting correlated inputs. A model with biologically tuned expression of plasticity also achieved this separation over a range of frequencies without the need of external competitive mechanisms. Postsynaptic spiking frequency was not directly affected by presynaptic plasticity of short-term plasticity alone, however in combination with a postsynaptic component, it helped restrain positive feedback, contributing to activity homeostasis. In conclusion, expression locus may determine distinct coding schemes while also keeping activity within bounds. Our findings highlight the importance of correctly implementing expression of plasticity in modelling, since the locus of expression may affect functional outcomes in simulations.

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